911 Calls Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Capstone Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this capstone project we will be analyzing some 911 call data from [Kaggle](https://www.kaggle.com/mchirico/montcoalert). The data contains the following fields:\n",
    "\n",
    "* lat : String variable, Latitude\n",
    "* lng: String variable, Longitude\n",
    "* desc: String variable, Description of the Emergency Call\n",
    "* zip: String variable, Zipcode\n",
    "* title: String variable, Title\n",
    "* timeStamp: String variable, YYYY-MM-DD HH:MM:SS\n",
    "* twp: String variable, Township\n",
    "* addr: String variable, Address\n",
    "* e: String variable, Dummy variable (always 1)\n",
    "\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": [
    "____\n",
    "** 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",
    "import matplotlib\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('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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </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",
       "Name: zip, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 townships (twp) for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LOWER MERION    8443\n",
       "ABINGTON        5977\n",
       "NORRISTOWN      5890\n",
       "UPPER MERION    5227\n",
       "CHELTENHAM      4575\n",
       "Name: twp, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Take a look at the 'title' column, how many unique title codes are there? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "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",
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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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99487</th>\n",
       "      <td>40.132869</td>\n",
       "      <td>-75.333515</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST; NORRISTOWN; 2016-08-2...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:06:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99488</th>\n",
       "      <td>40.006974</td>\n",
       "      <td>-75.289080</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL; LOWER MERION; ...</td>\n",
       "      <td>19003.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:07:02</td>\n",
       "      <td>LOWER MERION</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99489</th>\n",
       "      <td>40.115429</td>\n",
       "      <td>-75.334679</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST;  NORRISTOWN; Station ...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: FALL VICTIM</td>\n",
       "      <td>2016-08-24 11:12:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99490</th>\n",
       "      <td>40.186431</td>\n",
       "      <td>-75.192555</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN;  HORSHAM; Station 352; ...</td>\n",
       "      <td>19002.0</td>\n",
       "      <td>EMS: NAUSEA/VOMITING</td>\n",
       "      <td>2016-08-24 11:17:01</td>\n",
       "      <td>HORSHAM</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99491</th>\n",
       "      <td>40.207055</td>\n",
       "      <td>-75.317952</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST; UPPER GWYNEDD; 2016-08...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:17:02</td>\n",
       "      <td>UPPER GWYNEDD</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99492 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             lat        lng  \\\n",
       "0      40.297876 -75.581294   \n",
       "1      40.258061 -75.264680   \n",
       "2      40.121182 -75.351975   \n",
       "3      40.116153 -75.343513   \n",
       "4      40.251492 -75.603350   \n",
       "...          ...        ...   \n",
       "99487  40.132869 -75.333515   \n",
       "99488  40.006974 -75.289080   \n",
       "99489  40.115429 -75.334679   \n",
       "99490  40.186431 -75.192555   \n",
       "99491  40.207055 -75.317952   \n",
       "\n",
       "                                                    desc      zip  \\\n",
       "0      REINDEER CT & DEAD END;  NEW HANOVER; Station ...  19525.0   \n",
       "1      BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...  19446.0   \n",
       "2      HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...  19401.0   \n",
       "3      AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...  19401.0   \n",
       "4      CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...      NaN   \n",
       "...                                                  ...      ...   \n",
       "99487  MARKLEY ST & W LOGAN ST; NORRISTOWN; 2016-08-2...  19401.0   \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL; LOWER MERION; ...  19003.0   \n",
       "99489  CHESTNUT ST & WALNUT ST;  NORRISTOWN; Station ...  19401.0   \n",
       "99490  WELSH RD & WEBSTER LN;  HORSHAM; Station 352; ...  19002.0   \n",
       "99491  MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...  19446.0   \n",
       "\n",
       "                             title            timeStamp                twp  \\\n",
       "0           EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1          EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2              Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3           EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4                   EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "...                            ...                  ...                ...   \n",
       "99487  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:06:00         NORRISTOWN   \n",
       "99488  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:07:02       LOWER MERION   \n",
       "99489             EMS: FALL VICTIM  2016-08-24 11:12:00         NORRISTOWN   \n",
       "99490         EMS: NAUSEA/VOMITING  2016-08-24 11:17:01            HORSHAM   \n",
       "99491  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:17:02      UPPER GWYNEDD   \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  \n",
       "...                               ... ..      ...  \n",
       "99487         MARKLEY ST & W LOGAN ST  1  Traffic  \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL  1  Traffic  \n",
       "99489         CHESTNUT ST & WALNUT ST  1      EMS  \n",
       "99490           WELSH RD & WEBSTER LN  1      EMS  \n",
       "99491          MORRIS RD & S BROAD ST  1  Traffic  \n",
       "\n",
       "[99492 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'] = df['title'].apply(lambda title : title.split(':')[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the most common Reason for a 911 call based off of this new column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df['Reason'].value_counts()\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Reason', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "** Now let us begin to focus on time information. What is the data type of the objects in the timeStamp column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timeStamp']=pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You can now grab specific attributes from a Datetime object by calling them. For example:**\n",
    "\n",
    "    time = df['timeStamp'].iloc[0]\n",
    "    time.hour\n",
    "\n",
    "**You can use Jupyter's tab method to explore the various attributes you can call. Now that the timestamp column are actually DateTime objects, use .apply() to create 3 new columns called Hour, Month, and Day of Week. You will create these columns based off of the timeStamp column, reference the solutions if you get stuck on this step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda time : time.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month'] = df['timeStamp'].apply(lambda time : time.month)\n",
    "df['Day of Week'] = df['timeStamp'].apply(lambda time : time.dayofweek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month  Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12            3  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12            3  \n",
       "2                    HAWS AVE  1   Fire    17     12            3  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12            3  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12            3  "
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day of Week']=df['Day of Week'].map(dmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12         Thu  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12         Thu  \n",
       "2                    HAWS AVE  1   Fire    17     12         Thu  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12         Thu  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12         Thu  "
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Day of Week', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Day of Week',hue='Reason',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Month',hue='Reason',data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Did you notice something strange about the Plot?**\n",
    "\n",
    "_____\n",
    "\n",
    "** You should have noticed it was missing some Months, let's see if we can maybe fill in this information by plotting the information in another way, possibly a simple line plot that fills in the missing months, in order to do this, we'll need to do some work with pandas... **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a gropuby object called byMonth, where you group the DataFrame by the month column and use the count() method for aggregation. Use the head() method on this returned DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "byMonth = df.groupby('Month').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>11527</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13203</td>\n",
       "      <td>13096</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>9930</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11465</td>\n",
       "      <td>11396</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>9755</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11092</td>\n",
       "      <td>11059</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>9895</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11323</td>\n",
       "      <td>11283</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>9946</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11420</td>\n",
       "      <td>11378</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>10212</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>11777</td>\n",
       "      <td>11732</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "      <td>11786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>10633</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>12133</td>\n",
       "      <td>12088</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "      <td>12137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>7832</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>9073</td>\n",
       "      <td>9025</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "      <td>9078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>6907</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>7963</td>\n",
       "      <td>7916</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "Month                                                                      \n",
       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "6      11786  11786  11786  10212  11786      11786  11777  11732  11786   \n",
       "7      12137  12137  12137  10633  12137      12137  12133  12088  12137   \n",
       "8       9078   9078   9078   7832   9078       9078   9073   9025   9078   \n",
       "12      7969   7969   7969   6907   7969       7969   7963   7916   7969   \n",
       "\n",
       "       Reason   Hour  Day of Week  \n",
       "Month                              \n",
       "1       13205  13205        13205  \n",
       "2       11467  11467        11467  \n",
       "3       11101  11101        11101  \n",
       "4       11326  11326        11326  \n",
       "5       11423  11423        11423  \n",
       "6       11786  11786        11786  \n",
       "7       12137  12137        12137  \n",
       "8        9078   9078         9078  \n",
       "12       7969   7969         7969  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth['lat'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now see if you can use seaborn's lmplot() to create a linear fit on the number of calls per month. Keep in mind you may need to reset the index to a column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x25f70cc99f0>"
      ]
     },
     "execution_count": 25,
     "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())"
   ]
  },
  {
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>2015-12-10</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",
       "      <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",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week        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  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12         Thu  2015-12-10  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12         Thu  2015-12-10  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] = df['timeStamp'].apply(lambda time : time.date())\n",
    "df.head()"
   ]
  },
  {
   "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": 27,
   "metadata": {
    "scrolled": true
   },
   "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",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now recreate this plot but create 3 separate plots with each plot representing a Reason for the 911 call**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "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": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='Fire'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('Fire')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Now let's move on to creating  heatmaps with seaborn and our data. We'll first need to restructure the dataframe so that the columns become the Hours and the Index becomes the Day of the Week. There are lots of ways to do this, but I would recommend trying to combine groupby with an [unstack](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html) method. Reference the solutions if you get stuck on this!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.groupby(by=['Day of Week','Hour']).count()['Reason'].unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hour', ylabel='Day of Week'>"
      ]
     },
     "execution_count": 32,
     "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(y,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a clustermap using this DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x25f70ebf760>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure(figsize=(12,6));\n",
    "sns.clustermap(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now repeat these same plots and operations, for a DataFrame that shows the Month as the column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = df.groupby(by=['Day of Week','Month']).count()['Reason'].unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month', ylabel='Day of Week'>"
      ]
     },
     "execution_count": 35,
     "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(z,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x25f70dd2b30>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure(figsize=(12,6));\n",
    "sns.clustermap(y,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Continue exploring the Data however you see fit!**\n",
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}