911 Calls Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Set Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Import visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 9 columns):\n",
      "lat          99492 non-null float64\n",
      "lng          99492 non-null float64\n",
      "desc         99492 non-null object\n",
      "zip          86637 non-null float64\n",
      "title        99492 non-null object\n",
      "timeStamp    99492 non-null object\n",
      "twp          99449 non-null object\n",
      "addr         98973 non-null object\n",
      "e            99492 non-null int64\n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Read data\n",
    "df = pd.read_csv('911.csv')\n",
    "\n",
    "# Check dataframe info\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check head\n",
    "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": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 townships(twp) for 911 calls?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** How many unique title codes are there?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating New Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** In the title column there are 'Reasons/Departments' specified before the title code.  These are EMS, Fire, and Traffic.   \n",
    "Use .apply() with a custom lambda expression to create a new column called 'Reason' that contains this string value.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMS: BACK PAINS/INJURY'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select example\n",
    "x = df['title'].iloc[0]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMS'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.split(':')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     EMS\n",
       "1     EMS\n",
       "2    Fire\n",
       "3     EMS\n",
       "4     EMS\n",
       "Name: Reason, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create reason column\n",
    "df['Reason']=df['title'].apply(lambda title : title.split(':')[0])\n",
    "df['Reason'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the most common reason for a 911 call based off this new column?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Use seaborn to create a countplot of 911 calls by Reason**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xded8eb8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qYnBIkooYHJKkIgaHJKmIwSFJKmJwSJKKGBySpCIGhySpiMEhSSpicEiSihgckqQiBock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\nyP8DG4tbfPbI7bEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xde724e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Reason',data = df, palette = 'viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the data type of the objects in the timeStamp column?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
      "lat          99492 non-null float64\n",
      "lng          99492 non-null float64\n",
      "desc         99492 non-null object\n",
      "zip          86637 non-null float64\n",
      "title        99492 non-null object\n",
      "timeStamp    99492 non-null object\n",
      "twp          99449 non-null object\n",
      "addr         98973 non-null object\n",
      "e            99492 non-null int64\n",
      "Reason       99492 non-null object\n",
      "dtypes: float64(3), int64(1), object(6)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Convert timeStamp from strings to DateTime object**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslib.Timestamp"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timeStamp']=pd.to_datetime(df['timeStamp'])\n",
    "type(df['timeStamp'].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now that the timestamp column are actually DateTime objects, use .apply() to create 3 new columns called Hour, Month, and Day of Week.  \n",
    "Create these columns based off of the timeStamp column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-12-10 17:40:00')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = df['timeStamp'].iloc[0]\n",
    "time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    6517\n",
       "16    6490\n",
       "15    6154\n",
       "12    6029\n",
       "14    5997\n",
       "Name: Hour, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create hour column\n",
    "df['Hour'] = df['timeStamp'].apply(lambda time : time.hour)\n",
    "df['Hour'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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: Month, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create month column\n",
    "df['Month'] = df['timeStamp'].apply(lambda time : time.month)\n",
    "df['Month'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    15150\n",
       "2    14879\n",
       "4    14833\n",
       "0    14680\n",
       "3    14478\n",
       "5    13336\n",
       "6    12136\n",
       "Name: Day of Week, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create day of week\n",
    "df['Day of Week'] = df['timeStamp'].apply(lambda time : time.dayofweek)\n",
    "df['Day of Week'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm columns were added to dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Notice how the Day of Week is an integer 0-6. Use the .map() with a dictionary to map the actual string names to the day of the week**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Mon', 1: 'Tue', 2: 'Wed', 3: 'Thu', 4: 'Fri', 5: 'Sat', 6: 'Sun'}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dictionary\n",
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}\n",
    "dmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Map string names\n",
    "df['Day of Week'] = df['Day of Week'].map(dmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tue    15150\n",
       "Wed    14879\n",
       "Fri    14833\n",
       "Mon    14680\n",
       "Thu    14478\n",
       "Sat    13336\n",
       "Sun    12136\n",
       "Name: Day of Week, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day of Week'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 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": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10425fd0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RsRp4C/D+iFiSmauBE4HbgbuBayJiJjADOIRqUNRa4KR6/4nAnaNpX5KkiaDZ0cU7qUYF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102d7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create count plot\n",
    "sns.countplot(x = 'Day of Week', data = df, hue = 'Reason',palette= 'viridis')\n",
    "\n",
    "# Show edge lines\n",
    "plt.rcParams[\"patch.force_edgecolor\"] = True\n",
    "\n",
    "# Relocate legend\n",
    "plt.legend(bbox_to_anchor =(1.05,1), loc = 2, borderaxespad= 0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use seaborn to create a countplot of the Month column with the hue based off the Reason column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x100eb320>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8208d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create count plot\n",
    "sns.countplot(x = 'Month', data = df, hue = 'Reason', palette = 'viridis')\n",
    "\n",
    "# Relocate legend\n",
    "plt.legend(bbox_to_anchor = (1.05,1), loc = 2, borderaxespad = 0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Do you notice something strange about this Plot? **    \n",
    "Plot is missing some months.  May need to plot this information another way, possibly a simple line plot, that fills in the missing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a groupby object called byMonth that groups the DataFrame by month and uses the count() method for aggregation. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create group by object\n",
    "byMonth = df.groupby('Month').count()\n",
    "\n",
    "# View first few rows\n",
    "byMonth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a simple plot off of the dataframe indicating the count of calls per month**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10008f60>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+3hhjgKeAPuB71to3jDFPA7uAt4FXgI1ANLDP//XjQIK19i+MMZ8GbrXWfmWi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101a0908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth['lat'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Use seaborn's lmplot() to create a linear fit on the number of calls per Month.   \n",
    "Keep in mind you may need to reset the index to a column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset index to make month a column\n",
    "byMonth.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x106ff278>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NZkSrqA75bBtJVAZM/vry0/nryxcRzFkp8cPHdvCHNw+QtOFFQyEmw+4GvAVY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10654a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create linear model\n",
    "sns.lmplot(x = 'Month', y = 'twp', data = byMonth.reset_index()) # there was no 'Month' column in byMonth, need to reset the index for this code to work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use apply along with the .date() method to create a new column called 'Date' that contains the date from the timeStamp column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-12-10 17:40:00')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = df['timeStamp'].iloc[0]\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2015, 12, 10)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] = df['timeStamp'].apply(lambda t : t.date())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Grouby the Date column with the count() aggregate and create a plot of counts of 911 calls**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.tight_layout>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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Wp4wpPvaHj7rPQDgU4oCzBnb5noF6pF1A1IxlWXz6T5/mg//9O/z6Hz0WGCkj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\nP9qAmb9Zod0BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x154c21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all instances per date\n",
    "df.groupby('Date').count()['lat'].plot()\n",
    "plt.tight_layout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Recreate plot above but create 3 separate plots representing reason for the 911 Call**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nq2Uq2ACgsMjVXFMIaXRiw1Aa+XQMR04vuJ6vNzWcna1ibDAFAJhZ6M6dn7XU\nPaLKxw7xRSOIRmREZKmjsXXl4KLrPAflqYNaDx5UWB5K3JeoskwDVbIm3L1Hgg1Uo6mZ3WBGTC/i\n3mdOAQBqnu+enjcXYSOFJAoZc46Y73MDRechk4ohl44tT2Zu5YQHsuZ8NF9uYMZauB70GPvV5MkD\nk7jb4w13y5oyUGJ/u1QiahuoIEWZqOIDzJCZphvLWsk3WzoWKk0YBtqMguu7qw3bc6PK77kub5aG\nYBA6IUkShvJJzJXqLqHEOauW5eLNBciSZN+8naD6CPHGpgmXckmJLnJ5tFlhJCIjKq/vHBQdn/WS\ngwLCi8BPToke1PIiEnQPP3d0JlDoQ5t3bhrJAgBOU/6r4TZQU7aBSiGViCAeldeBgbLaniVjyKfj\ny1bxxaKSHf48N1uxDeBqG6g7HzyKT39jDwzDwDfuO4z/+K8XlhRmXFMGSkyWZlOmgXrxTBHv+dS9\neEKdbHs9ncxsypw4nBqepU8gomcRdFJrjRaqdQ0DlmEiD2q+y9qDJqn4OogkiIFsAi3NcK16z0yb\nk8iGoTQGc/GOghLC9qCEQj5bxWeFLBPxLgyUEBYkD6qXE/fJyRJu+/Zz9gaWK0G9qWH/kZk2kcRa\nKUo2DAN33HMIz3aQZhMuD6oa4kEJ+cnlhviKloFaKDdcoUORRktHLBrBxGjG9XzNYxzJQA0XkpAk\ncyLu9p7rNc8emsId9xxqM9KUC0wno8hl4qg3tCXXcjZbOmIR2V4wHxUEJ2JIfzV4YM9pPK5OYq7U\nwNnZCgwsbYfwtWWghALWbCqOUrWFw6fmYQDYc7j9JixWGsimYnaXBcrpNJeRh5oVJu5DJ/09KGoj\nQqHIguBSdwP9zm5CfAAcl124GakbwOhACsP5JOaK9a48GBq76O21NCefBJAaMvyzvDJ7APj47U/i\nyz880NVvWmk+8vlH8dBzZ/HkgfaFzFL50ZMn8Cd/dz8OWQsV2j25lzLzRlPDx/7lcXzvkWMoVZv4\n7sMv4vuPdhc+qTeccQfloOpNDZOzVYwOmHkMrxezGAzDQEnwCPYe8RfyNFsa4jEZI4UkEnEnquDn\nQSXiEbvb/0A2gYVye7sjwzDwbz98AR/53COrpurtxN2PHcd3H36xLW1hh/iSUXuh220kxktL0xGN\nysilYpAkuGrYphdqrrltOTRbeqix0XTdXkzsOTxtzxVL2Z1hbRkouz4ogmwqhpam47Q1ER/1kZ9S\nHz6CPJKUMNKEAAAgAElEQVT6MiaQmaLjiRw6OQ/dJyxBJ3rQWqkMWPHwbvtfOa2Oums46hdCPDMt\nGCir7mF6voanXpgM7E/W0nS7Un3eNwdleVAxuWuRRER26qA03cDTL3S3ml9JRIPRjTKyW+gmoxvL\n2T25d5Peo/vP4dCpBXzlxwdRtULf3S6MuslBnZk2V7u7NpmbEVQ9rytVm9h31J1POnB8zteTqdY1\naLqBHRtzkCUJD+w503Y/6bqBlmYgHpUhSxK2jGVd7xeZXqhhxPKeAHNhqBtG26T/Hz86iB88fhwn\np8o4LuTTlstjz5/D5+7ct6RQNp0j7+KmIoT4aIPBpbYpalp7usmyhHw6DjrSG4fN3N6hFQrz/dNd\n+/GRzz8SeBxmFup2CuBx9Zz9/FL2t1tTBqpJHlRMtsN2lLA9OVV2ub7Uh4/yQIAz4S+n3REZn2wq\nhkq9ZcfDRSgM6PWgFrqsO3CaxXZ3+OnzxQuXQnyjAylbtfOVHx/E//vaHjx90N9IzJca9kUrrjyb\nHg8qEYug0dBCi4PpAoxGJNdqar7cOK9FxX7sf9GZMFdyxSzeUFFr52AAaPYwxPfwPrN4PZOM2iKC\nbie0RrOziu+0dV3t2JiHhPYQ31d+dBCf/PenccZaOM6V6vjEl5/CHfceavusUtWclCdGMnjVFeM4\nNVXGUwfc1yZN2BQmvnjrIGRJwlA+gVq9ZV9LlVoT1XoLw9a1DjihdTEsf3KqjLsfO2579RSuLNea\n+OBnH8I9T58MPD6duPvRY3hw7xk89Fx4AwE/qIeg9/qkUGs6GbVTBkv1dGhXbMA5NgDw8kvHAABH\nz6xM5/Lj50qYLdYDjf85oSZz/1FHfdxNBxMva8pAiTko2vacDoKmGzguxMbFPnxEIrr8HNSspYZ7\n2SWjAIAjp9rDfHMeD6qwSA+qsQgVH+B4aOJK+exMBamEGe6gwjzyXqYCBBPijawbhi0yIRfc8aAi\nMBAeynJCfDKULQP2+5ot3Vf5deDYLD7x5SdtVdFKIuYnV9JAiYY3Fu19rm1qrmrf8PlM3N6ss1Jv\ndfW7a43OOSgq0J0YySCZiKIieDGGYeA5y3ui8NHZmQp0w8DUXPt5pfxTLhXHm67bBgnAnQ8ddS1g\n6FiS5/vbP3c5/uJ3rsXm0SwMOOdzSlDwERRZENWzLxw3d/mh3pUnrbzXgWNzODtbxfM+5RrdoOk6\njllz0XcfenFRXdRbmm57ed57qlx3PCgyUEvto0ceFADks868SPenGB0SMQxjUQIGisIE5ejFpgKa\ncJzoGMyX6vjfX3qyq9rNNWWgmsI2FOQZiaEIMcznVfABTohvJXJQdFJnfU6qUzBofvdiFUXNRaj4\nxO+hC9cwDJyZLmO0kIIkSXZhnr2LZIBUlcYdsVR3ZFCbHo+um24SdogvIuGll4zis//z1XjVFeOu\nzxX52o9fwPPH5pa0+gzDMAyXx9hND0Evjz9/Dn/wt/fZdTaEuOITPajzLTM/MVnCv/uonh7Zf9Y+\nx7WG5hKEdCMW6EbFRx7UxuEMUomIy4M6N1u1ryGahGi17CePpvxTNh3DxuEMrr54BC+eKeKsMIGR\nV0eLtUwqhq3jOSStXBSF+UQFH+F4UM5306R54+4JSDDFMwBw2FLkdtP+Z++RaXz9vsMuQ3pqqoJm\nS4ckAWdnq3js+XMhn+B8zrd+csRlQL2L58nZKiTJPEYU4gvyoLxRJC+tllCbKSzcd0yY3vBsQCnK\n7XcfwJ/8w0P2uQ9D1w174REUMpy0zq+3bTQtip46OAX1+Bz+4T+fw7kOewKuKQNFnkUiJiMrhO4o\ncfqi4KJ6+/ABzgTbzV5GQcwU64hGJGyyFEV+ld1kKMiDWqyiyL4pu1Tx2TUf1o1YrJjbJowMmDfr\nkBD2MMfsb6Bo3PTbqBWK3UnCCot0U6yrkQdlvScWjQSqGSu1Fh7bdxYA8MzB7jpedMtsse7KRS7F\ngzpyegHlWqtNFCMujqIReUVEON1wxz2HcPdjx9vGQ7VuEVlCrdFyeUTdeO+NpoaILEGWpMAc1Olp\n0zMfyMaRSrg7pIjF4metiYU8p3mf76cVM93L2zaYm9KJCwFv/pMgxSQZ4WlBwUc49T6isGkeqUQU\n2zfmMDqQwonJMgzDwGErEtKptvLEZAmf+o9ncOeDR12G4qhl4N748q2IyJKZAwxROFZqLfzjt/bh\nmz85giPCwlpsJDBXquPQqQVcsnkAiVjE/j1+HtQT6jl85HOP4C//+fE2Q6LpOjRdh24YQojPURgn\n41Hks/FAw7fn8DRmi3V88t+fxlSHlmmlmlmCAwR7UHRt7Nzk3lSdFnwUlarWW7jt2/tCv29tGaim\n41lkBMNDJ/CIsJsm5Qey6ZXOQdUwmEs4k61PXmm2VIcsSa7wYpCiSOTwqQV84bv77VVntyG+fCYG\nCc7ET333SGk13KWBIon59g3mnjuOB+XNQTntjqbmq/jCd/a3rbid7Uac30A5Oe9k9dQLk/Z3HDo1\nv6xqeS9Us3PRRvM3LcVA1ewwkseDcuWgTLViKhHB7HncDK5UbeI5q6jVO1HRdZ3PxFGra64Jspuw\nUK2pIRGLIJ2M+uYDNF3H2ZkKNg5nIEmSaaAaTh7o+WOOgSIPatI6ZpV6qy18VfQsIuk6FWv2nHC3\nO5qQsgQp7R6UmINyX28LlQbOzlaxcyIPWTIXmaVqE3OlBo6eIQ8qpOVSS8fff8PZZ9UVvbEWxy+/\nbAxvum4bZot1fP3ew4GfddcjjmLv4Elnc3HRg3rKUpy+TDHTCYVsHBLac4qVWhO3330AsiTh1FQZ\nf3X7k/a5n5qv4r3/5z78+Ekztxb1eFC0eB3KJTBTrLflh+sNDVPzNfO6LtZx+w/CVbjidiAzC3Xf\nkP3kXBXJeASK1QWHDC95UIdPLyAek7FtQw6HTi2E5qx7bqB0w8Dn79yHT39jj1MfFHV7UMOFJCZG\n0jg7U7F/DJ2gtNBc1q6DClnh/sv3VXzuTn+r3dJ0zJcaGMwlkU2bRsFvsp8rNlDImlt8EIWMqSh6\ncO8ZPHuo3Ut4+Lkz+PiXnsBPnj2Nk5NlSHAupk5EZBm5TNxW8TkGyvSgEvEIhvIJDOcTiEbkwGK/\nuaL5/u0bzZUsTWreOijRg7rvmVP4yZ7TbXVotkhC2P/JUTO6b7BHLO/pxt0bYRjA5+7cjw9+9iFX\nMtXLzEIN33noaMfFBuVCdkxYBmoJIb6G9Z5JIY+iG4YrlxaNyJAkCRuGMjg7U1lyb7MXTszhv544\nEXhTPv78OfvYer0iMr6FjKnQEhcC3Qgl6g3NlGmnYr4e1LnZKjTdsFVf6UQUhmGGEw3DwPMvzmIg\nG8dgLmGHZsQmxd7Jv+RZRNoGakE0UFZ42RNNSFqSfpKaU6idwmCAIB6y7gsKOZECcdOoqQh8XD1n\nG7pStRl47I+dK7o2cxQXZUfPLCAiS9g8msWbrtuOjcNp/OjJE76bPxYrDVfnBNHTEOemJywDdY2V\n747IMvKZeNt5/9YDRzFfbuCWG3bg5ldtRanatEOMB0/Mo9HU7e+gKAgdm6F8wjpuSVcujDg9Y94/\n112xAZduHcCzh6ax/2hw1w9adFAI9pAnR28YBs7NVTE2kMKmETNSQ+ejbAldTk2WsX1D3p7jw1SR\nPTdQdz38Ih7YewaPq5NChwW3gTI9GrNYlSYNktiS9BdwPJIgkcTkXBX3PHUSj+w76ysfXyibKreh\nXMI0CukY5j2TvW4YmCvVXTcK4FwQX/jufnzuzn1tN8HX7j2EiCzbNRyQ0HFDRpGBbNxU4RmGfZGI\nceY/fNtu/I9fuRr5TCxwlUiGY9u4aaDmPR6UNwfVaGp2Dy+vMXE6SQhGOtueE9B1A88fm8WOiTxe\nd+0WAGZI4exs1VeAQt976x3P4mv3HsYPnzgRclRWxoOq+3hQpoLMeQ0Z74nhNDTdCOwuH4RhGPjc\nnfvwV7c/iS/94ADUY3O+r3vYMuZAu6Gn0LWt9hL+3m2ILxEzhTWVWvtETYrViWFzYqEwW7Xewlyp\ngYVKEzs3FTA+mMLMQh3NluYy6t4cLKn46F6m8JzoQTUDagLtEJ91v8+XG5AkuDYnzKfjkCRgwToO\nNEnv3GxOiJutUPZ9VuskwAxn1wIWMRSuHLMWfiVrxd/SdBw/V8bm0SxiUTPU++brd8AA8F9Ptl+f\n52araLZ07LCuSTE1QXNTudaEemwOOzbmXCH6gVwCs56uMUdPL0ACcPMrt+K1L90MCcBPnj0NwBG1\n0EKa7mG6RsjjDMpvkYhk00gGv/zaXQCAr/oUFBOUa7xixxAA4Pg5tzJwvtxAo6ljbDCFq3YO45pL\nRvGGV2y1jqfZeMGAeb/a83VITrenBurkVBlfv89xkynsEI9FXAZqKJcQ4rPmAaLYNK20AEd0EPSD\nH9hjnlRNN3x3rqQYP53MfCbeJh0vVZvQdMO+AIiC8G8KKxCGYWCu1MDESAY37J6wnvMdYiAD2QTq\nTQ21hhPaEbcm2TyWxcbhDHLpuGlovfUmhoHp+RqyqZh90Xo9qKjQSQIwJ266AM96JmTfEF+W1IbO\nMZst1tHSDGwZz2HTSAa7dw5j3Oof6E2szyzUcPvdKj71lWdsxdTdjx4L9aJOTZURkSVssYzuUvKP\nNY9SDHDCO+NWl2lS8G20VoV+5QdhzBbreHDvGfsYi90aiHuePokDx+fs/opBIT7Kt4le02JCfJlk\nDC3NsCdLwzBw18Mv4q5HXjR/o9dANTTbm8in4xgbTMGA2VRWjDB4DZSz24B57w7mEpAATAvJ+kZQ\nDsoT4lsom63FxB2bZVlCLu1EFmgxtd3KdVHOy56ELYNVDMhD0QJlq/U++s1nre7uW8edGq1rlFEU\nsnE8sOd0Wy6KvD4ah9ijkqJEp6bK0HQDytZB13sHswk0W7rLwy1Wm8ikYohGZAwXkrh8xxAOnpzH\n6emynY+iY08LqV2bC/jV1+7C661FITXgnvEYKIpATIxksH1DHpdvH8TRM8XA/Bqd063W/eaNMNHC\nbXQwhXQyhve+9SXYtamAWFRGuda0xSoXTeSFXdDXqIE6PVV2TdTiKsDrQdnt9a0bkdqgpOJiiC94\n23ddN/ATy0AB7SfKfM6coOz6pkwc1bq79YhXYk7QBUCrgpOudjFmwWIuHcMNVy1t63ax5oNuANFA\nEfl0HI2W7vIk6g0Nf/e1PTg3V8W28azVfUNyclBkoKyx0yr16OmiPZl41TZekQTg5MpE40whoA1W\nXuP9b9+N37n5UgDtiqp7nz6FHz1pTtI7J/J4w8u3YKHSdK2ARQzDwKmpMjYMpe1Q71I8KArxTc/X\n7BwiLZYusSYQMiwU/upG8SRCnt5VO4cBOCtfwCwP+MdvPYd//Z6KXDqG977lJQDaw3b1poZ4VEba\n8sLdHlS4gdIN0yCZIb6o6zeenCzjq/ccwqGTC4hGZHtiT9lKupY9YaaTUXsHXMqVkce9IBSB7z08\njVK1CUmCPd5oRMZALuH2oAIUrSlPiG+h0kA+477nAGAgE7cn58m5KjLJKDJJc+4YH0zjD99+FW66\negI3XT2BK7abq/6gCAN5g9ssQ0S/mXKReSFiEY3IeM3Vm1Cta/jxE+5OHjQ3bRhKtxleWkCRwtG7\nXT3NPeL59DYkoDnkwb1n7IUSHXu6h2VJwhtesdX2zoI8KDJQFA6lcxvUfJq+h7xTb76ZDBR9DpFJ\nRlGutuyoyY6NeTvvGCY66qmBosmEIl10ocWtnV1psh/KJ50aAes1ziTtXNjxkByUemwWMwt1+zP9\nknv0/WR8/PaccYp03RfWtcoYfu11F+M3Xn8JAPeWBcUqSeJNue2Nuyfwums3+x+UAMhDmy81fD0o\nIp+xtiEQJv8fPXkCTx+cwuXbB/F7b74CkiRhdCCFMzNl6Ibh2rAQcNx3Mbx2brbq8spatN2GsKKl\nsKh4c5GBIk/EHKN57LwTBXUN+fN3XIMP/uY1+LnrtiEiS3hk/1n4MVuso9bQsHEkg2jEVKctJQdF\nHpSmG/bYaXLaMpbFNcooXnbxCAAn/HV6uoK7Hn4xsCjaC63ir7lk1GxDYy1g6g0Nf/f1PXh431kM\n5RP4wK9cjS1jWes4tueg4rGIvSijyUaSOof4KJRGHhTg5Iio0/iv/szF+NR/v96ezMQQX0XI+ZKH\nR+/bYeU06f751oNH8amvPIMjpxeQTcVcudrhfBKzxbqdw2sG7C6dFEJ8jaaGal1DIRODl3zW7F9X\nrbcwNe909yeu2jmC3/rZS/FbP3upfd2J7ZfmS3V84O8fwA8fP45py4OiEDgZ8KD77VqrAPbgcXe4\n1s6PJ6N2npig3+tVOBKDnqJ8uyGBYMh27xxBRJbw7KFpOwdG12ssIK/tGCj3vHdyqox8Jm6Pg3JW\nQb096Z4dG0hZ+W6PgZpzOtyImHnPJl48W0QuHcNQPmHnHddsiI8GNpB1q3HIslJydTArqOrsEJ9P\nDipksz1K5pFixm+FQIaILmT677xwEs7MmBexKHcFzLDY66/dgl1W/Fv0oLyhjt+5+VL8+usuafv+\nMOwQZ7luhz1S8fY6KnunUcGoPnVwCpIEvPuWK+0x7NiYR7Wu4cx0xbVhIWBezFvHsvZNZMqaNZfR\nczwo9yU0kE24VlWk8tow7BionM8YAbM7RiIeMVVYVrsWM8zqfl213sKtdzyLT3/TVFxtGjG9s26a\n3PohhgXve+YUPn77E/aNn8vE8d63vMSOo48MJBGNSHjqhSl89Z5D+Ox/Ptem/vODVqrbNuQwNpDC\nySlT/jxfaUA3DLzisjF84vd/yg6dDGQTPiE+HYmYbCeo6boaKSR9PagH957Gx29/AuVa0zbCiZiM\nPO2lZF3Xz1k98l552ZhrwhQNVFXwoMYG3a1z6Jqn8zRjeUgtzWibgIcLSTOPawl2Oob4Glrbfen6\nPMtDUI/NoaUZbROjCKkJRc/94X1nMVus43F1EpPzNeSFeiSa9IMMFHk1XuGBMzdF7HwWQb/XKyAh\nvPlFuyFB2l12c/HmAo6fK7kKYQHnHvYyaB0nsRaqVm9har6GCeHepOMZVFBPc0A+E0dB8F4Jx4Py\nGKikKcyZmq9hy1gWkiTZi5KwhgC9NVCCbBZoT/RtHEpjbDCFRDzieBBWfoMumqQwScdDclAU8796\nl7kS9quqpu8nY0gyVnGCPHzKvCkpAeplfNB0610eVMXxoJYK3ejlais8xEfeiTXmUrWJQyfnsXNT\nwTVZXGSp3g6fWrALT2NCuO6qXcP248ut0IgY5rNFErJb6FGwcmV0fihssmHI6VSdTkYhS5JrotB1\nA2dnq9gwlHaJR7KpmGsCqDc1/N+vPoOnD07ZtS2kFuqmh6AfdaHg9dsPHsWBE/O2ajHrCcFEZBnj\nQ2n799WbGv71+wc6tnc6NW3mysYGU5gYyaBca2Gh3LCvjaFc0vW7C9l4WzEueVBJz3kfH0y7cpOA\neV7/6bvP48CJeTy6/5x9XBKxiMsbbzQ1qMfnsWUs68qjAo5CtiJ6UMkYNg6nsWtTwb4Gdk6YBoom\nKzHHk/MYKO8KPagvJUVGavWW/bkFnxDfVqt33xNWzzevByWS9TEopDA9emYB0/M1jAyk7BKXSpsH\n5R4jhS5LnlB11c6POx4UnVo6DzQG7/Hx9uPza0gAOFEOL0HKYPLMxNTGi2fo/nFya0O20jIgxFcx\nxSqZVMxePLq2AZqt2qFcEXHuoX6LtmYg5J5dEwbKqTmyPCjLE3r3L16JP//Na9yvIQ+q3oIEuLof\nOyq+9h98/FwJqUTUljz6eVDzbR5UzPU8YN74mWS0bWVEyLKEieEMTk2X7XyGUw/SvgLsFgrLUE+y\niCz59vKzPSjrwt5zeBqGAezeOex6HRmoI6cX2kQSgBlGMJ+TsNsyVq4WJiSSiHgNlPtcTs5VEZEl\nDAvHS5akts3ZphdqaLZ0bBxyx66zqRiqdc0e4/3PnMKBE/O49tIx/MFbX4KbX7kVu61FRyIWWVKI\nT/Sg6F4j4+f1AABHRHDZtkFcsWMIew5P2xOdH2KuLBqRMWEZ1JNTZcG79l9Ji94oqfDEhUkyHmnL\nW2i6gc/8517ougEJ5iRMebZEPOK6Rg6cmENL030nPFeIz5qs04koohEZf/YbL8M7f/5y/OINO3DF\njiFXSYYYuvUa+BGP1DyoL2WySw+KPM6nrDZfYwPpttcQNMnT+M7OVOz6pkZTh6YbGCkkbaUt5Z7E\n8KZIRJaRSkTaQtVifpwMJnkmZJCLASE+p92RU5Tv97ordzj3sxhCDervGYtGkEvHXDkoKp6neiXA\nWUCIC3hx4VMsN5BLmyU2hUzcpawGzPt9dCDpGhMAR70Mx0CtfQ/KDvFRIZd5MuggZ5KxtnAb3YQ1\nq6ZDPBBBhbqNpoYzMxVsGc1gIJtARJYCPahY1AmheD27hUoDU/M1s3VIiER802gGzZZuS7Pt1dIy\nPCgnsd1Cra4hnYz5jiFHOShrzM9YORIyOMSWsSyiERmHTy34VvPv2JjH6EASuzYV7An5rOBBtew6\nKG+Iz91NYnKuimFrO2vXONNx141NzUc3eAyUN4xCK8A3vmILXnrJKN7+ml0uefxiPSjDMNBoam3n\npu5RzIns2lSAJAFvvn47fuuNCuIxGV/+4Quu33PsbBH/9N39aDQ1zCyYuTIyTOTxnZoq2+8JmqjO\nzlZx4PicKXJo6aYHJSzKkvGInTOlSW2hXMfUfA1XXzyCS7YM4MDxOTu/l4hFnNB1qWH39iMBgYgt\nVHB5UOZ1KMsSrrtyA958/Q5EIzKy6Zi9KClVmxgfSuMVl43huis2uD7TKzUPlpk7300h9oKPgdo8\nloUkOUYkzIOyQ3zWtUS5zUs2Ox0PRgopxKJm/ptCfJWQnG8mGbN3DSbE/Dh5UKScpblJbAMlMuAR\nMxQD5o4t41n7uc1jTnQiKAcFmN7Z1HwNP3z8OGqNFh7ccxqxqIwrL3LO/UA2AUlywrRnZir47397\nH754136rf2fTDhF758dStYlyreW7eBcbL2wedRuoNZuDqtshPvOkkKPot2VCLGrWEM0LIgnvBRMP\nSLqdtNSCm8eykGUJA9l4oAdVyMSddv6eEB8pUC4KCO8RdAKoE3uQm74Y0knqTdhEpd6yJwovzurY\nvLDV43MYyMZtiS0RjcjYNp7FicmSfQOKHpQsS/iL33k5/uCXrrLjyaIHRYsJ7zmgyXKmWEet0UKx\n0vTNC+QzpmdExpG2DxFzVYAzcdNK0t7gzWeyoBzUYrqptzTDKk7NIB6TsWk042pB5fUAAOBnrtmE\nT77neihbBzE6kMJbbrgIpWoT37z/iP2aB/eewf3Pnsa+o7O2go8M04RgoJzFi/t7KCTzhe/sw8e/\n9CSOnzWvpYQgkgBMT2Mw706A0wo+k4zhlZeb/RHvt5SQiZjTkoo6LwDmhOfF9qBqWuhxB0zjsVBu\noNnSUa1rGMol8O5brsQ1Vs6XIE+CxDOkIPWu/BOxCCSYkZKFUrAHlYhFXIua0BwULXasa4m85Ftu\nuMh+zYjVnUXstmHnfAMMlFe27qQforhoIo+Nw2m81CrGtXNQtSZkq1uH+/NMD5UW4kHpAVmScNPV\nm7BzIm+LOoDwHRIu3z6Elqbjyz98wbqmirhyx5Arj29uGZ+wQ3wnzpVgGMB9z5zG7d9XUam37GvV\na6DonI4Ntnux5EFFZMm+/mNdNFbocYjP7UERQT3qxAR8raG5VpKAkIPyrKKpIzq5loP5JOZLDVc3\nAMPq7i3eBHlP6JEu6Ism3D2mvFALohk7jrx8Dyprhx1aqDVCDJSgkKMN44bySV9va8fGPDTdwCEr\nr+a9uDPJGFKJKAZyCcSisqsWimqG6LcSQ0KSlQoffQ2UJ9xyOsCDIgNFq1QxF+IlEYvAMJyQwR33\nHLJr3wBz36Jb73i2Ladl/tYoPvgb1+CP3r4bG4V8mV+ILyLLrkLt1127GYlYBC8Iu5aSAT8xWXLV\nmgCOVP3MTKVjiI8WGlTxL4okAHOVPuRZdVPeKhGL4NpLxyABdhfyRDziKD3LDUzNVxGPyW25EPOz\nKczmeFCpkOuuWm/ZRjLoWh+3SgL2Hpmxpe9A+6JUkiQkE1FUG1qoBwU4qrt4VG6bS7y/JyJLtqp2\ntlhHIhbBJVsK9pwzajWjzaRitlH261pDZFJR1BuaK0xFIolUwqzn/Ng7X2VLwxuCzDyTiraFwiRJ\nwmAuboskvAIrkbfceBE+9FvXuq7RaCQ4svPLr9mFT/z+dXjJRcM4Zi14aNcGEVJa6rphh+FjURn3\nPG0ucvJ2jt49P1KExc+LJQ9q43DG2RiVQnxrtQ6KLKf3wgvadC6fiVs9v0zZqWj5AXPVH41IbR6U\nY6DMC3kol4BuGK74fqXeQkszXHUJtDMlrRCoyIxktUHYPcLKK2egkokoJJhuNIX4/KCLlVazmm74\nrvwAp5ljo6lDQrvggaB4sxjCmpwzuzB7G9U6Bqre1jNQxMkHmMeG9rcabwvxWa+zJvyqnQtpv0bE\nLuwtTXftNlutt/CP334OTx+cwh6hzX9dyM1s22BW9W8cccbgZ6C8RGQZowNJTM47UnwKD52YLNl9\n4MTkcCETx/RCLXCF7E0yk+otEYu4itOT8SgGc+4FEf2meNysJ9wylrVza4lYBLGomceaLzdMYYDV\nFd+LfS1VGl15UIAjpw/Kt0YjMq5RRjFbrOOF43NtnfRFqJt6mAcFOHmo0QH/30FIkoRsKmZfc7NF\nsyNMRJax3foM8qAyiSgqtRZ0wwgt6xBzw4SdH4+1C7hEmXnQtWX29TQX0KWKv5hCRFysdmqfNlJI\n4ffefDlGCknEo7KdvxUZyifsjjk09/3mGy6xvSAai9dAURdzPwOVtY6TuCFlbK2H+Gg1IaqHIrLk\nqhYXodXR9IK5vblXVQOYF4KfByXBqSS3J1EhYUjGqiCswKhSfWq+hoMn57Hv6Aw2j2Y6hurynjxQ\nsdzm4koAAB8XSURBVGLmthIxf8PbDbIkIZ2MYqZYgwEEelDRiDkpLVSaoTcWAFwsxN6jUTn05qYb\nmybgybkqhvPJthtiWFBpUQ5utNB+weY8UuczMxUM5xNtx8j2oISEtdluxsdACR0waBU7vWAaja/f\nd9gO6x4767RnIQ8qKXyv00kh0iajD2J0IIV6Q7PHWaIi2KkyDp0064HEG3e4kMTMQt3ulu+9pryd\nSijkE/cRSVBimyTEogcFAJdscZLg9FwhE8fkXBXlWqut2TCRSkSRjEcwV6yjUm8hHpMDJ0C6p45Y\nooOwxRiFHR/ZdzZ0b7RUPIqa5UHJlnHxgzo8hIX3iGw6hlKliWbLPFfkCf/CT+/Aza/caudPMqkY\nDDj5N0lCW8SGXgcAJaHzQ7WhIZmIuO4nWjzXm7pZ21QLNlCDuQQMw9xJwamhDJ5zxMVqN5ugZpIx\nfOgd1+CT77/Rdwzi/EjG56KJAt75C5cjGpHsBYE3xEcpAL8c1JaxLCKyZBeqA6LRXrMGqt2DCtuC\ngjwTSqin4u0Tbzwmt9VBnZoqY3QgZd+cdtsPQetvK4W8cs7tg5gtmhtsGQbwaz9zccff5Q0NUiX4\nYnrv+ZFJOiqcTIAHZf4tinK1KaiP/A3jUD5pJ2+DvCcil46j2dLRaOpoNDXMlRq+EwJNajMLNbvK\n3Ru2A9wXd0vTMVdquPb6IbKevEGl1gpcxTselG73cKvWNcyXG7jnqZP2REzhDfO1tPWJc4wm7Gap\n3Xu8dCxIVk8hvlNTZUwv1CxhhbtgVdMNnJgsIRqR2ia/QjaOq3eN4PLtZicLOu8JH5FEOhFFPCYL\nIT5HUg64DRT9znwmbt8nIwV/AwWYk+VssY5qyHEXfz+VYYQZqEu3DqKQieOx58/Zi6iYz+ItKXhQ\nuXQscOG6c1MBV2wftPcjCyNn7ZRNIWpaCFyxfQhvf80u+xylBSVftd5CKh71vX+9ij/ANGre6A5g\nTsjNloaK1ecxzIMCnK1kgPDjKSrkwkQSIoVsAjsCUhViLZRYenPVzhH8vz+8ET9thSu9HtS5uSpk\nSWqrEQXMyMg/fODV9uIEgFCou2ZzUBokuI1C2CZ+5N1QEaXfiiYei6Au/OBStYlStelKvvtJeO1a\nC08M+7d/9lJcunUAmm7gp6/aiMt81E5ekvEoErGI40FVG12FijqRSUXtUE1QLgAwV1TlWis0uUvQ\nxoxBDTQJR6zQCMw/AWYYZTifxPRCHaeny5Ck9rAd4NxwxUrTaX7rkz/wKq/CBCK2gWpodjNhwNx2\nWtMNXLFjCGODKRw7W0SparZQEosqCfKgSDnZDY6BMleRFOKj87Vrs3syoElgtlhHLh1vm/xkScL7\n3nYVfuGntgMQPSgZEVm2PY5kImrlLZKOSMJjoC4WDJRXoQq0F52LDOYSKNfMWqSgsDLgrJqPWGHw\nsBW/LJulC+Vay14sBHlQmm5geqEWGN4DzN/5gV99KV5xWRcGyhrXi5YXTd6nl4wtSmqZBirgHsoI\n4iWi1tB8Xx+zFs+d6iLFnXWLlQZSlrQ/CHHhEFSouxjsWrX5GubLDURkyb7nxAiHnwc1ZO2o4If3\n+TUvM69bstl4TLaThWF7JNEERh6Ut2CR3i8m3UgdtlEwUOSWixdVkAcVj0Xw/rftxu+9+XK7jVE3\n5DMxLJQbqDc1NJr6shR8hDhBdPKgWppu58DCDJQ4eYVhG5Rqs227Dy9D+SSq9RaOnSthdCAVWq9V\nrDRC61zEEJ9hGKEeFHnfYqEwYO5sCpie3NbxHMq1Fj79jT344l3PY4+1NYp4440NppC32lJ1Cxnr\nyTkzpFj2KLt2eTZvE41CWH6Bzp3oQQHOtU8GZyiXwEKliWZLtwuPxXAeLRLE54hQD8qaLOtNLXBh\nADh5B1oUdcq30rVD9VB+1wj9Rk03AgUSi4W8+X2WvN4bSiVsz6jWRKUWZqAc8RJgiq1Mj8snBB2N\noNHS7NdmQkJ8QPtmnEGkluBBhUEh+cm5KhbKpnDMK+YAzGuPdhKvN8xIhbeDRBiOqC3YQHW1RFQU\n5ZUA/reqqjcpirILwBdhqsL3Anivqqq6oijvBPAuAC0AH1VV9c5On9toaojHZGtztAjKtVZoDHXI\nSgZTItY3BxWLuFxGUj+JYSbn4mvhxLki/vJzD9sTpq+UNR7Bqy7f0PZ8GPlMHEdOFe2ODssRSBCi\nKx82WdDfyNMJM1CXLNZAVRwD5ReSA5wVWL2hYWKr/yRPmz0uVBpCpwAfD8oO8TXsYsqglTyFVepN\nDWI0iHrGbRxOQ9N1PP78OTxvbXdBTV/Fgu9oRMZf/rdXdr3jMeD2oOpNszkw1WVFZMnubE2IeR9v\nLYwInUuqcaIQXTIewULZ+c2DQrEueVBi2PLybYM4O1Ox6+S69aBEsUZYiG8gZ66cqaDaT54vIn6n\nJPmHmC/amMfj1r5HYUZ0MWy2kvTU6Na7bQ5h55as3auDwuTexW5LM69Rv8VzLCajVBXySgFCEnFn\n3VK1aQs3ghAXqyvhQdFi48xMBfPlht1/0gvtJL5Qbjj5Zh+JeRDd7FDd0UApivInAN4BgHr3fArA\nh1VVvUdRlM8AuEVRlIcAvA/AtQCSAH6iKMoPVFUNbbHcaGq2FU0loijXWoEKPsCZBI5bbYv847yy\nVduiIyLLTn3NULsyq1xr4rnD0zg9XbHzJX5hpqVQyCSgGwu2t7ecLhKEeCGGhVvopqFiyLCJhVY8\nG4fDLyzyAEtVMcQX7EERQZ+bFwweeXpe7xUwQ76JeARFMacWGOKzPChPuJJCiBuG0215DLqxvOKM\nsJCSHzSBTs5V7RXyrs0FPHdkBlvHs22tfFweVFgCPOHe1I3GSflXWqmLq25bJBF3JqtfevVFeJky\naneZdnlQASIJ8XOB8EWRLEkYHUja91GnBZlooOPRiG9+52dfuRXXXjqKI6eLXS+kOkG1aOSRBhko\n+q3k4QXdb+JiF3D2qfPzoOLRCBotR5kXJpIAnC05Os0d4v29Eh5UPGYKb46dLaHZ0kPnxEImjqNn\nivY8F9Rhx/d7ulDxdeNBHQLwVgD/av37GgD3Wo/vAvAGABqAByyDVFcU5SCAqwA8FvbB9aZuX8hk\nbLw7a4oUsnFEI7I9AfleBDHHbUwlZPvAieEaO25cbbU1IvWbJJcCTXDUk29FPKhUdx4U3TTk6YQZ\nKEmS8Pd/dGNHkYRYMBsmHwccJR+AwDBZwgrtilLWoBshZ/XjE9vt+CHuBOzdkDIiSxgpJNsWNSRq\nWI7CEjAN6WAugcm5mr2a3jCUxnVXjLv6EBLiMQoL8SUTZsGqXcRu3R8UPaDf4+z3U3Ok88JvSidj\nrm4RdH3GonKoMR7s0oMCzMnp9HQFEhxZcRCigQqLmowUUoGe+lIYH0q5PL0gA0Xjd6IQ3XlQNaFI\n10siZn7vQkD3EILCjpSfC/OwAY/MfAU8KMDs77j/RTMMGjYnbhrN4tCpha56IXpxCnWXYaBUVf2a\noijbhackVVXpfikCKADIA5gXXkPPh9Js6cikYhgdzaGQS+DEZAnZVByjo8F1RuNDKTvENzaSbXtt\nzjq5uUIKg7kkJudryKRiuGjbkL1KMwwD0YiMRkt3CSXisQi2bBpYttoOACasbhLnrAt8Yjwf+ru6\nYVxo6phORAM/b2zYfN28JWHeuALfvcU6TrokYbZkJm53bB3yPVYXbXUc58t3jdjf7R3D+FAG0/NV\nUAh626ZB33EO5JM4dqaIuLWSHBlK+75ubMRMfMcSUbRabgM1MZrFhnHzknz7z1yMeCyCL33veXui\nGhttv5aIbo/dxGgW+49MQ4qZt9XYcAa3vCY4b5lOmrU2G0K+GwDSqZid06JrPp9Nusa9fbOp9mvq\njkhiw1jwed9WpdZAaYyNBXdGuajmeKMjQ5nQcW6bKOCZQ9PIZeIYHw/vtjI0bMqOKRwmfu5yr9VO\nbB3P4fCpeciyhIu2DfsuzjZZx4d21B4a8L/m5DjlycxxL1g5uKGBVNvrM9ZEX7OuzS0ThcDfmk3F\nbO9t5xb/+0IkETf7UI6P5jA6sojcacDnbt9UsA3UxrHg6/Oll47jvmdO4WmrF+KlF410ff4yOfPY\nSiGL4+5lSg6iucsBmAOwYD32Ph9Ko6lBBjA5WYRdAG0YmJwsBr5nIJuwDVSj3mx7rWFNOGfOLKBW\nruP0VBnbNuQwNeXewTSTjGKuWLN3zN29cxiFbLztdUuFfs9j+84AAAbT0dDf1Q26EKtNp2KBn2dY\nr6Pi13q1sezvbtWtgtrJEk5NlTA+mA48VhGhQ0dSljA5WcToaK5tDMO5BI6fLeLQCfNS0Zst33Em\nYzIaTQ1HrddB031fV7di+zOzlbbE62ghab/n5pdvQbOl40vfe97+e63if4z8xh3EQDoG3QD2HjBX\nk9D9x0kM5RKo1FqQO1zzqXjENlA0TlkyJ7lmzbwHZOuYHzu9ANkK81RKNUxO+q+ojaY5AQ9k4+G/\nryXsrBpw3Ims5WVkkt1d69QbLmJdI8DijvdSGR9M4vCpebPl2bT/NSxr5j1k7/UUcC6pVGZ6vorJ\nySJOnzVVjIbfsbK8+pOWgrDlM38RhUwcpWoTw/kkXqmMdDwmKctALcxXEDW621U67FgXxO4UQODr\nxgumQ0BeUMQIv0ZEaHFY8tndXPzuxfKUoig3qap6D4CbAfwYwKMAPqYoShJAAsBlMAUUoRhwwjKU\nyO+UmB4VYvf+dVDm5331nkNWNbbhW4eTTkat/Id5cN59y5WuRPlyIbeYhB9iBfVSEcMmYeEWe7uA\nDu1pFgPlSY6eKaLR1O22PX7Q1t6FbDw0FEk5LNpXKCgMSiEw2u4j6PfQua81NLu+aSAbx1yp0XYN\nUG9Hyh0sN8QHmNtcA87eY53CXEP5JE5MljurtIRzHffkoJKeHNRcsW6v1L15L5HBXAJvvn5725bj\nXnKZuO3phJ1LwAnvdKtYHc4nMTVfC1Xung/MXpln23bFFilkE669yMLCyvGYs4AIK+2g30meUZCK\nDzBzlCenynjHGxXfcKGXTNLc4LKbQt1u2DDkhOrCQsDjgyl7S5yBbHxR91E0Yqq3w2TmS5m5PgDg\nNkVR4gD2A7hDVVVNUZRbAdwPU7r+IVVV/Xe88uA1UJ0O8IiQhAu7CB6z1D+Af6I+k4qZKpWSucvu\nShonwJ1P2bEh17EFSTeIE0QmFQN0/xOb8UwknXIH3X63LEl2A9yJEFFFNCLjppdtsvMiQdCEVmto\ndpNMP7JWaI96AQb9nqTYScKaKDaPZTFXmgksFrYN1Aqcf8q3HbQMbqc6KhJWdJrQxd9LQpCrdg7j\nxGTJXiiIcvyIJTQKmywkScIvCk1Sg5Als7ny9EK9cw7KEmDku8y3DheSwPHw0pLzAXWU8baT8rJ1\nPIu9h021X5gSNmvtFguIe0EF58fPzFSsnY2DP/NXXrsLN+6ecHVeCIMWbSsxzwDuLdvDJP6SJGHn\nRB7PHJpelECCiMXk0ELdrmYuVVWPAniV9fgAgFf7vOY2ALctdoB20teaIMJUfIBbbhpUqEtcsWMI\nE8MZ3HDVRNvrsskYDMOsk+qUhFwK4qpj5+aO6biuEFdcqUQUjaq/a+xVHPkdp8VitpqJ2s1LJ0bC\nPcJ3vEHp+JmiCtC7WZ4IeRinraarQTVgCVsgo9kTxbXKGI6eLrr2vLG/MxO3VWcr4UHRQshedXfw\noF537Rakk9G2Gikv4sKExrl714irj5osS0gnoijVmkhYk6mo4lsOAzmzu3UnT3x8MIW33bQTl20L\n98oIEkqs1Kq/Wy6aKGA4n8DlHca5bTzXlYHKpWOYsoRDdqNYH68nJhSmbhrNhOa6Nw5nFlWHt2ui\ngGKluWILbdoiR9ONjorWXZvN3KNfF/NOxKPyintQK4ooMwe68KAERU9QoS5x/Us2BNYviS1KRvIr\nn5QtCKviXR26n3eLV2YeZKDElVlY/7TFkk3HbQPl3b5jKYiKn7BVN3kJtLlcUKhJDPGRmuq6Kzbg\nxt3tCxTAbRRXwkCND6YhSU73iLAVMmCq/N56486On5v2CfH5kUmZLa4yqRiiEQkReWXOu9mMdqGj\nByVJEn7uVdu6/lyS2od1jzkfZFMx/PV7ru/4uq3CNhahHlQ6jmNnitB1Q1DxBTczBvz7Uy6HX37t\nLrztNTt9C2qXQjQiY2QghbMzlY6lN1fuGMbX7ztsb4K6GOLR9tZ0Ij3tJAGIstnuclBi0VqYzBwA\nLtkcXDsheiPnw4NKxCP2amZnhxVyt9CEZzZLDWl9IkyMYTfWYqFcUDQiB0rMF8NIIWlvhR3mQVEz\nUNpivFMvvkZTR7WhIRoJP05i6GIlvI1YVHaFOcJyDIsh5eNB+WGGmlrmZp4rYHCJ3TuHMTGS8Q2T\nLgfyoFY7xNctW4U9ssLyb0P5JAwAzx6aFjYrDPaggJUrPBZZKeNEXKuM4tKtAx0XJts25PDJ91wf\nuBAMI2b1Jwyi5x6UXXhoxWw7raZyqRgSsQiaLd138iEDN1JItm0FISKubsPqUJbDtvEcWpq+6KLP\nIOKxCKIRuaPRScQiTmJ7BQ0UGfINQ+kVWZ1HIzKGckmz11pIHmY4n3QJGoJCTWRkqNVRUO0KQQYq\nGpFXzNvYOJyxc2WdPKhucRVihizgMskYmi0dC+VGqKe1WK5/yUZc/5KNK/Z5BC1y/PI1a4HRgRSS\n8Uhgbz3iba+9GA/tOY3Pf2cfLrYWxb7pB2FuG1lCvma1+aVXd/buiaB6sk7EozLm1movPsBZPVFx\nWqcEqyRJ2DyWwVA+4RvDTVgXgdKh8vx8e1AA8IFf2Y0//rWXruhnjg4kXUWefkiSZE+OK2mgKJm/\nEuE9gsJ8tEWJH5IkucItQb8pIpvGe6HcMCeVDuonWjgkFtHSqBO0l1QqEVkxo0e5rHhUDl0l0zU9\nZ23Et9YZG0zjv73pMrzpuu29HoovsiRhq6W+DbuPLtpUwG++4RKUay08fdCsB/IVcMXOrwfVj8Ri\naz0HZd1Il24bxJ/++ks77lYLAO/5xZfYW0V7mRjJQAJwzaVjoZ+RXgUP6nzE1v/o7bsDtx0QSSfN\nPaFWMsRHSrEwifliGRtMYf+Ls/ZWKkFs+//t3XuMXGUZx/Hv7KXd7W2XLUsvFOja0gesliKXUoG6\nBKSAgogYCOEmomIAiWkUREhF+QMFCRoBFQJ4CUkRMcQaFNCCYAIkioBaHyQiCkLFciuhFwr1j/ec\nZRanc84sM3ves/v7JE0mnZlznj07O895n/c2Yyprn3oxs0+tv7eL59a/RqWtkrlwZVpbb8YgklS6\nblm9xXwbNWmo/F0/zsk5S4ExaUXLrJmOPnCAtU+9WHenXoBle81my+tvsOo3T2x3SP6wFpQSFBCu\nSVq6ryWaBNVWqWTOyUjVa07uNnMq3/vCYObAgOo5Ks1YaXy05C0NpF9WzUxQs5I+iHkj6Azdnt2S\nRVSz1gJM+wOyWoT9vd1DS8RktaDSpNjMctisViSoGlsd1FJ9zma2CsezhQN9LBzI3mIHwqjMeTv3\nsO6F12r+/qtbUHk2VxwPsgbFFZ+gWtBBmmfU2rASX4taUEVKf75mJqgl757BLjtNGVoRuhmWLZrN\n/Nk9mcdMS3xZQ7f7M+bJVUv7oJrZgpo1fRLtbZWmLToM+QcQVX+OJzR5Xp/kMzBrGgOzat/ApS2o\nyV0dTf27LLOs7//Cr1JRpYhhgyRa1AdVpEkt6INqa6s0NTk1csyZfZPYYerEzJFk1Qkqq/N9Sncn\nPVMmjGj+xvZ0T+xgxQmLR9xpXEv6O8xsQXWXr8Q3ngwN4FLraUhWN0jhCaqZ5ZVGDB8kUZ4SX16T\nJ6YtqLHxRdXWVmHl6ftllgT6e+svhfX2Y15yxv5Nb8XvkXOial4jK/GNjd/7WJK2oNT/9JasqkDh\nCaqoWnn3xI6hbQzGcgtqLJUS8gzXb6QFBc3bXqWV0sSTVYqsvulSgorP9J4uOtorzGvSxP2xIP4+\nqIL+kNoqlbDdweatTZuvEpNW9EGVwfRpXUM3HlktqLKY1NXBKYcvYNeZ9Vc8KeMovvFkh6kTufKc\ng5padi+7rKXtCr9SRc4inzZ5Ap2dzZuvEpN9rZ9/PPfKsPXaxoOO9jb6piVrx42hL4JD3jcn8zXD\nBkloFF+UxuKArHci+kESRbWgAD5x5J5MqrPETpn1Tevi00cvLDqMQvT3drP+lc1NHZ1XBsMWlR1n\nP7uUU72VUSCGlSQKTFDz5/Sw1+79hZ1fWiMdJTWWWlB5pCtpgEp8Ug5ZJb7iE1SkC0VKeQ0kfTXj\ncTLk5Jwj/kRiUIISnxKUNNcHFu/MnnP7mr76dhlM7u7kvy9vUoKSUoi6xNfMPWtEUm1tlXGZnACm\ndOVbt08kBlGX+BZkrDguIo1JpxdoLT4pg6xRjYV+ilecsLjI04uMOenE46w1C0ViMH9OD+cdv2i7\nzxfaB1VrPycRGbnl++/KgoHpzGninl0irdJWqdSdq1n4IAkRaZ7pPV3sMb+f55/fUHQoIu+YCtUi\nIhIlJSgREYmSEpSIiERJCUpERKKkBCUiIlFSghIRkSgpQYmISJSUoEREJEqVbdu2FR2DiIjI/1EL\nSkREoqQEJSIiUVKCEhGRKClBiYhIlJSgREQkSkpQIiISJSUoERGJUmk3LDSzTuAGYC4wEbgU+Atw\nE7AN+BNwtru/mby+H/gdsMjdN5lZO3AlsG/y/q+4++q3naMb+DGwE7ABOM3dn0+eawdWAde7+y/L\nELeZHZqc73XgP8Cp7v5axPEeDFyRnOdedz8/4/JGEXfV8xcmxzsx9pjN7KOEa/2v5KUr3f3eEsQ9\nH/guMAHYDJzo7usjj/meqpftAdzk7hdkxRxJ7IcBlwFbgbvd/aK8cY9EmVtQJwPr3f1g4AjgO4SL\nflHyfxXgIwBmthy4E5hZ9f5TgE53PzB53fwa5/gs8FhyvB8CFyXHmwf8FtivTHED1wDHuvsy4G/A\nmZHHexXhC+cAYH8z2ztHvDHEjZkdCXyogXiLjnkf4IvuPpj8y5WcIoj7+8l5lhES1YLYY06vMXAG\n8DQhwTSiyOt9OXAqsBQYNLP3Nhh7Q8qcoH4CXJw8rhAy+j5A+od1B3BY8vjN5PELVe9fDjxjZr8A\nrgN+XuMcBwFp66j6eFMIX+5rShb3oLuvSx53AJsij3eJuz9pZlOAHuDVHPEWHndyV/8ZYGUD8RYa\nc3KeM8zsPjP7ppk1Ul0pJO7kLn8n4OikVbIUeCjmmN/2/FXA+e7eyOe66NgfBvqATqALeKPB2BtS\n2gTl7q+6+wYzmwrcSsjwFXdP127aQPhSw93vqtHs35Fw5/Bh4OvAjTVOMw14ucbxHnH3tSWM+1kA\nMzsOOIRwZxRzvFvN7ABCyeI5wt1mLkXFnSTTqwkJamveeIuMOXl8F3AusIxwA3ZWCeLuAxYCdxM+\nzzsAp0UeMwBmtgiY5u6/zhNvRLE/BqwG1hLKwX9tNP5GlLYPCsDMdgF+Blzj7jeb2Teqnp4KvFTn\n7euB1ckv9V4zW5Dc+V6fPP8j4JXkOHmOV4q4zezzwPHAEe6epwVVaLzu/gAw18wuBS6ggVZJQXEf\nTiinrAJ6gdlmdoG7XxZxzAA3uPtLSQy3Ax/LE2/Bcb8AbHD3NUkMq4EPEvpnYo05dTKh9TIiRcRu\nZr3Al4CF7v5Mcs4VhLJfS5Q2QZnZDEJt9Zyqu5CHzWzQ3e8BjqR+Ce5+4Cjgp2a2F/BPd38CGKw6\nR2/ymoeS491X5rjN7MuEUsBh7r4x5njNrELo5zvG3V8k3MV15Ym5yLjd/TbgtuT5QeCsBpJTkdf6\nUTN7v7s/DRwK/D5PzEXG7e4bzexxMzvY3e8jtP7+HHPMVe8/lNB6aViBsW8klNnTkuSzQP9Ifoa8\nSpuggAsJTfqLzSytx54HfNvMJhCaoLfWef91wLVm9gChjlurpHEt8AMzux/YApxU1riTD/VK4A/A\nHWYGsMrdr40xXnffZmZXJLFuJvwx5BnUUWjcDcQXTczJtT4TuM3MNhJGhDVyd1/ktf4kcHXSZ/Yk\nkHekZ9Gfj5k1Sm95FfU52WxmK4A7zWwToZV2+gh/hly03YaIiESptIMkRERkbFOCEhGRKClBiYhI\nlJSgREQkSkpQIiISpTIPMxeJlpnNBR4nDNkG6AYeJcxdWVfnfWvc/ZDWRygSP7WgRFrn3+6+2N0X\nE1atfoL681OgarKkyHinFpTIKEgmw64E1iXrsJ0LvAeYAThwHMnKAmb2oLsvMbMjgK8SFuZ8EvjU\nO5jcKVI6akGJjBJ330LY5uRYYIu7LyUs2tkNHOXun0tet8TCHj6XAcvdfW/gV4xwaRyRslILSmR0\nbSNsWfB3MzubUPrbnbCCeLUlwK7AmmRZqnaGb5kgMuYpQYmMkmSdNAPeBXwN+BZhq4MdCWuiVWsH\n7nf3Y5L3dvHW6tIi44JKfCKjwMzagEuAB4B5wC3ufiNhn6tlhIQE8Eay8OmDwFIzS3eIvZgWbmsg\nEiO1oERaZ7aZ/TF53E4o7Z0E7AzcbGYfBzYTktZA8rrbgUdIdrgFbjGzdsJmjSePYuwihdNq5iIi\nEiWV+EREJEpKUCIiEiUlKBERiZISlIiIREkJSkREoqQEJSIiUVKCEhGRKP0PqHOzfrOXbWkAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144350f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Traffic plot\n",
    "df[df['Reason']=='Traffic'].groupby('Date').count()['lat'].plot()\n",
    "plt.title('Traffic')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RaKniY+u4qMzc6yo+LpIYClRqGsYlEeGAD/PLeePgT1HYlA7q3GIW//DtV+nv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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146bfa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Fire plot\n",
    "df[df['Reason']=='Fire'].groupby('Date').count()['lat'].plot()\n",
    "plt.title('Fire')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YVDPBi9OOeVVOz61BkSVMjhaxYyjPniWwamiOcpqCQk7pqjTQmpiMWVBx173n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\n16YkJQLMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147901d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create EMS plot\n",
    "df[df['Reason']=='EMS'].groupby('Date').count()['lat'].plot()\n",
    "plt.title('EMS')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heat Maps\n",
    "## By Day of Week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create multi-level index and unstack to re-structure dataframe as matrix\n",
    "dayHour = df.groupby(['Day of Week','Hour']).count()['Reason'].unstack() # columns become Hours and the Index becomes Day of the Week\n",
    "dayHour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x161f2b38>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i17o7BnDUL8vNUu/+8elf5K/+8PliuT/40ryt9i5HPF0sF+D4ry/Qam/37SeL5Z6y30Kt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\nGiVJktTW/wfDNSsbrHsklAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f29ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create heatmap\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(dayHour,cmap = 'coolwarm',linewidths=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create Clustermap using this DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sstallworth\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook.py:136: MatplotlibDeprecationWarning: The axisbg attribute was deprecated in version 2.0. Use facecolor instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1598c908>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MByNi59E2NmhJkiQN72hgl4h4OdWZ4WcAz83MT2Xmr0bb2KAlSZI0vB8DKwHP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1598c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clustermap\n",
    "sns.clustermap(dayHour,cmap = 'coolwarm',linewidths =2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "  </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"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Matrix\n",
    "dayMonth = df.groupby(['Day of Week','Month']).count()['Reason'].unstack()\n",
    "dayMonth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18793ba8>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x187c8630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create heatmap\n",
    "sns.heatmap(dayMonth, lwn = 1, cmap = 'coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sstallworth\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook.py:136: MatplotlibDeprecationWarning: The axisbg attribute was deprecated in version 2.0. Use facecolor instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1897db38>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mSiZmk/m7A3DTnQ/XXEn7dtxuEwAeufmamitp39O33wuA5tf/seZK2tf/+g8B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\nJBVi0JIkSSrk/wH0/Py9vwnLgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1897d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create cluster map\n",
    "sns.clustermap(dayMonth, lw = 1, cmap = 'coolwarm')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}