911 Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "26f3e8f5-172a-4c6e-8ae7-5b9b4ecce43a",
   "metadata": {},
   "source": [
    "# 911 Calls Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b73611a2-7b10-4714-96a1-2b48a42f1398",
   "metadata": {},
   "source": [
    "## Data and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e02af78-1ace-4412-a767-9f8e602cf8ce",
   "metadata": {},
   "source": [
    "### Import numpy, pandas, other visualization libraries and set %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "dd6fdc83-1caa-454b-9f36-76c5155d6227",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "047400e7-a46d-4fbc-b36b-2d97de39da4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (4.5,3.5)\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context(context='notebook', font_scale=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "db9c5db2-ec11-45d7-a291-802a9abe5158",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "bd0f0bc7-dc6d-4a6c-aa0b-ab7eb60c8063",
   "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "fe10bd35-fc57-405e-9912-e4162ef0279c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "3bc266a7-92be-46ab-925f-75708f284832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e\n",
       "1    99492\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['e'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20fa433b-eeb3-48e0-8f28-78f64b0326ed",
   "metadata": {},
   "source": [
    "### Basic Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d947a0e1-cd79-4342-9535-721befd82432",
   "metadata": {},
   "source": [
    "#### What are the top 5 Zipcodes for 911 calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "7135e529-47b4-415d-8ccd-5fd4b2f75d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zip\n",
       "19401.0    6979\n",
       "19464.0    6643\n",
       "19403.0    4854\n",
       "19446.0    4748\n",
       "19406.0    3174\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19514be6-35ae-4e45-af37-9e0b4860dac6",
   "metadata": {},
   "source": [
    "- These are zip codes where the maximum number of calls are coming from"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68bf5de-580e-49e3-964f-4d041b55289f",
   "metadata": {},
   "source": [
    "### What are top 5 townships(twp) for 911 calls?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "c93115cf-0c0c-4b5a-a48a-33169afd1b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "twp\n",
       "LOWER MERION    8443\n",
       "ABINGTON        5977\n",
       "NORRISTOWN      5890\n",
       "UPPER MERION    5227\n",
       "CHELTENHAM      4575\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e55946e-ab41-43bd-be6c-14fd81adbbff",
   "metadata": {},
   "source": [
    "#### Take a look at the title column, how many unique title codes are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "b3ca8019-e01c-419d-bfa3-e8f177f30e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lat          14579\n",
       "lng          14586\n",
       "desc         99455\n",
       "zip            104\n",
       "title          110\n",
       "timeStamp    72577\n",
       "twp             68\n",
       "addr         21914\n",
       "e                1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "84a02ef4-ca19-4258-ba75-95e072a1dfed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['EMS: BACK PAINS/INJURY', 'EMS: DIABETIC EMERGENCY',\n",
       "       'Fire: GAS-ODOR/LEAK', 'EMS: CARDIAC EMERGENCY', 'EMS: DIZZINESS',\n",
       "       'EMS: HEAD INJURY', 'EMS: NAUSEA/VOMITING',\n",
       "       'EMS: RESPIRATORY EMERGENCY', 'EMS: SYNCOPAL EPISODE',\n",
       "       'Traffic: VEHICLE ACCIDENT -', 'EMS: VEHICLE ACCIDENT',\n",
       "       'Traffic: DISABLED VEHICLE -', 'Fire: APPLIANCE FIRE',\n",
       "       'EMS: GENERAL WEAKNESS', 'Fire: CARBON MONOXIDE DETECTOR',\n",
       "       'EMS: UNKNOWN MEDICAL EMERGENCY', 'EMS: UNRESPONSIVE SUBJECT',\n",
       "       'Fire: VEHICLE ACCIDENT', 'EMS: ALTERED MENTAL STATUS',\n",
       "       'Fire: FIRE ALARM', 'EMS: CVA/STROKE',\n",
       "       'Traffic: ROAD OBSTRUCTION -', 'EMS: SUBJECT IN PAIN',\n",
       "       'EMS: HEMORRHAGING', 'EMS: FALL VICTIM', 'EMS: ASSAULT VICTIM',\n",
       "       'EMS: SEIZURES', 'EMS: MEDICAL ALERT ALARM',\n",
       "       'EMS: ABDOMINAL PAINS', 'Fire: PUMP DETAIL',\n",
       "       'Fire: FIRE INVESTIGATION', 'EMS: OVERDOSE', 'EMS: MATERNITY',\n",
       "       'EMS: UNCONSCIOUS SUBJECT', 'EMS: CHOKING', 'EMS: LACERATIONS',\n",
       "       'Fire: TRASH/DUMPSTER FIRE', 'Fire: UNKNOWN TYPE FIRE',\n",
       "       'Fire: BUILDING FIRE', 'Fire: ELECTRICAL FIRE OUTSIDE',\n",
       "       'Fire: DEBRIS/FLUIDS ON HIGHWAY',\n",
       "       'Traffic: DEBRIS/FLUIDS ON HIGHWAY -', 'EMS: FEVER',\n",
       "       'EMS: ALLERGIC REACTION', 'Traffic: VEHICLE LEAKING FUEL -',\n",
       "       'EMS: FRACTURE', 'Fire: BURN VICTIM', 'EMS: BURN VICTIM',\n",
       "       'Fire: RESCUE - GENERAL', 'Fire: WOODS/FIELD FIRE',\n",
       "       'EMS: RESCUE - GENERAL', 'Fire: FIRE SPECIAL SERVICE',\n",
       "       'Fire: VEHICLE FIRE', 'Traffic: VEHICLE FIRE -',\n",
       "       'EMS: WARRANT SERVICE', 'Fire: S/B AT HELICOPTER LANDING',\n",
       "       'EMS: EMS SPECIAL SERVICE', 'Traffic: HAZARDOUS ROAD CONDITIONS -',\n",
       "       'Fire: RESCUE - ELEVATOR', 'EMS: FIRE SPECIAL SERVICE',\n",
       "       'EMS: DEHYDRATION', 'EMS: CARBON MONOXIDE DETECTOR',\n",
       "       'EMS: BUILDING FIRE', 'EMS: APPLIANCE FIRE', 'EMS: SHOOTING',\n",
       "       'EMS: POISONING', 'Fire: TRANSFERRED CALL',\n",
       "       'Fire: RESCUE - TECHNICAL', 'EMS: RESCUE - TECHNICAL',\n",
       "       'Fire: VEHICLE LEAKING FUEL', 'EMS: EYE INJURY',\n",
       "       'EMS: ELECTROCUTION', 'EMS: STABBING', 'Fire: FIRE POLICE NEEDED',\n",
       "       'EMS: AMPUTATION', 'EMS: ANIMAL BITE', 'EMS: FIRE ALARM',\n",
       "       'EMS: VEHICLE FIRE', 'EMS: HAZARDOUS MATERIALS INCIDENT',\n",
       "       'EMS: RESCUE - ELEVATOR', 'EMS: FIRE INVESTIGATION',\n",
       "       'Fire: MEDICAL ALERT ALARM', 'EMS: UNKNOWN TYPE FIRE',\n",
       "       'EMS: GAS-ODOR/LEAK', 'Fire: TRAIN CRASH',\n",
       "       'Fire: HAZARDOUS MATERIALS INCIDENT', 'EMS: TRANSFERRED CALL',\n",
       "       'EMS: TRAIN CRASH', 'EMS: RESCUE - WATER',\n",
       "       'EMS: S/B AT HELICOPTER LANDING',\n",
       "       'Fire: UNKNOWN MEDICAL EMERGENCY', 'Fire: RESCUE - WATER',\n",
       "       'EMS: CARDIAC ARREST', 'EMS: PLANE CRASH', 'Fire: PLANE CRASH',\n",
       "       'EMS: WOODS/FIELD FIRE', 'Fire: CARDIAC ARREST',\n",
       "       'Fire: EMS SPECIAL SERVICE', 'Fire: UNCONSCIOUS SUBJECT',\n",
       "       'EMS: HEAT EXHAUSTION', 'EMS: DEBRIS/FLUIDS ON HIGHWAY',\n",
       "       'EMS: ACTIVE SHOOTER', 'EMS: DISABLED VEHICLE',\n",
       "       'Fire: POLICE INFORMATION', 'Fire: DIABETIC EMERGENCY',\n",
       "       'EMS: BOMB DEVICE FOUND', 'Fire: SYNCOPAL EPISODE',\n",
       "       'EMS: INDUSTRIAL ACCIDENT', 'EMS: DROWNING', 'EMS: SUSPICIOUS'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "8a143282-d87f-4ef0-b567-93797fef461e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['title'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab358aa8-248b-47da-86a3-07d78bdb009d",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bff83c-7480-452c-9f9a-602589258c45",
   "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 values.\n",
    "\n",
    "#### For Example, If the title column value is EMS: BACK PAINS/INJURY, the reason column value should be EMS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "c2d606f4-f28d-4244-b2f9-5592fea9524f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            EMS\n",
       "1            EMS\n",
       "2           Fire\n",
       "3            EMS\n",
       "4            EMS\n",
       "          ...   \n",
       "99487    Traffic\n",
       "99488    Traffic\n",
       "99489        EMS\n",
       "99490        EMS\n",
       "99491    Traffic\n",
       "Name: Reason, Length: 99492, dtype: object"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'] = df['title'].apply(lambda x:x.split(':')[0])\n",
    "df['Reason']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "56a5bf81-5eec-4e37-98d2-67bcfb0ef0be",
   "metadata": {},
   "outputs": [
    {
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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",
       "    </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": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363251af-1cfc-49b1-9558-18f7b715784c",
   "metadata": {},
   "source": [
    "#### What is the most common Reason for a 911 call based off this new column(Reason)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "7563033c-de7d-4549-bd3b-f133a2b4bf39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timeStamp\n",
       "2015-12-10 17:40:01    8\n",
       "2016-04-21 17:57:01    7\n",
       "2015-12-23 14:12:01    7\n",
       "2016-02-14 14:02:01    6\n",
       "2016-03-09 16:57:01    6\n",
       "                      ..\n",
       "2016-03-18 11:01:01    1\n",
       "2016-03-18 11:02:00    1\n",
       "2016-03-18 11:02:01    1\n",
       "2016-03-18 11:07:01    1\n",
       "2016-08-24 11:17:02    1\n",
       "Name: count, Length: 72577, dtype: int64"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = df['timeStamp'].value_counts()\n",
    "values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65498250-613d-4f83-a902-dd5c16506abb",
   "metadata": {},
   "source": [
    "#### Now use seaborn to create a countplot of 911 calls by Reason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "3515b93c-e93f-4854-98c9-47e5e9eb9a48",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shaik\\AppData\\Local\\Temp\\ipykernel_3240\\2377940984.py:1: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(data = df, x= 'Reason', palette='viridis');\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, x= 'Reason', palette='viridis');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a4d2dd-3ae1-4ec1-ab9b-7fe10138535e",
   "metadata": {},
   "source": [
    "- If you look at the calls for EMS it is around 48k\n",
    "- For fire it was 13-15k\n",
    "- For Traffic it was >30k and and <36k"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "003befe8-e518-4de4-9062-c60732fa38eb",
   "metadata": {},
   "source": [
    "#### Now let us begin to focus on time information. What is the data type of the objects in the time Stamp column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "693e8f1d-4fbf-42d0-96c8-e4be5ebf3a11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n"
     ]
    }
   ],
   "source": [
    "df['timeStamp'].dtype\n",
    "print(df['timeStamp'].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "49ea2506-52b4-417e-bcc7-aa563d512862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc6ddd8-a148-4e88-ae56-d6ff923178b8",
   "metadata": {},
   "source": [
    "#### Use pd.to_datetime to convert the column fro strings to Date Time Objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "c8411689-f0fc-4e6c-8d48-2e6b8fc7a005",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "8f6d7be3-6041-4dec-b275-8bc1098bfc33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timeStamp'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "539e16c4-fa8d-4299-a7df-1b7b65cf7600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab0d5f0-17e8-4673-95a9-7f43fd02e6dd",
   "metadata": {},
   "source": [
    "#### You can no wgrab specific attributes from a Datetime object by caling them . For example\n",
    "\n",
    " time = df['timeStamp'].iloc[0]\n",
    "\n",
    " time.hour\n",
    "\n",
    "#### We can use jupyters tab method to explore the various attributes that we 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 this columns based off of the timeStamp column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "9c2e0947-4500-4e3e-933d-1af28d35bd07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2015-12-10 17:40:00\n",
       "1       2015-12-10 17:40:00\n",
       "2       2015-12-10 17:40:00\n",
       "3       2015-12-10 17:40:01\n",
       "4       2015-12-10 17:40:01\n",
       "                ...        \n",
       "99487   2016-08-24 11:06:00\n",
       "99488   2016-08-24 11:07:02\n",
       "99489   2016-08-24 11:12:00\n",
       "99490   2016-08-24 11:17:01\n",
       "99491   2016-08-24 11:17:02\n",
       "Name: timeStamp, Length: 99492, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timeStamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "d828d814-2550-4df8-9ff0-cafe1258312d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda x: x.hour)\n",
    "df['Month'] = df['timeStamp'].apply(lambda x: x.month)\n",
    "df['Day of week'] = df['timeStamp'].apply(lambda x: x.day_of_week)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "31244640-42ac-4bd8-ac70-414b744b1fc1",
   "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": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "971568a3-9127-44ec-9702-b3845c597bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1     13205\n",
       "7     12137\n",
       "6     11786\n",
       "2     11467\n",
       "5     11423\n",
       "4     11326\n",
       "3     11101\n",
       "8      9078\n",
       "12     7969\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Month'].value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "3cc753e8-0472-44a0-85ee-934670e1be51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hour\n",
       "17    6517\n",
       "16    6490\n",
       "15    6154\n",
       "12    6029\n",
       "14    5997\n",
       "13    5967\n",
       "18    5762\n",
       "11    5543\n",
       "10    5413\n",
       "9     5314\n",
       "8     5044\n",
       "19    4908\n",
       "20    4377\n",
       "7     3970\n",
       "21    3788\n",
       "22    3283\n",
       "23    2559\n",
       "6     2513\n",
       "0     2112\n",
       "1     1721\n",
       "5     1629\n",
       "2     1549\n",
       "3     1435\n",
       "4     1418\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hour'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "a43b1e12-3f9d-4e82-930c-1b78dd1bb882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day of week\n",
       "1    15150\n",
       "2    14879\n",
       "4    14833\n",
       "0    14680\n",
       "3    14478\n",
       "5    13336\n",
       "6    12136\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day of week'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31719c7-db21-4293-89d8-5a90604d14fc",
   "metadata": {},
   "source": [
    "#### Notice how the Day of the Week is an integer 0-6. Use the .map() with this dictionary to map the actual string names of 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": 406,
   "id": "024b623e-e060-482e-98b6-0cdb0c10da47",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0 :'Mon', 1:'Tue', 2:'Wed', 3:'Thu', 4:'Fri',5:'Sat', 6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "d5457a83-05fc-40a6-9412-21ad1690bc24",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day of week'] = df['Day of week'].map(dmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "90b1b239-9c91-490d-a57b-2eb04187825a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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></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": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "fbdf679e-42e1-4d21-88b3-e68fc748e5cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day of week\n",
       "Tue    15150\n",
       "Wed    14879\n",
       "Fri    14833\n",
       "Mon    14680\n",
       "Thu    14478\n",
       "Sat    13336\n",
       "Sun    12136\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day of week'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb32861-598a-4730-af5d-014fadcee764",
   "metadata": {},
   "source": [
    "#### Now use Seaborn to create a countplot of the Day of the week column with the hue based off of the reason column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "65172102-71f7-4220-9a03-5fba5693d564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c866f82ed0>"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df , x = 'Day of week', hue ='Reason', palette = 'viridis')\n",
    "plt.legend(bbox_to_anchor=(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "0257a763-f793-4041-af3b-41be4967ebc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c8693208c0>"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df , x = 'Month', hue ='Reason', palette = 'viridis')\n",
    "plt.legend(bbox_to_anchor=(1.25,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc76353-7b69-466c-b03e-6f3142cb1137",
   "metadata": {},
   "source": [
    "#### Did you notice anything strange about the plot?\n",
    "\n",
    "- You should have notice that some months(sep,oct, and Nov) are missing lets see if we can fill the information by plotting the information in another way, possiby a simple line plot that fills in the missing months, in order to do this , we need to work with pandas again."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd5b778-7b1a-422f-b997-2537d85e1535",
   "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": 415,
   "id": "6ae774cb-6246-4fe5-9fa2-597517d83fab",
   "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": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth = df.groupby('Month').count()\n",
    "byMonth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aea6554-c274-4fcd-a402-418a8edb29be",
   "metadata": {},
   "source": [
    "#### Now create a simple plot off the dataframe indicating the count of calls per month?      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "60cf2689-5c99-4295-a514-eea9ac433a53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Month'>"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth.plot(kind='line', y='e', legend = False, xlim=(0,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79a4ba3-96c1-4405-8593-bf3cc5c12a7f",
   "metadata": {},
   "source": [
    "- What we see here is number of calls comes down from january and goes up again during the summer months and and falls of drastically off October and december months.\n",
    "- This also produces an estimated value for the number of calls, for the missing months."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862e9643-63dc-4751-babc-75c57b7b05c8",
   "metadata": {},
   "source": [
    "#### Now see if you can use seaborn's lmplot to create a linear fit on the number per month.Keep in mind you may need to reset the index to a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "3fd9baed-6d11-4d1c-b8c2-3a268e5fa385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day of week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>11527</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13203</td>\n",
       "      <td>13096</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>9930</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11465</td>\n",
       "      <td>11396</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>9755</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11092</td>\n",
       "      <td>11059</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>9895</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11323</td>\n",
       "      <td>11283</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>9946</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11420</td>\n",
       "      <td>11378</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\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>6</th>\n",
       "      <td>7</td>\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>7</th>\n",
       "      <td>8</td>\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>8</th>\n",
       "      <td>12</td>\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": [
       "   Month    lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "0      1  13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "1      2  11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "2      3  11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "3      4  11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "4      5  11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "5      6  11786  11786  11786  10212  11786      11786  11777  11732  11786   \n",
       "6      7  12137  12137  12137  10633  12137      12137  12133  12088  12137   \n",
       "7      8   9078   9078   9078   7832   9078       9078   9073   9025   9078   \n",
       "8     12   7969   7969   7969   6907   7969       7969   7963   7916   7969   \n",
       "\n",
       "   Reason   Hour  Day of week  \n",
       "0   13205  13205        13205  \n",
       "1   11467  11467        11467  \n",
       "2   11101  11101        11101  \n",
       "3   11326  11326        11326  \n",
       "4   11423  11423        11423  \n",
       "5   11786  11786        11786  \n",
       "6   12137  12137        12137  \n",
       "7    9078   9078         9078  \n",
       "8    7969   7969         7969  "
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth.reset_index(inplace=True)\n",
    "byMonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "25af5992-5599-4beb-b52d-70374e916034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c869492210>"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data = byMonth, x = 'Month' ,y= 'e', height=4, scatter_kws= {'s':10}, line_kws={'lw':2}) #scatter_kws-> s represents the size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3075bd3-8eab-463c-91aa-708fffaeb153",
   "metadata": {},
   "source": [
    "- The shadow represents the error\n",
    "- What we see is as we go from january to decemeber, The regression analysis is showing that number of calls per month goes lower and lower .\n",
    "- However if you see the the scattered point it goes down and then it goes up again before it crashes in august and december\n",
    "- -Thats why its showing a much larger margin of error there over the shadow portion.\n",
    "- So the actual data points could lie anywhere between 8500 to 12000 for the month October.so it desnt give a good fit here because of this high error."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f0f368-2983-4685-bcbd-7c5664951592",
   "metadata": {},
   "source": [
    "#### Create a new column called 'Date' that contains the date from the rimestamp column. You will need to use  apply along with the .date() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "88aa0ea7-e57c-4d55-a1bd-8372782c31fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2015-12-10\n",
       "1        2015-12-10\n",
       "2        2015-12-10\n",
       "3        2015-12-10\n",
       "4        2015-12-10\n",
       "            ...    \n",
       "99487    2016-08-24\n",
       "99488    2016-08-24\n",
       "99489    2016-08-24\n",
       "99490    2016-08-24\n",
       "99491    2016-08-24\n",
       "Name: Date, Length: 99492, dtype: object"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date']= df['timeStamp'].apply(lambda x:x.date())\n",
    "df['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "8c97a4b0-2b21-4411-a173-b5231f625e66",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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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",
       "      <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": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "11b5a1f8-bd4a-4d0a-beba-d6016a88e366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['Date'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2157fa5-25ed-4d17-8fc3-2b68fea32f83",
   "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": 428,
   "id": "e7e9b432-9d5e-436e-86fb-864149fc7e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "byDate = df.groupby('Date').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "02fc1e39-c4f1-437d-ad63-c0092dd60f08",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  </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",
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       "    <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",
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       "      <td>396</td>\n",
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       "      <th>2015-12-12</th>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
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       "      <th>2015-12-13</th>\n",
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       "      <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",
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       "      <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2016-08-20</th>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>279</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>327</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-21</th>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>299</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>352</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-22</th>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>336</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>388</td>\n",
       "      <td>384</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-23</th>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>390</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>437</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
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       "    <tr>\n",
       "      <th>2016-08-24</th>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>106</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 13 columns</p>\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",
       "2016-08-20  328  328   328  279    328        328  328   327  328     328   \n",
       "2016-08-21  357  357   357  299    357        357  357   352  357     357   \n",
       "2016-08-22  389  389   389  336    389        389  388   384  389     389   \n",
       "2016-08-23  439  439   439  390    439        439  439   437  439     439   \n",
       "2016-08-24  132  132   132  106    132        132  132   132  132     132   \n",
       "\n",
       "            Hour  Month  Day of week  \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  \n",
       "...          ...    ...          ...  \n",
       "2016-08-20   328    328          328  \n",
       "2016-08-21   357    357          357  \n",
       "2016-08-22   389    389          389  \n",
       "2016-08-23   439    439          439  \n",
       "2016-08-24   132    132          132  \n",
       "\n",
       "[259 rows x 13 columns]"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "cf546279-c840-4208-80ae-ec7317e93f33",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of week</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  Day of week  \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": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "fd93e967-64d0-406c-a213-2bb839316dc4",
   "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",
       "    <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>2016-08-20</th>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>279</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>327</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-21</th>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>299</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>352</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-22</th>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>336</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>388</td>\n",
       "      <td>384</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-23</th>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>390</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>437</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-24</th>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>106</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            lat  lng  desc  zip  title  timeStamp  twp  addr    e  Reason  \\\n",
       "Date                                                                        \n",
       "2016-08-20  328  328   328  279    328        328  328   327  328     328   \n",
       "2016-08-21  357  357   357  299    357        357  357   352  357     357   \n",
       "2016-08-22  389  389   389  336    389        389  388   384  389     389   \n",
       "2016-08-23  439  439   439  390    439        439  439   437  439     439   \n",
       "2016-08-24  132  132   132  106    132        132  132   132  132     132   \n",
       "\n",
       "            Hour  Month  Day of week  \n",
       "Date                                  \n",
       "2016-08-20   328    328          328  \n",
       "2016-08-21   357    357          357  \n",
       "2016-08-22   389    389          389  \n",
       "2016-08-23   439    439          439  \n",
       "2016-08-24   132    132          132  "
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDate.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84463987-6e49-4874-8ee1-3f3d5cf92f8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "a793fbe2-53f2-48d6-ba3c-b72879277401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byDate.plot(kind='line', y ='e', figsize=(8,4), legend = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47220c4a-ddbe-4fc1-be97-10b9ac25c9ae",
   "metadata": {},
   "source": [
    "- The data actually starts from 2015 , then it goes till the 8th month i.e, is August of 2016.\n",
    "- In the begining i have predicted that we might have a missing data in the year 2016 rather this graph explains us that the data starts somewhere in the month of december  in 2015 and continues only till 2016.\n",
    "- The data starts from 10th of Dec 2015 till 24th of August 2016.\n",
    "- The number of calls is less in August and Decmeber because we do not have the data of the complete months.\n",
    "- If you analyse this data this is the total calls byDate,  On any individual day it looks like the total number of calls varies between 300 and 450 calls with spikes on certain days.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85c70fd-9451-4dcc-892a-7efa2a4eeff2",
   "metadata": {},
   "source": [
    "#### Now recreate the plot but create 3 separate plots with each plot representing a Reason for the call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "d8e1c06c-d5ed-4a3a-a1f5-b5d29ff2d4b4",
   "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",
       "    <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>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>108</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>137</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>109</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>144</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>54</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>150</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>184</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</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   43   43    43   35     43         43   43    41   43      43   \n",
       "2015-12-11  141  141   141  108    141        141  141   137  141     141   \n",
       "2015-12-12  146  146   146  109    146        146  146   144  146     146   \n",
       "2015-12-13   78   78    78   54     78         78   78    76   78      78   \n",
       "2015-12-14  186  186   186  150    186        186  186   184  186     186   \n",
       "\n",
       "            Hour  Month  Day of week  \n",
       "Date                                  \n",
       "2015-12-10    43     43           43  \n",
       "2015-12-11   141    141          141  \n",
       "2015-12-12   146    146          146  \n",
       "2015-12-13    78     78           78  \n",
       "2015-12-14   186    186          186  "
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDateTraffic = df[df['Reason'] == 'Traffic'].groupby('Date').count()\n",
    "byDateTraffic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "5932f404-8ac5-44d9-903b-8715279d02cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Traffic'}, xlabel='Date'>"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byDateTraffic.plot(kind='line', y='e' , figsize=(8,4), legend=False, title = 'Traffic')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35480f70-431a-49db-b75c-8698162f859d",
   "metadata": {},
   "source": [
    "- If you see the graph for the traffic reason , the number of calls are varying between 80 and 200 with spikes on certain days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "95cf9991-e3d5-4f39-b6b6-6d8a6969f4a1",
   "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",
       "    <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>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>59</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>53</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</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   15   15    15   13     15         15   15    15  15      15   \n",
       "2015-12-11   69   69    69   59     69         69   68    68  69      69   \n",
       "2015-12-12   68   68    68   53     68         68   68    68  68      68   \n",
       "2015-12-13   51   51    51   48     51         51   51    51  51      51   \n",
       "2015-12-14   39   39    39   36     39         39   38    39  39      39   \n",
       "\n",
       "            Hour  Month  Day of week  \n",
       "Date                                  \n",
       "2015-12-10    15     15           15  \n",
       "2015-12-11    69     69           69  \n",
       "2015-12-12    68     68           68  \n",
       "2015-12-13    51     51           51  \n",
       "2015-12-14    39     39           39  "
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDateFire = df[df['Reason'] == 'Fire'].groupby('Date').count()\n",
    "byDateFire.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "eed175b6-9711-4292-834a-3ae36f855210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Fire'}, xlabel='Date'>"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byDateFire.plot(kind='line', y='e' , figsize=(8,4), legend=False, title = 'Fire')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b7d933-e673-40d1-b10e-603cb3ca5ae2",
   "metadata": {},
   "source": [
    "- If you see the graph for the Fire reason the number of calls on each day varies in between 30 to 80 calls on a normal days with a spike on certain days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "293f3c70-2fbf-49df-ba35-a1dbc4e5bff7",
   "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",
       "    <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>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>166</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>171</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>178</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>201</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</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   57   57    57   52     57         57   57    57   57      57   \n",
       "2015-12-11  186  186   186  166    186        186  186   186  186     186   \n",
       "2015-12-12  189  189   189  171    189        189  189   189  189     189   \n",
       "2015-12-13  190  190   190  178    190        190  190   190  190     190   \n",
       "2015-12-14  222  222   222  201    222        222  222   222  222     222   \n",
       "\n",
       "            Hour  Month  Day of week  \n",
       "Date                                  \n",
       "2015-12-10    57     57           57  \n",
       "2015-12-11   186    186          186  \n",
       "2015-12-12   189    189          189  \n",
       "2015-12-13   190    190          190  \n",
       "2015-12-14   222    222          222  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDateEMS = df[df['Reason'] == 'EMS'].groupby('Date').count()\n",
    "byDateEMS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "6a35cad1-72cd-4bd8-8935-dd5a249bd2a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byDateEMS.plot(kind = 'line', y = 'e',  figsize=(8,4), legend=False, title = 'EMS');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efcdd7fe-1b37-44c5-bf31-f58a7e4a3db4",
   "metadata": {},
   "source": [
    "- Here it looks like the calls vary between 150 and 225 with spikes on downward direction at the end of april or first of may."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c098cf79-0199-4b93-984f-d8c156439d80",
   "metadata": {},
   "source": [
    "#### Now lets 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 a lots of ways to do this , but I would recommend trying to combine group by with an unstack method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "2bfc16fe-54bb-4b53-a10a-badcfb0be547",
   "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": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f03e81f-e3b0-4d24-9b48-6adb56802b31",
   "metadata": {},
   "source": [
    "- The easiest way to do this is using pivot table. If you check the dataframe we have added the new columns Reason, Hour Month and Day of the week and Date. What I want here is, 'Day of week' to be the index and the hour column actually be the columns and the hour varies between 0 to 23.\n",
    "- So I want 24 columns for each hour of day and seven rows for each day of the week.\n",
    "- Easiest way to to it is using the pivot table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "1c226fe7-2b26-4ef8-9b64-e14df60a78cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>275</td>\n",
       "      <td>235</td>\n",
       "      <td>191</td>\n",
       "      <td>175</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>372</td>\n",
       "      <td>598</td>\n",
       "      <td>742</td>\n",
       "      <td>752</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>980</td>\n",
       "      <td>1039</td>\n",
       "      <td>980</td>\n",
       "      <td>820</td>\n",
       "      <td>696</td>\n",
       "      <td>667</td>\n",
       "      <td>559</td>\n",
       "      <td>514</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>282</td>\n",
       "      <td>221</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>204</td>\n",
       "      <td>267</td>\n",
       "      <td>397</td>\n",
       "      <td>653</td>\n",
       "      <td>819</td>\n",
       "      <td>786</td>\n",
       "      <td>...</td>\n",
       "      <td>869</td>\n",
       "      <td>913</td>\n",
       "      <td>989</td>\n",
       "      <td>997</td>\n",
       "      <td>885</td>\n",
       "      <td>746</td>\n",
       "      <td>613</td>\n",
       "      <td>497</td>\n",
       "      <td>472</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>375</td>\n",
       "      <td>301</td>\n",
       "      <td>263</td>\n",
       "      <td>260</td>\n",
       "      <td>224</td>\n",
       "      <td>231</td>\n",
       "      <td>257</td>\n",
       "      <td>391</td>\n",
       "      <td>459</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>789</td>\n",
       "      <td>796</td>\n",
       "      <td>848</td>\n",
       "      <td>757</td>\n",
       "      <td>778</td>\n",
       "      <td>696</td>\n",
       "      <td>628</td>\n",
       "      <td>572</td>\n",
       "      <td>506</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>383</td>\n",
       "      <td>306</td>\n",
       "      <td>286</td>\n",
       "      <td>268</td>\n",
       "      <td>242</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "      <td>402</td>\n",
       "      <td>483</td>\n",
       "      <td>620</td>\n",
       "      <td>...</td>\n",
       "      <td>684</td>\n",
       "      <td>691</td>\n",
       "      <td>663</td>\n",
       "      <td>714</td>\n",
       "      <td>670</td>\n",
       "      <td>655</td>\n",
       "      <td>537</td>\n",
       "      <td>461</td>\n",
       "      <td>415</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>278</td>\n",
       "      <td>202</td>\n",
       "      <td>233</td>\n",
       "      <td>159</td>\n",
       "      <td>182</td>\n",
       "      <td>203</td>\n",
       "      <td>362</td>\n",
       "      <td>570</td>\n",
       "      <td>777</td>\n",
       "      <td>828</td>\n",
       "      <td>...</td>\n",
       "      <td>876</td>\n",
       "      <td>969</td>\n",
       "      <td>935</td>\n",
       "      <td>1013</td>\n",
       "      <td>810</td>\n",
       "      <td>698</td>\n",
       "      <td>617</td>\n",
       "      <td>553</td>\n",
       "      <td>424</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>269</td>\n",
       "      <td>240</td>\n",
       "      <td>186</td>\n",
       "      <td>170</td>\n",
       "      <td>209</td>\n",
       "      <td>239</td>\n",
       "      <td>415</td>\n",
       "      <td>655</td>\n",
       "      <td>889</td>\n",
       "      <td>880</td>\n",
       "      <td>...</td>\n",
       "      <td>943</td>\n",
       "      <td>938</td>\n",
       "      <td>1026</td>\n",
       "      <td>1019</td>\n",
       "      <td>905</td>\n",
       "      <td>731</td>\n",
       "      <td>647</td>\n",
       "      <td>571</td>\n",
       "      <td>462</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>250</td>\n",
       "      <td>216</td>\n",
       "      <td>189</td>\n",
       "      <td>209</td>\n",
       "      <td>156</td>\n",
       "      <td>255</td>\n",
       "      <td>410</td>\n",
       "      <td>701</td>\n",
       "      <td>875</td>\n",
       "      <td>808</td>\n",
       "      <td>...</td>\n",
       "      <td>904</td>\n",
       "      <td>867</td>\n",
       "      <td>990</td>\n",
       "      <td>1037</td>\n",
       "      <td>894</td>\n",
       "      <td>686</td>\n",
       "      <td>668</td>\n",
       "      <td>575</td>\n",
       "      <td>490</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Hour          0    1    2    3    4    5    6    7    8    9   ...   14   15  \\\n",
       "Day of week                                                    ...             \n",
       "Fri          275  235  191  175  201  194  372  598  742  752  ...  932  980   \n",
       "Mon          282  221  201  194  204  267  397  653  819  786  ...  869  913   \n",
       "Sat          375  301  263  260  224  231  257  391  459  640  ...  789  796   \n",
       "Sun          383  306  286  268  242  240  300  402  483  620  ...  684  691   \n",
       "Thu          278  202  233  159  182  203  362  570  777  828  ...  876  969   \n",
       "Tue          269  240  186  170  209  239  415  655  889  880  ...  943  938   \n",
       "Wed          250  216  189  209  156  255  410  701  875  808  ...  904  867   \n",
       "\n",
       "Hour           16    17   18   19   20   21   22   23  \n",
       "Day of week                                            \n",
       "Fri          1039   980  820  696  667  559  514  474  \n",
       "Mon           989   997  885  746  613  497  472  325  \n",
       "Sat           848   757  778  696  628  572  506  467  \n",
       "Sun           663   714  670  655  537  461  415  330  \n",
       "Thu           935  1013  810  698  617  553  424  354  \n",
       "Tue          1026  1019  905  731  647  571  462  274  \n",
       "Wed           990  1037  894  686  668  575  490  335  \n",
       "\n",
       "[7 rows x 24 columns]"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index = 'Day of week', columns = 'Hour', values = 'e', aggfunc = 'sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "805106c2-4784-4f80-8227-a6c9e12f0ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<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>Day of week</th>\n",
       "      <th>Hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </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",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Wed</th>\n",
       "      <th>19</th>\n",
       "      <td>686</td>\n",
       "      <td>686</td>\n",
       "      <td>686</td>\n",
       "      <td>590</td>\n",
       "      <td>686</td>\n",
       "      <td>686</td>\n",
       "      <td>686</td>\n",
       "      <td>682</td>\n",
       "      <td>686</td>\n",
       "      <td>686</td>\n",
       "      <td>686</td>\n",
       "      <td>686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>597</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>662</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>575</td>\n",
       "      <td>575</td>\n",
       "      <td>575</td>\n",
       "      <td>508</td>\n",
       "      <td>575</td>\n",
       "      <td>575</td>\n",
       "      <td>574</td>\n",
       "      <td>572</td>\n",
       "      <td>575</td>\n",
       "      <td>575</td>\n",
       "      <td>575</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>432</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>485</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>335</td>\n",
       "      <td>335</td>\n",
       "      <td>335</td>\n",
       "      <td>294</td>\n",
       "      <td>335</td>\n",
       "      <td>335</td>\n",
       "      <td>334</td>\n",
       "      <td>335</td>\n",
       "      <td>335</td>\n",
       "      <td>335</td>\n",
       "      <td>335</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lat  lng  desc  zip  title  timeStamp  twp  addr    e  \\\n",
       "Day of week Hour                                                          \n",
       "Fri         0     275  275   275  248    275        275  275   275  275   \n",
       "            1     235  235   235  200    235        235  235   232  235   \n",
       "            2     191  191   191  165    191        191  191   191  191   \n",
       "            3     175  175   175  164    175        175  175   175  175   \n",
       "            4     201  201   201  184    201        201  201   201  201   \n",
       "...               ...  ...   ...  ...    ...        ...  ...   ...  ...   \n",
       "Wed         19    686  686   686  590    686        686  686   682  686   \n",
       "            20    668  668   668  597    668        668  668   662  668   \n",
       "            21    575  575   575  508    575        575  574   572  575   \n",
       "            22    490  490   490  432    490        490  490   485  490   \n",
       "            23    335  335   335  294    335        335  334   335  335   \n",
       "\n",
       "                  Reason  Month  Date  \n",
       "Day of week Hour                       \n",
       "Fri         0        275    275   275  \n",
       "            1        235    235   235  \n",
       "            2        191    191   191  \n",
       "            3        175    175   175  \n",
       "            4        201    201   201  \n",
       "...                  ...    ...   ...  \n",
       "Wed         19       686    686   686  \n",
       "            20       668    668   668  \n",
       "            21       575    575   575  \n",
       "            22       490    490   490  \n",
       "            23       335    335   335  \n",
       "\n",
       "[168 rows x 12 columns]"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Getting the same results using unstack method\n",
    "## Here we use the groupby method and rather than grouping by single column, we will pass in a list of two columns and that\n",
    "## will produce a multi index dataframe\n",
    "\n",
    "df.groupby(['Day of week', 'Hour']).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d60288-3492-4cd9-bceb-5a483c7ad9b3",
   "metadata": {},
   "source": [
    "- If  we observe the dataframe we can see two indexes here one is 'Day of Week' and 'Hour' for each day of the week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "edc0ed19-6574-4864-a00f-ebcc5bd87447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>275</td>\n",
       "      <td>235</td>\n",
       "      <td>191</td>\n",
       "      <td>175</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>372</td>\n",
       "      <td>598</td>\n",
       "      <td>742</td>\n",
       "      <td>752</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>980</td>\n",
       "      <td>1039</td>\n",
       "      <td>980</td>\n",
       "      <td>820</td>\n",
       "      <td>696</td>\n",
       "      <td>667</td>\n",
       "      <td>559</td>\n",
       "      <td>514</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>282</td>\n",
       "      <td>221</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>204</td>\n",
       "      <td>267</td>\n",
       "      <td>397</td>\n",
       "      <td>653</td>\n",
       "      <td>819</td>\n",
       "      <td>786</td>\n",
       "      <td>...</td>\n",
       "      <td>869</td>\n",
       "      <td>913</td>\n",
       "      <td>989</td>\n",
       "      <td>997</td>\n",
       "      <td>885</td>\n",
       "      <td>746</td>\n",
       "      <td>613</td>\n",
       "      <td>497</td>\n",
       "      <td>472</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>375</td>\n",
       "      <td>301</td>\n",
       "      <td>263</td>\n",
       "      <td>260</td>\n",
       "      <td>224</td>\n",
       "      <td>231</td>\n",
       "      <td>257</td>\n",
       "      <td>391</td>\n",
       "      <td>459</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>789</td>\n",
       "      <td>796</td>\n",
       "      <td>848</td>\n",
       "      <td>757</td>\n",
       "      <td>778</td>\n",
       "      <td>696</td>\n",
       "      <td>628</td>\n",
       "      <td>572</td>\n",
       "      <td>506</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>383</td>\n",
       "      <td>306</td>\n",
       "      <td>286</td>\n",
       "      <td>268</td>\n",
       "      <td>242</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "      <td>402</td>\n",
       "      <td>483</td>\n",
       "      <td>620</td>\n",
       "      <td>...</td>\n",
       "      <td>684</td>\n",
       "      <td>691</td>\n",
       "      <td>663</td>\n",
       "      <td>714</td>\n",
       "      <td>670</td>\n",
       "      <td>655</td>\n",
       "      <td>537</td>\n",
       "      <td>461</td>\n",
       "      <td>415</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>278</td>\n",
       "      <td>202</td>\n",
       "      <td>233</td>\n",
       "      <td>159</td>\n",
       "      <td>182</td>\n",
       "      <td>203</td>\n",
       "      <td>362</td>\n",
       "      <td>570</td>\n",
       "      <td>777</td>\n",
       "      <td>828</td>\n",
       "      <td>...</td>\n",
       "      <td>876</td>\n",
       "      <td>969</td>\n",
       "      <td>935</td>\n",
       "      <td>1013</td>\n",
       "      <td>810</td>\n",
       "      <td>698</td>\n",
       "      <td>617</td>\n",
       "      <td>553</td>\n",
       "      <td>424</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>269</td>\n",
       "      <td>240</td>\n",
       "      <td>186</td>\n",
       "      <td>170</td>\n",
       "      <td>209</td>\n",
       "      <td>239</td>\n",
       "      <td>415</td>\n",
       "      <td>655</td>\n",
       "      <td>889</td>\n",
       "      <td>880</td>\n",
       "      <td>...</td>\n",
       "      <td>943</td>\n",
       "      <td>938</td>\n",
       "      <td>1026</td>\n",
       "      <td>1019</td>\n",
       "      <td>905</td>\n",
       "      <td>731</td>\n",
       "      <td>647</td>\n",
       "      <td>571</td>\n",
       "      <td>462</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>250</td>\n",
       "      <td>216</td>\n",
       "      <td>189</td>\n",
       "      <td>209</td>\n",
       "      <td>156</td>\n",
       "      <td>255</td>\n",
       "      <td>410</td>\n",
       "      <td>701</td>\n",
       "      <td>875</td>\n",
       "      <td>808</td>\n",
       "      <td>...</td>\n",
       "      <td>904</td>\n",
       "      <td>867</td>\n",
       "      <td>990</td>\n",
       "      <td>1037</td>\n",
       "      <td>894</td>\n",
       "      <td>686</td>\n",
       "      <td>668</td>\n",
       "      <td>575</td>\n",
       "      <td>490</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Hour          0    1    2    3    4    5    6    7    8    9   ...   14   15  \\\n",
       "Day of week                                                    ...             \n",
       "Fri          275  235  191  175  201  194  372  598  742  752  ...  932  980   \n",
       "Mon          282  221  201  194  204  267  397  653  819  786  ...  869  913   \n",
       "Sat          375  301  263  260  224  231  257  391  459  640  ...  789  796   \n",
       "Sun          383  306  286  268  242  240  300  402  483  620  ...  684  691   \n",
       "Thu          278  202  233  159  182  203  362  570  777  828  ...  876  969   \n",
       "Tue          269  240  186  170  209  239  415  655  889  880  ...  943  938   \n",
       "Wed          250  216  189  209  156  255  410  701  875  808  ...  904  867   \n",
       "\n",
       "Hour           16    17   18   19   20   21   22   23  \n",
       "Day of week                                            \n",
       "Fri          1039   980  820  696  667  559  514  474  \n",
       "Mon           989   997  885  746  613  497  472  325  \n",
       "Sat           848   757  778  696  628  572  506  467  \n",
       "Sun           663   714  670  655  537  461  415  330  \n",
       "Thu           935  1013  810  698  617  553  424  354  \n",
       "Tue          1026  1019  905  731  647  571  462  274  \n",
       "Wed           990  1037  894  686  668  575  490  335  \n",
       "\n",
       "[7 rows x 24 columns]"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lets use unstack on it \n",
    "\n",
    "byDayHour = df.groupby(['Day of week', 'Hour']).count()['e'].unstack()\n",
    "byDayHour\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1cada0-b9bf-4c04-ad0a-7a11e8d4a897",
   "metadata": {},
   "source": [
    "- This is a 7 x 24 df . We have seven days of the week and 24 hours of the day ranging from 0 to 23 and we have the sum that is the count for each row which has 'e' value as 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "684c7f08-ee17-44d2-b122-abc00853d21c",
   "metadata": {},
   "source": [
    "#### Now create a HeatMap using this DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "5cdfdf70-beaa-4ea9-acdd-7eedab96c321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "sns.heatmap(data = byDayHour, cmap = 'viridis_r');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c2653f-2040-4f2b-b1fe-d377fcc3c684",
   "metadata": {},
   "source": [
    "- If you see the color bar the darker color has the higher value and the lighter color has the lower value.\n",
    "- We have the days of the week on the y axis and hours of the day on the x axis.\n",
    "- At midnight i.e, between 01 A.M to 5 A.M the values are lower and if you go through the day from the morning to the afternoon and evening the the colors become darker and darker thrpught the day and starts getting lighter again as you reach 9 P.m, 10 P.m and 11 P.m.\n",
    "- If you look at the Y axis the days of the week sat and sun has lighter colors compared to the other days of the week\n",
    "- The number of calls increased as you go through the day.\n",
    "- Most of the calls have been around three to seven in the evening and similiarly most of the calls happen on the weekdays than on the weekends.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb0b3a8-e621-432f-bfa3-54e1d7afee22",
   "metadata": {},
   "source": [
    "#### Create a clustermap using the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "885c7784-4479-420d-ac30-bbe8c796bba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.clustermap(data =byDayHour, cmap= 'viridis_r',figsize=(6, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b305f64e-91bd-4c24-87be-c96295f3fe06",
   "metadata": {},
   "source": [
    "#### Lets repeat these same plots and operations, for a DataFrame that shows the Month as the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "62d127eb-a96e-46a0-a971-bf67504fe56c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>1970</td>\n",
       "      <td>1581</td>\n",
       "      <td>1525</td>\n",
       "      <td>1958</td>\n",
       "      <td>1730</td>\n",
       "      <td>1649</td>\n",
       "      <td>2045</td>\n",
       "      <td>1310</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>1727</td>\n",
       "      <td>1964</td>\n",
       "      <td>1535</td>\n",
       "      <td>1598</td>\n",
       "      <td>1779</td>\n",
       "      <td>1617</td>\n",
       "      <td>1692</td>\n",
       "      <td>1511</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2291</td>\n",
       "      <td>1441</td>\n",
       "      <td>1266</td>\n",
       "      <td>1734</td>\n",
       "      <td>1444</td>\n",
       "      <td>1388</td>\n",
       "      <td>1695</td>\n",
       "      <td>1099</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>1960</td>\n",
       "      <td>1229</td>\n",
       "      <td>1102</td>\n",
       "      <td>1488</td>\n",
       "      <td>1424</td>\n",
       "      <td>1333</td>\n",
       "      <td>1672</td>\n",
       "      <td>1021</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>1584</td>\n",
       "      <td>1596</td>\n",
       "      <td>1900</td>\n",
       "      <td>1601</td>\n",
       "      <td>1590</td>\n",
       "      <td>2065</td>\n",
       "      <td>1646</td>\n",
       "      <td>1230</td>\n",
       "      <td>1266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>1973</td>\n",
       "      <td>1753</td>\n",
       "      <td>1884</td>\n",
       "      <td>1430</td>\n",
       "      <td>1918</td>\n",
       "      <td>1676</td>\n",
       "      <td>1670</td>\n",
       "      <td>1612</td>\n",
       "      <td>1234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>1700</td>\n",
       "      <td>1903</td>\n",
       "      <td>1889</td>\n",
       "      <td>1517</td>\n",
       "      <td>1538</td>\n",
       "      <td>2058</td>\n",
       "      <td>1717</td>\n",
       "      <td>1295</td>\n",
       "      <td>1262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month          1     2     3     4     5     6     7     8     12\n",
       "Day of week                                                      \n",
       "Fri          1970  1581  1525  1958  1730  1649  2045  1310  1065\n",
       "Mon          1727  1964  1535  1598  1779  1617  1692  1511  1257\n",
       "Sat          2291  1441  1266  1734  1444  1388  1695  1099   978\n",
       "Sun          1960  1229  1102  1488  1424  1333  1672  1021   907\n",
       "Thu          1584  1596  1900  1601  1590  2065  1646  1230  1266\n",
       "Tue          1973  1753  1884  1430  1918  1676  1670  1612  1234\n",
       "Wed          1700  1903  1889  1517  1538  2058  1717  1295  1262"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDayMonth = df.groupby(['Day of week', 'Month']).count()['e'].unstack()\n",
    "byDayMonth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a91a4e-1392-4007-9b03-a6969e8cf8bd",
   "metadata": {},
   "source": [
    "- We have our days on the row index and months on the column  and we have total number of calls and the data is given as a form of matrix where the actual data relates to the column and row index.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "aecd6338-eb5f-47f5-a4b1-1715f05011f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.heatmap(data = byDayMonth, cmap = 'viridis_r');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b701a933-9cee-43cc-b32e-8d6cf3f08c48",
   "metadata": {},
   "source": [
    "- So here we have the day data on thr row index and the month data on the column index.\n",
    "- Analysing earlier i have realised that the data actually starts from december 10th of 2015 and went till 24 th of august 2016.\n",
    "- -The data was incompte for the months december and august so obiviously number of calls were lesser compared to other months.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "a34bc2e4-1295-402c-aac0-d3089048531a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(data = byDayMonth, cmap = 'viridis_r', figsize=(6,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e2628c-fb8a-4a31-8147-df7930b31f8c",
   "metadata": {},
   "source": [
    "### Observations\n",
    "- In this Project Its seen that more calls are made on weekdays than on weekends.\n",
    "- And also more calls were made on late afternoon and evening hours than compared to late nighs and morning hours\n",
    "- That kind of make sense as people are more active during these times so there would be more traffic calls or maybe EMS calls.\n",
    "- However there was no pattern observed for months data partly due to the missing daa for the fall and the winter months.\n",
    "- If the missing data is found then it could be analysed for the different reasons for summer and winter months as there could be more fire incidents in the summers and more traffic related calls in the winter months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "436e0e65-0ab1-4482-a270-dc23b4663b52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.4"
  }
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