01-911 Calls Data Capstone Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Capstone Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this capstone project we will be analyzing some 911 call data from [Kaggle](https://www.kaggle.com/mchirico/montcoalert). The data contains the following fields:\n",
    "\n",
    "* lat : String variable, Latitude\n",
    "* lng: String variable, Longitude\n",
    "* desc: String variable, Description of the Emergency Call\n",
    "* zip: String variable, Zipcode\n",
    "* title: String variable, Title\n",
    "* timeStamp: String variable, YYYY-MM-DD HH:MM:SS\n",
    "* twp: String variable, Township\n",
    "* addr: String variable, Address\n",
    "* e: String variable, Dummy variable (always 1)\n",
    "\n",
    "Just go along with this notebook and try to complete the instructions or answer the questions in bold using your Python and Data Science skills!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Import numpy and pandas **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import visualization libraries and set %matplotlib inline. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the csv file as a dataframe called df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('911.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() of the df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 zipcodes for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19401.0    6979\n",
       "19464.0    6643\n",
       "19403.0    4854\n",
       "19446.0    4748\n",
       "19406.0    3174\n",
       "Name: zip, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts()#values are already sorted with value_counts\n",
    "\n",
    "#print only 5\n",
    "df['zip'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 townships (twp) for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LOWER MERION    8443\n",
       "ABINGTON        5977\n",
       "NORRISTOWN      5890\n",
       "UPPER MERION    5227\n",
       "CHELTENHAM      4575\n",
       "Name: twp, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Take a look at the 'title' column, how many unique title codes are there? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['title'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** In the titles column there are \"Reasons/Departments\" specified before the title code. These are EMS, Fire, and Traffic. Use .apply() with a custom lambda expression to create a new column called \"Reason\" that contains this string value.** \n",
    "\n",
    "**For example, if the title column value is EMS: BACK PAINS/INJURY , the Reason column value would be EMS. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMS: BACK PAINS/INJURY'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df['title'].iloc[0]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EMS', ' BACK PAINS/INJURY']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.split(':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMS'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.split(':')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             [EMS,  BACK PAINS/INJURY]\n",
       "1            [EMS,  DIABETIC EMERGENCY]\n",
       "2                [Fire,  GAS-ODOR/LEAK]\n",
       "3             [EMS,  CARDIAC EMERGENCY]\n",
       "4                     [EMS,  DIZZINESS]\n",
       "                      ...              \n",
       "99487    [Traffic,  VEHICLE ACCIDENT -]\n",
       "99488    [Traffic,  VEHICLE ACCIDENT -]\n",
       "99489               [EMS,  FALL VICTIM]\n",
       "99490           [EMS,  NAUSEA/VOMITING]\n",
       "99491    [Traffic,  VEHICLE ACCIDENT -]\n",
       "Name: title, Length: 99492, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stuff = df['title'].apply(lambda title: title.split(':'))#future reference, can be used to put column value in a list/variable\n",
    "stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])\n",
    "df['Reason']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the most common Reason for a 911 call based off of this new column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts() #EMS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea24912cd0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "sns.countplot(x='Reason', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "** Now let us begin to focus on time information. What is the data type of the objects in the timeStamp column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      " 9   Reason     99492 non-null  object \n",
      "dtypes: float64(3), int64(1), object(6)\n",
      "memory usage: 7.6+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()#object not an int(so most likely a string)\n",
    "df['timeStamp'].iloc[0]\n",
    "type(df['timeStamp'].iloc[0]) #str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You should have seen that these timestamps are still strings. Use [pd.to_datetime](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) to convert the column from strings to DateTime objects. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-12-10 17:40:00')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'].iloc[0])\n",
    "time = df['timeStamp'].iloc[0] #converting to datatime allows you to grab data from the object\n",
    "time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You can now grab specific attributes from a Datetime object by calling them. For example:**\n",
    "\n",
    "    time = df['timeStamp'].iloc[0]\n",
    "    time.hour\n",
    "\n",
    "**You can use Jupyter's tab method to explore the various attributes you can call. Now that the timestamp column are actually DateTime objects, use .apply() to create 3 new columns called Hour, Month, and Day of Week. You will create these columns based off of the timeStamp column, reference the solutions if you get stuck on this step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grabing from datatime object\n",
    "time.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#other attributes of datetime ojects (.year, .month,.hr,.dayofyear)\n",
    "time.dayofweek "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create 3 new columns \n",
    "df['Hour'] = df['timeStamp'].apply(lambda time:time.hour)\n",
    "df['Month'] = df['timeStamp'].apply(lambda time: time.month)\n",
    "df['Day of Week'] = df['timeStamp'].apply(lambda time: time.dayofweek)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Notice how the Day of Week is an integer 0-6. Use the .map() with this dictionary to map the actual string names to the day of the week: **\n",
    "\n",
    "    dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12         Thu  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12         Thu  \n",
       "2                    HAWS AVE  1   Fire    17     12         Thu  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12         Thu  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12         Thu  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day of Week'] = df['Day of Week'].map(dmap)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea0a2b0430>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Day of Week', data=df, hue='Reason', palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea0a3d5c10>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Month', data=df, hue='Reason', palette='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10330ada0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KhpMkaaZrazJURJxLtYxi66L+Y1SHlaXtOJNSkrZpd9bxW4ElmXlvyTCaHbyURpK2abfQ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\nYEO7LC7vVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ef16780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Did you notice something strange about the Plot?**\n",
    "\n",
    "_____\n",
    "\n",
    "** You should have noticed it was missing some Months, let's see if we can maybe fill in this information by plotting the information in another way, possibly a simple line plot that fills in the missing months, in order to do this, we'll need to do some work with pandas... **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a gropuby object called byMonth, where you group the DataFrame by the month column and use the count() method for aggregation. Use the head() method on this returned DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "Month                                                                      \n",
       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "\n",
       "       Reason   Hour  Day of Week  \n",
       "Month                              \n",
       "1       13205  13205        13205  \n",
       "2       11467  11467        11467  \n",
       "3       11101  11101        11101  \n",
       "4       11326  11326        11326  \n",
       "5       11423  11423        11423  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth = df.groupby('Month').count()\n",
    "byMonth.head()\n",
    "#since the other data isnt reliable, we can use the lat for the aggregate, if we have the lat the call took place"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a simple plot off of the dataframe indicating the count of calls per month. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea09c1cd30>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fhdncftlkcsek+F1exBTnB3AOynbXcfk0zbUhkaVAiLDrz8/iyunjeXLtbh5ZvYsbFQzdamt37D/SyJ6DDVTWNbCn00+wvoEDx5oBSIw3FhTmcPtlk8gJxG4QdDg/dzSJ8ca6cgWCRJ4CYQCM8CZdv2Vu3pAOhiONLaf4wD9BsL6BlrZPJ2uKjzOyRg8nJzCcK6ePJyeQQm4ghcK8dCamDZ2H/YYlxnNe9mg2qB9BBoACYQCdKhguOXscd8+fEvVTJra0tVN1qPGkD/uOD//K+gYONZx8HXx0SiK5gRSmZ6Zy9cwJ5Hof+rmBFCamDYvIbGTRqDg/wGNv7+JEcxvDk/RMi0SOAsEHnYPhl2t38/PVu/jqQ9EVDIcamnnjwxrWl9ex+2DoA3/foUba2j/9lp8Yb2Snp5ATSGFWTtonH/Y53k/qsL5PRj+UlBSk8/Cbjncr67lw0li/y5EYpkDw0YjkBP760kncHCXBsOdgA69s28+r26vZUFFPW7tjdEoiBWNHMCc3netnp5DjBUDumBQmpA4jXlNA9lthXgAz2FCuQJDIUiAMAqcKhounjOXbV5ztWzC0tzs27T3Mqm37eXVbzSdTOk4dP4q/ufQsrpw+gfOy0jTvb4SlDU9k2oRUjWskEadAGEQ6guGWeXk8ucafYGhsaWPNzoO8sq2a17ZXU3O0ifg4ozg/nf91zXSuOCdDwzH7oCQ/nedKg7S0tZOovhWJEAXCIJSS9Gkw/HLtbn7+ZudgmEJhXnjn+K073swbH9Swals1qz+upaG5jRFJ8Vw6dRxXTh/P56dmMDolKazvKWempGAMS9fsZuu+I8zWU+8SIQqEQSwlKYEll3S6lPTmLr760JqwBEPFgeOs2lbNqu3VlFbU0e5gQuowbpyTxRXnjGfepDEapXUQKS4InR1uKK9TIEjEmHOu97UGoaKiIldaWup3GQOqobn1k2A4eLz5jIKhvd3xXvBQKAS2VbOj5hgA0yaM4gvTx3Pl9AnMzErV/L2D2GU/eYPJGaN4dHGR36VIFDOzMudct39EOkOIIj2dMdw9fwpF+ScHQ2NLG29/fIBXt1fz6vYaDhxrIiHOuOCsADddkMsV54wfEk/7xoqSggCvbKumvd2pI18iQoEQhToHw1Nr9/Dz1TtZ8PAaPjd5LH996VlUHW7k1W3VvPXxAU60tDEqOeGT/oDLzs4gLUX3/0ej4vwAz5UG2VF7LOom+5HooECIYilJCXzzkrO4aW7uJ8Fwy2PrAchMG8bComyunD6eCwrGkJSgO1OiXceEOevK6xQIEhG9BoKZPQ5cA9Q452Z6bQuBHwLnACXOudJO698L3Aa0AXc55/7gtRcCTwDDgZeBu51zzsySgWVAIXAQ+JpzriJMxzckdA6GV7fXcNbYEczIVH9ArMkNpDA+NZkN5XXcMjfP73IkBp3O18YngKu7tG0BbgRWd240s+nAImCGt82DZtZxq8pDwBJgivfTsc/bgHrn3GTgp8ADZ3wUAoSC4dpZmczMSlMYxCAzozg/wPryOqL1ZhAZ3HoNBOfcaqCuS9t259yH3ax+HfCsc67JOVcO7ABKzGwikOqcW+NCf8nLgOs7bbPUW14BzDd9mol0q6QgwP4jjQTrT/hdisSgcF9YzgIqO/0e9NqyvOWu7Sdt45xrBQ4DY7rbuZktMbNSMyutra0Nc+kig19HP8J6DYctERDuQOjum73rob2nbT7b6Nwjzrki51zRuHHj+liiSPQ6O2MUacMTNa6RRES4AyEI5HT6PRvY57Vnd9N+0jZmlgCk0eUSlYiExMUZRXnpOkOQiAh3ILwILDKzZDMrINR5vN45VwUcNbO5Xv/ArcALnbZZ7C0vAF536jETOaWSggC7Dhyn9miT36VIjOk1EMzsGWANMNXMgmZ2m5ndYGZBYB7wOzP7A4BzbivwHLAN+D1wh3OuzdvVt4BHCXU07wRWeu2PAWPMbAfwHeCesB2dSAwq9voRSnXZSMKs1+cQnHN/foqXfnOK9e8D7uumvRSY2U17I7CwtzpEJGRmZhrDEuNYV17HF8+d6Hc5EkP0+KpIlElKiGNObro6liXsFAgiUag4P8C2qiMcaWzxuxSJIQoEkShUUhDAOSjbXe93KRJDFAgiUej83NEkxBkbdPuphJECQSQKpSQlMDMrTc8jSFgpEESiVElBgE3BwzS2tPW+sshpUCCIRKmS/ADNbe28X3nI71IkRigQRKJUUX46oIHuJHwUCCJRanRKElPHj2K9nkeQMFEgiESxkoIAG3fX09rW7ncpEgMUCCJRrLggwPHmNrZVHfG7FIkBCgSRKFaSrwlzJHwUCCJRbELaMHIDKRrXSMJCgSAS5YrzA2yoqEfTiEh/KRBEolxJQTp1x5vZWXvM71IkyikQRKJcScEYANaXa6A76R8FgkiUyx+TwtiRyawvP+h3KRLlFAgiUc7MKClIZ0OFzhCkfxQIIjGgJD/A3kMnCNY3+F2KRDEFgkgMKC4IPY+g20+lPxQIIjFg2oRURiUnqGNZ+kWBIBID4uOMovx0nSFIvygQRGJEcUGAHTXHOHisye9SJEopEERiRMe4RrrbSPpKgSASI87NTiM5IU6XjaTPFAgiMSI5IZ7ZOaM18qn0mQJBJIaUFATYuu8wx5pa/S5FopACQSSGlBQEaHewcbf6EeTMKRBEYsic3HTi40yXjaRPeg0EM3vczGrMbEuntoCZrTKzj71/0zu9dq+Z7TCzD83sqk7thWa22XvtZ2ZmXnuymf3Ka19nZvnhPUSRoWNEcgIzMlNZr45l6YPTOUN4Ari6S9s9wGvOuSnAa97vmNl0YBEww9vmQTOL97Z5CFgCTPF+OvZ5G1DvnJsM/BR4oK8HIyKh20/fqzxEU2ub36VIlOk1EJxzq4GuXzeuA5Z6y0uB6zu1P+uca3LOlQM7gBIzmwikOufWuNC0Tsu6bNOxrxXA/I6zBxE5c8UFAZpb29kUPOx3KRJl+tqHMN45VwXg/ZvhtWcBlZ3WC3ptWd5y1/aTtnHOtQKHgTHdvamZLTGzUjMrra2t7WPpIrGt2HtATf0IcqbC3anc3Td710N7T9t8ttG5R5xzRc65onHjxvWxRJHYFhiRxJSMkXpATc5YXwOh2rsMhPdvjdceBHI6rZcN7PPas7tpP2kbM0sA0vjsJSoROQPFBQHKKuppa+/2u5VIt/oaCC8Ci73lxcALndoXeXcOFRDqPF7vXVY6amZzvf6BW7ts07GvBcDrXj+DiPRRSX6Ao02tbK864ncpEkUSelvBzJ4BLgPGmlkQ+AFwP/Ccmd0G7AEWAjjntprZc8A2oBW4wznXcavDtwjdsTQcWOn9ADwGPGlmOwidGSwKy5GJDGElnSbMmZmV5nM1Ei16DQTn3J+f4qX5p1j/PuC+btpLgZndtDfiBYqIhEfm6OFkjR7O+vI6/vKiAr/LkSihJ5VFYlRJQYANFXXoCqycLgWCSIwqKQhw4Fgz5QeO+12KRAkFgkiM0vMIcqYUCCIxatK4EYwZkaRxjeS0KRBEYpSZUZwf0ANqctoUCCIxrLggQGXdCaoOn/C7FIkCCgSRGFaifgQ5AwoEkRh2zsRRjExO0GUjOS0KBJEYlhAfx5y8dJ0hyGlRIIjEuJL8dD6qPkb98Wa/S5FBToEgEuNKCkLTi5Turve5EhnsFAgiMe687DSS4uNYX37Q71JkkFMgiMS4YYnxzMpJY32FzhCkZwoEkSGgpCDA1r2HOd7U6ncpMogpEESGgOL8AK3tjnf3HPK7FBnEFAgiQ0BhXjpxhsY1kh4pEESGgFHDEpmemcoGPY8gPVAgiAwRxfkBNu6pp7m13e9SZJBSIIgMESX5AZpa29m897DfpcggpUAQGSKKC0ID3WlcIzkVBYLIEDF2ZDJnjRuhcY3klBQIIkNIiTdhTlu787sUGYQUCCJDSElBgKONrXy4/6jfpcggpEAQGUKK89WPIKemQBAZQrLThzMxbZgeUJNuKRBEhhAzo6QgwPryOpxTP4KcTIEgMsQU5weoPdrE7oMNfpcig4wCQWSIucB7HkGXjaQrBYLIEDM5YyTpKYmsKA2yveqI3+XIINKvQDCzu81si5ltNbNve20BM1tlZh97/6Z3Wv9eM9thZh+a2VWd2gvNbLP32s/MzPpTl4icmpmx5JJJvFtZzxf/8y2u+a+3eOJP5ZpzWbC+diyZ2UzgWaAEaAZ+D3wL+CZQ55y738zuAdKdc98zs+nAM976mcCrwNnOuTYzWw/cDawFXgZ+5pxb2dP7FxUVudLS0j7VLiJQf7yZF97by/KyIFv3HSEpPo4rpmewsDCHi6eMJSFeFxBikZmVOeeKunstoR/7PQdY65xr8N7kTeAG4DrgMm+dpcAfge957c8655qAcjPbAZSYWQWQ6pxb4+1nGXA90GMgiEj/pI9I4i8uKuAvLipg274jLC+r5IX39vHy5v1kjErmxjnZLCjMZnLGSL9LlQHSn0DYAtxnZmOAE8CXgFJgvHOuCsA5V2VmGd76WYTOADoEvbYWb7lr+2eY2RJgCUBubm4/SheRzqZnpvKDzBnc+8VzeP2DGlaUVfKLt3bx8Js7OT93NAsLc7hm1kRShyX6XapEUJ8DwTm33cweAFYBx4D3gZ4mbO2uX8D10N7dez4CPAKhS0ZnVLCI9CopIY6rZ07g6pkTqDnayG/f3cvy0iD/9JvN/OilrVw9YwILi3KYd9YY4uLU1Rdr+nOGgHPuMeAxADP7V0Lf7qvNbKJ3djARqPFWDwI5nTbPBvZ57dndtIuIjzJGDWPJJZP45sVnsSl4mOVllbz43j5++94+skYP56tzslhQmEPumBS/S5Uw6XOnMoCZZTjnaswsF3gFmAf8E3CwU6dywDn3XTObATzNp53KrwFTvE7lDcCdwDpCncr/5Zx7uaf3VqeyyMBrbGnjlW3VLC+t5O0dB3Au9FzDwqIcvnTuBFKS+vUdUwZAT53K/Q2Et4AxhPoBvuOce83rU3gOyAX2AAudc3Xe+t8HvkHo0tK3O+4kMrMi4AlgOKHO5DtdL4UpEET8te/QCX7z7l6Wl1ZScbCBEUnxfPm8iSwozKE4Px3dPT44RSwQ/KRAEBkcnHOU7q5neWklv9tUxfHmNvLHpLCgMJsb52STOXq43yVKJwoEERkQDc2tvLx5P8tLK1lXXocZfG7yWBYW5fCF6eMZlhjvd4lDngJBRAbcnoMNrNgY5NdlQfYeOkHqsAS+MiuThUU5zMpO0yUlnygQRMQ37e2ONbsOsry0kpVb9tPU2s6UjJEsLMrm+vOzyBg1zO8ShxQFgogMCkcaW/jdpiqWl1aycc8h4uOMy84ex8KibC6fNp6kBA2XEWkKBBEZdHbUHGNFWZDnNwapOdpEYEQS183OZGFhDtMzU/0uL2YpEERk0Gpta+etjw+woizIqm3VNLe1MyMzlYWF2Vw3O4v0EUl+lxhTFAgiEhXqjzfz4vv7WF5WyZa9R0iMN644ZzwLi7K5ZMo4jcAaBgoEEYk626uOsKIsyG/f3cvB481kjErmhjlZLCzM0Qis/aBAEJGo1dzazhsf1rC8NMgbH9bQ1u44P3c0Cwqz+cqsTI3AeoYUCCISE2qPNoVGYC2r5KPqYyR7o7MuLMzhwkkagfV0KBBEJKY459i89zDLS4O88N5ejjS2kpk2jK8Whib1yRszwu8SBy0FgojErMaWNlZtq2Z5WZC3Pq7FOSgpCLCwMJsvnTuREckagbUzBYKIDAlVh0/w/Ma9rCgLUn7gOClJ8Xz53IksLNIIrB0UCCIypDjnKNtdz/LSIC9t2sfx5jbyxqSwYE42Xy0c2iOwKhBEZMhqaG5l5eb9rCgLsmbXwU9GYF1QmM1VMyYMuRFYFQgiIkBlXQMryoKs8EZgHdUxAmthNrNzRg+JS0oKBBGRTtrbHWt3HWR5WZCVW6pobAmNwLqgMJsb5sT2CKwKBBGRUzjS2MLLm6pYXhakbHf9JyOwLijMZv45sTcCqwJBROQ07Kz9dATW6iNNpKckct3sLBYWZTMjM83v8sJCgSAicgba2h2rP65lRemnI7BOn5jKwqLQCKyBKB6BVYEgItJHhxq8EVhLg2zee2IuitMAAAhjSURBVJjEeGP+tNAIrJeeHX0jsCoQRETC4IP9R1he+ukIrONGJXPj+aFLSpMzRvld3mlRIIiIhFFLWztvfFDD8rIgb3xQQ2u7Y1bOaBZ6I7CmDR+8I7AqEEREIuTAMW8E1tIgH1YfJTkhjqtmTGBhUTYXThpL/CAbgVWBICISYT2NwPrVOdnkjx0cI7AqEEREBlBjSxuvbq9meWloBNZ2ByX5ARYUZfNln0dgVSCIiPhk/+FGfr0xyK/LguzyRmD90rkTWVCYzQUFgQEfLkOBICLiM+ccG/d0jMBaxbGmVnIDKSwoDI3AmjVAI7AqEEREBpGG5lZ+v2U/y0s/HYH1okljWVgU+RFYIxYIZvZ3wF8BDtgM/CWQAvwKyAcqgD9zztV7698L3Aa0AXc55/7gtRcCTwDDgZeBu10vhSkQRCQWVNY18OuNoRFYg/UnGJWcwDWzMllYlM35ERiBNSKBYGZZwNvAdOfcCTN7jtCH+XSgzjl3v5ndA6Q7575nZtOBZ4ASIBN4FTjbOddmZuuBu4G13j5+5pxb2dP7KxBEJJa0tzvWlh9kRWmQl70RWCd7I7DeeH4WGanhGYG1p0Do7zPXCcBwM0sgdGawD7gOWOq9vhS43lu+DnjWOdfknCsHdgAlZjYRSHXOrfHOCpZ12kZEZEiIizMunDSW//jabDZ8/wruv/Fc0oYncv/KD5h3/+t844kNvLy5iqbWtojV0Od7n5xze83s34A9wAngFefcK2Y23jlX5a1TZWYZ3iZZhM4AOgS9thZvuWv7Z5jZEmAJQG5ubl9LFxEZ1EYNS2RRSS6LSnLZ9ckIrHu5/YONpKck8sNrZ3Dd7G4/Jvulz2cIZpZO6Ft/AaFLQCPM7OaeNummzfXQ/tlG5x5xzhU554rGjRt3piWLiESds8aN5LtXT+NP91zOE39ZzEWTx0bsjqT+PB1xBVDunKsFMLPngQuBajOb6J0dTARqvPWDQE6n7bMJXWIKestd20VExBMfZ1w2NYPLpmb0vnIf9acPYQ8w18xSLNQNPh/YDrwILPbWWQy84C2/CCwys2QzKwCmAOu9y0tHzWyut59bO20jIiIDpD99COvMbAWwEWgF3gUeAUYCz5nZbYRCY6G3/lbvTqRt3vp3OOc6eke+xae3na70fkREZADpwTQRkSEkkrediohIjFAgiIgIoEAQERGPAkFERAAFgoiIeKL2LiMzqwV2+13HaRoLHPC7iAiJ5WOD2D4+HVv06s/x5Tnnuh3qIWoDIZqYWempbvOKdrF8bBDbx6dji16ROj5dMhIREUCBICIiHgXCwHjE7wIiKJaPDWL7+HRs0Ssix6c+BBERAXSGICIiHgWCiIgACoSIMrMcM3vDzLab2VYzu9vvmsLNzOLN7F0ze8nvWsLJzEab2Qoz+8D7/2+e3zWFi5n9nff3uMXMnjGz8Mze7hMze9zMasxsS6e2gJmtMrOPvX/T/ayxr05xbD/x/i43mdlvzGx0uN5PgRBZrcDfO+fOAeYCd5jZdJ9rCre7CU2MFGv+E/i9c24aMIsYOUYzywLuAoqcczOBeGCRv1X12xPA1V3a7gFec85NAV7zfo9GT/DZY1sFzHTOnQd8BNwbrjdTIESQc67KObfRWz5K6EMl/DNj+8TMsoEvA4/6XUs4mVkqcAnwGIBzrtk5d8jfqsIqARhuZglAClE+Za1zbjVQ16X5OmCpt7wUuH5AiwqT7o7NOfeKc67V+3UtJ09B3C8KhAFiZvnA+cA6fysJq/8LfBdo97uQMDsLqAX+x7sc9qiZjfC7qHBwzu0F/o3QbIZVwGHn3Cv+VhUR473pefH+jdxExP76BmGcYVKBMADMbCTwa+DbzrkjftcTDmZ2DVDjnCvzu5YISADmAA85584HjhO9lxxO4l1Lvw4oADKBEWZ2s79VSV+Y2fcJXZZ+Klz7VCBEmJklEgqDp5xzz/tdTxhdBFxrZhXAs8DlZvZLf0sKmyAQdM51nM2tIBQQseAKoNw5V+ucawGeBy70uaZIqDaziQDevzU+1xNWZrYYuAa4yYXxYTIFQgSZmRG6Dr3dOfcfftcTTs65e51z2c65fEKdkq8752Lim6Zzbj9QaWZTvab5wDYfSwqnPcBcM0vx/j7nEyMd5l28CCz2lhcDL/hYS1iZ2dXA94BrnXMN4dy3AiGyLgJuIfTt+T3v50t+FyWn5U7gKTPbBMwG/tXnesLCO+tZAWwENhP6DIjqYR7M7BlgDTDVzIJmdhtwP3ClmX0MXOn9HnVOcWz/DYwCVnmfKQ+H7f00dIWIiIDOEERExKNAEBERQIEgIiIeBYKIiAAKBBER8SgQRE7BzJyZPdnp9wQzq+3ryK7eCKq3d/r9slgbJVaimwJB5NSOAzPNbLj3+5XA3n7sbzRwe69rifhEgSDSs5WERnQF+HPgmY4XvDH3f+uNS7/WzM7z2n/ojWP/RzPbZWZ3eZvcD0zyHib6idc2stO8C095Tw+L+EKBINKzZ4FF3iQy53HyaLX/G3jXG5f+n4BlnV6bBlwFlAA/8Ma0ugfY6Zyb7Zz7R2+984FvA9MJjbJ6USQPRqQnCgSRHjjnNgH5hM4OXu7y8ueAJ731XgfGmFma99rvnHNNzrkDhAZWG3+Kt1jvnAs659qB97z3EvFFgt8FiESBFwnNIXAZMKZTe3eXdzrGgmnq1NbGqf9bO931RCJOZwgivXsc+JFzbnOX9tXATRC6Ywg40Mt8F0cJDUomMijp24hIL5xzQUJzLHf1Q0Kzqm0CGvh0uOVT7eegmf3JmzB9JfC7cNcq0h8a7VRERABdMhIREY8CQUREAAWCiIh4FAgiIgIoEERExKNAEBERQIEgIiKe/w+cQf5gE+lrpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth['lat'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now see if you can use seaborn's lmplot() to create a linear fit on the number of calls per month. Keep in mind you may need to reset the index to a column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth.reset_index()#reseting index, making month a column of its own inorder to plot on x axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fea09d73e20>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Month', y='twp', data=byMonth.reset_index()) # reset index "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a new column called 'Date' that contains the date from the timeStamp column. You'll need to use apply along with the .date() method. ** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grabing value from object(datetime conversion)\n",
    "t=df['timeStamp'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = df['timeStamp'].apply(lambda t:t.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    </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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.date()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now groupby this Date column with the count() aggregate and create a plot of counts of 911 calls.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    </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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Date').count().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea0b1a6340>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Date').count()['lat'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>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",
       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\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",
       "<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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Date').count()"
   ]
  }
 ],
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