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01-911 Calls Data Capstone Project (Done)

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01-911 Calls Data Capstone Project_RP_Done

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01-911 Calls Data Capstone Project_Yi Cong

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01-911 Calls Data Capstone Project_Submission

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01-911 Calls Data Capstone Project_Simran

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01-911 Calls Data Capstone Project_REAMONN CHAN

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01-911 Calls Data Capstone Project - Sally Khoo

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01-911 Calls Data Capstone Project_jerrell

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01-911 Calls Data Capstone Project_Shobana

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01-911 Calls Data Capstone Project_ryphua

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01-911 Calls Data Capstone Project_Yen Ling

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Please help to take a look, thanks!

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01-911 Calls Data Capstone Project (Submitted)

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i realised that my wk 8 was not posted after checking and so I am reposting here

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01-911 Calls Data Capstone Project_Daling Sheng

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01-911 Calls Data Capstone Project_HY_Completed

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Week 9 Assignment

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01-911 Calls Data Capstone Project - completed

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01-911 Calls Data Capstone Project_sheryl

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something fantastic

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GracieSham_01-911 Calls Data Capstone Project

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Completed

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01-911 Calls Data Capstone Project_Raymond

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Completed

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Capstone Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this capstone project we will be analyzing some 911 call data from [Kaggle](https://www.kaggle.com/mchirico/montcoalert). The data contains the following fields:\n",
    "\n",
    "* lat : String variable, Latitude\n",
    "* lng: String variable, Longitude\n",
    "* desc: String variable, Description of the Emergency Call\n",
    "* zip: String variable, Zipcode\n",
    "* title: String variable, Title\n",
    "* timeStamp: String variable, YYYY-MM-DD HH:MM:SS\n",
    "* twp: String variable, Township\n",
    "* addr: String variable, Address\n",
    "* e: String variable, Dummy variable (always 1)\n",
    "\n",
    "Just go along with this notebook and try to complete the instructions or answer the questions in bold using your Python and Data Science skills!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Import numpy and pandas **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import visualization libraries and set %matplotlib inline. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the csv file as a dataframe called df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv ('911.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() of the df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 zipcodes for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19401.0    6979\n",
       "19464.0    6643\n",
       "19403.0    4854\n",
       "19446.0    4748\n",
       "19406.0    3174\n",
       "Name: zip, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 townships (twp) for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Take a look at the 'title' column, how many unique title codes are there? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** In the titles column there are \"Reasons/Departments\" specified before the title code. These are EMS, Fire, and Traffic. Use .apply() with a custom lambda expression to create a new column called \"Reason\" that contains this string value.** \n",
    "\n",
    "**For example, if the title column value is EMS: BACK PAINS/INJURY , the Reason column value would be EMS. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMS'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].iloc[0].split(\":\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99487</th>\n",
       "      <td>40.132869</td>\n",
       "      <td>-75.333515</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST; NORRISTOWN; 2016-08-2...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:06:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99488</th>\n",
       "      <td>40.006974</td>\n",
       "      <td>-75.289080</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL; LOWER MERION; ...</td>\n",
       "      <td>19003.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:07:02</td>\n",
       "      <td>LOWER MERION</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99489</th>\n",
       "      <td>40.115429</td>\n",
       "      <td>-75.334679</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST;  NORRISTOWN; Station ...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: FALL VICTIM</td>\n",
       "      <td>2016-08-24 11:12:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99490</th>\n",
       "      <td>40.186431</td>\n",
       "      <td>-75.192555</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN;  HORSHAM; Station 352; ...</td>\n",
       "      <td>19002.0</td>\n",
       "      <td>EMS: NAUSEA/VOMITING</td>\n",
       "      <td>2016-08-24 11:17:01</td>\n",
       "      <td>HORSHAM</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99491</th>\n",
       "      <td>40.207055</td>\n",
       "      <td>-75.317952</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST; UPPER GWYNEDD; 2016-08...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:17:02</td>\n",
       "      <td>UPPER GWYNEDD</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99492 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             lat        lng  \\\n",
       "0      40.297876 -75.581294   \n",
       "1      40.258061 -75.264680   \n",
       "2      40.121182 -75.351975   \n",
       "3      40.116153 -75.343513   \n",
       "4      40.251492 -75.603350   \n",
       "...          ...        ...   \n",
       "99487  40.132869 -75.333515   \n",
       "99488  40.006974 -75.289080   \n",
       "99489  40.115429 -75.334679   \n",
       "99490  40.186431 -75.192555   \n",
       "99491  40.207055 -75.317952   \n",
       "\n",
       "                                                    desc      zip  \\\n",
       "0      REINDEER CT & DEAD END;  NEW HANOVER; Station ...  19525.0   \n",
       "1      BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...  19446.0   \n",
       "2      HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...  19401.0   \n",
       "3      AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...  19401.0   \n",
       "4      CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...      NaN   \n",
       "...                                                  ...      ...   \n",
       "99487  MARKLEY ST & W LOGAN ST; NORRISTOWN; 2016-08-2...  19401.0   \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL; LOWER MERION; ...  19003.0   \n",
       "99489  CHESTNUT ST & WALNUT ST;  NORRISTOWN; Station ...  19401.0   \n",
       "99490  WELSH RD & WEBSTER LN;  HORSHAM; Station 352; ...  19002.0   \n",
       "99491  MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...  19446.0   \n",
       "\n",
       "                             title            timeStamp                twp  \\\n",
       "0           EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1          EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2              Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3           EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4                   EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "...                            ...                  ...                ...   \n",
       "99487  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:06:00         NORRISTOWN   \n",
       "99488  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:07:02       LOWER MERION   \n",
       "99489             EMS: FALL VICTIM  2016-08-24 11:12:00         NORRISTOWN   \n",
       "99490         EMS: NAUSEA/VOMITING  2016-08-24 11:17:01            HORSHAM   \n",
       "99491  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:17:02      UPPER GWYNEDD   \n",
       "\n",
       "                                 addr  e   Reason  \n",
       "0              REINDEER CT & DEAD END  1      EMS  \n",
       "1          BRIAR PATH & WHITEMARSH LN  1      EMS  \n",
       "2                            HAWS AVE  1     Fire  \n",
       "3                  AIRY ST & SWEDE ST  1      EMS  \n",
       "4            CHERRYWOOD CT & DEAD END  1      EMS  \n",
       "...                               ... ..      ...  \n",
       "99487         MARKLEY ST & W LOGAN ST  1  Traffic  \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL  1  Traffic  \n",
       "99489         CHESTNUT ST & WALNUT ST  1      EMS  \n",
       "99490           WELSH RD & WEBSTER LN  1      EMS  \n",
       "99491          MORRIS RD & S BROAD ST  1  Traffic  \n",
       "\n",
       "[99492 rows x 10 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'] = df['title'].apply (lambda title: title.split(\":\") [0] )\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the most common Reason for a 911 call based off of this new column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Reason', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot (x = 'Reason', data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "** Now let us begin to focus on time information. What is the data type of the objects in the timeStamp column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type('timeStamp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You should have seen that these timestamps are still strings. Use [pd.to_datetime](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) to convert the column from strings to DateTime objects. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You can now grab specific attributes from a Datetime object by calling them. For example:**\n",
    "\n",
    "    time = df['timeStamp'].iloc[0]\n",
    "    time.hour\n",
    "\n",
    "**You can use Jupyter's tab method to explore the various attributes you can call. Now that the timestamp column are actually DateTime objects, use .apply() to create 3 new columns called Hour, Month, and Day of Week. You will create these columns based off of the timeStamp column, reference the solutions if you get stuck on this step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = df['timeStamp'].iloc[0]\n",
    "time.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99487</th>\n",
       "      <td>40.132869</td>\n",
       "      <td>-75.333515</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST; NORRISTOWN; 2016-08-2...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:06:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99488</th>\n",
       "      <td>40.006974</td>\n",
       "      <td>-75.289080</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL; LOWER MERION; ...</td>\n",
       "      <td>19003.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:07:02</td>\n",
       "      <td>LOWER MERION</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99489</th>\n",
       "      <td>40.115429</td>\n",
       "      <td>-75.334679</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST;  NORRISTOWN; Station ...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: FALL VICTIM</td>\n",
       "      <td>2016-08-24 11:12:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99490</th>\n",
       "      <td>40.186431</td>\n",
       "      <td>-75.192555</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN;  HORSHAM; Station 352; ...</td>\n",
       "      <td>19002.0</td>\n",
       "      <td>EMS: NAUSEA/VOMITING</td>\n",
       "      <td>2016-08-24 11:17:01</td>\n",
       "      <td>HORSHAM</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99491</th>\n",
       "      <td>40.207055</td>\n",
       "      <td>-75.317952</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST; UPPER GWYNEDD; 2016-08...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:17:02</td>\n",
       "      <td>UPPER GWYNEDD</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99492 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             lat        lng  \\\n",
       "0      40.297876 -75.581294   \n",
       "1      40.258061 -75.264680   \n",
       "2      40.121182 -75.351975   \n",
       "3      40.116153 -75.343513   \n",
       "4      40.251492 -75.603350   \n",
       "...          ...        ...   \n",
       "99487  40.132869 -75.333515   \n",
       "99488  40.006974 -75.289080   \n",
       "99489  40.115429 -75.334679   \n",
       "99490  40.186431 -75.192555   \n",
       "99491  40.207055 -75.317952   \n",
       "\n",
       "                                                    desc      zip  \\\n",
       "0      REINDEER CT & DEAD END;  NEW HANOVER; Station ...  19525.0   \n",
       "1      BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...  19446.0   \n",
       "2      HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...  19401.0   \n",
       "3      AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...  19401.0   \n",
       "4      CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...      NaN   \n",
       "...                                                  ...      ...   \n",
       "99487  MARKLEY ST & W LOGAN ST; NORRISTOWN; 2016-08-2...  19401.0   \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL; LOWER MERION; ...  19003.0   \n",
       "99489  CHESTNUT ST & WALNUT ST;  NORRISTOWN; Station ...  19401.0   \n",
       "99490  WELSH RD & WEBSTER LN;  HORSHAM; Station 352; ...  19002.0   \n",
       "99491  MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...  19446.0   \n",
       "\n",
       "                             title           timeStamp                twp  \\\n",
       "0           EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1          EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2              Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3           EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4                   EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "...                            ...                 ...                ...   \n",
       "99487  Traffic: VEHICLE ACCIDENT - 2016-08-24 11:06:00         NORRISTOWN   \n",
       "99488  Traffic: VEHICLE ACCIDENT - 2016-08-24 11:07:02       LOWER MERION   \n",
       "99489             EMS: FALL VICTIM 2016-08-24 11:12:00         NORRISTOWN   \n",
       "99490         EMS: NAUSEA/VOMITING 2016-08-24 11:17:01            HORSHAM   \n",
       "99491  Traffic: VEHICLE ACCIDENT - 2016-08-24 11:17:02      UPPER GWYNEDD   \n",
       "\n",
       "                                 addr  e   Reason  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  \n",
       "...                               ... ..      ...   ...    ...          ...  \n",
       "99487         MARKLEY ST & W LOGAN ST  1  Traffic    11      8            2  \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL  1  Traffic    11      8            2  \n",
       "99489         CHESTNUT ST & WALNUT ST  1      EMS    11      8            2  \n",
       "99490           WELSH RD & WEBSTER LN  1      EMS    11      8            2  \n",
       "99491          MORRIS RD & S BROAD ST  1  Traffic    11      8            2  \n",
       "\n",
       "[99492 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hour'] = df['timeStamp'].apply (lambda time: time.hour)\n",
    "df['Month'] = df['timeStamp'].apply (lambda time: time.month)\n",
    "df['Day of week'] = df['timeStamp'].apply (lambda time: time.dayofweek)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Notice how the Day of Week is an integer 0-6. Use the .map() with this dictionary to map the actual string names to the day of the week: **\n",
    "\n",
    "    dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon', 1:'Tue', 2:'Wed', 3:'Thu', 4:'Fri', 5:'Sat', 6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day of week'] = df['Day of week'].map (dmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Day of week', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot (x = 'Day of week', data = df, hue = 'Reason')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot (x = 'Month', data = df, hue = 'Reason')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Did you notice something strange about the Plot?**\n",
    "\n",
    "_____\n",
    "\n",
    "** You should have noticed it was missing some Months, let's see if we can maybe fill in this information by plotting the information in another way, possibly a simple line plot that fills in the missing months, in order to do this, we'll need to do some work with pandas... **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a gropuby object called byMonth, where you group the DataFrame by the month column and use the count() method for aggregation. Use the head() method on this returned DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day of week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>11527</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13203</td>\n",
       "      <td>13096</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>9930</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11465</td>\n",
       "      <td>11396</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>9755</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11092</td>\n",
       "      <td>11059</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>9895</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11323</td>\n",
       "      <td>11283</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>9946</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11420</td>\n",
       "      <td>11378</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "Month                                                                      \n",
       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "\n",
       "       Reason   Hour  Day of week  \n",
       "Month                              \n",
       "1       13205  13205        13205  \n",
       "2       11467  11467        11467  \n",
       "3       11101  11101        11101  \n",
       "4       11326  11326        11326  \n",
       "5       11423  11423        11423  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth=df.groupby('Month').count()\n",
    "byMonth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a simple plot off of the dataframe indicating the count of calls per month. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth['lat'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now see if you can use seaborn's lmplot() to create a linear fit on the number of calls per month. Keep in mind you may need to reset the index to a column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f61317130>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Month', y='lat', data=byMonth.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a new column called 'Date' that contains the date from the timeStamp column. You'll need to use apply along with the .date() method. ** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of week</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month Day of week        Date  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12         Thu  2015-12-10  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12         Thu  2015-12-10  \n",
       "2                    HAWS AVE  1   Fire    17     12         Thu  2015-12-10  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12         Thu  2015-12-10  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12         Thu  2015-12-10  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] = df['timeStamp'].apply(lambda t:t.date())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now groupby this Date column with the count() aggregate and create a plot of counts of 911 calls.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Date').count()['lat'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now recreate this plot but create 3 separate plots with each plot representing a Reason for the 911 call**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CBp8D8xR8TnvqlcQfCwaopVZZpiWGoRQmK8kbRjH4CJtB7McwX91VZYyheuVzKBBoqZW3fu1aSaEZGovKSjJjoiJGMfgInpVUrGPwQ3dVOcGGg9RS2T0mK8lQCrN5kzeqKgYi+ioRDRLRVu1YDxE9QER7xO9u7bGbiWgvEe0iomsbJfhCQ65gAgGtjmGeCty8xBhCwUBLmd+OVh4tJLehsZjuqt7wYjHcCeC1rmOfAPAgY2w9gAfF/yCijQCuB7BJPOc2IgrWTdoFTLGQbB5dSblZ1DEEWiz4bOt/t47chsaidvVrobHcDKoqBsbYLwGMuA5fB+Au8fddAN6iHf82YyzLGHsJwF4AF9dH1IWNpQV5A/PUKylTkK6k6pXP4RZLV7VM8NlQAuNK8katMYZFjLFjACB+D4jjywAc0s47LI4ZqiDnLtLabs9Xd9XKriT+OxRssawk40oylMC4krxR7+AzlThW8hsgopuIaAsRbRkaGqqzGK2HnJx5VpI41vDgs/cmeuEWzkpqJUvH0DgYYyYrySO1KoYTRLQEAMTvQXH8MIAV2nnLARwt9QKMsdsZY5sZY5v7+/trFGPhoMcY5n1rTw/7MXCLoaHi1BWTlWRw43QvNlGQFqBWxfBDADeKv28EcK92/HoiihLRGgDrATwxNxFPDeSkyyufGx98tm2GnJe220KuULC1LAZT4GZwow9zMyYqE6p2AhF9C8AVAPqI6DCAWwB8BsDdRPQeAAcBvAMAGGPbiOhuANsBFAB8iDFmNUj2BYUcqEHCvASfs0IpBMhjumqLZSWZScDgxsSdvFNVMTDGbijz0FVlzr8VwK1zEepURCoB3WJopCtJBp4T0RCms4Wy5xUrnwmMcdcSUalQkr8wMQaDG8feIsa9WBFT+ewTStYxNHBVI+ML7dEQbFZeCTEtXRVonUnWrA4NbuTYbbVizWZgFINPUDEGLfjcyElYtxiA8kpIT1etdJ7fMBaDwY28x8It5hZtBkYx+IRigZtWx9DAwTud5Yqhsy3M37/M5FmMMfCh0iomuMlKMrhxtncxW75WwigGnyBdOaTt+dzICW00lQMA9CWjAIB8mTfTg8/6/37H7PlscGMptygfy2ZYlMcoBp9QzEoiiMV5Q4PPY+k8AKA3GQFQ3mJgLepKkh8nGKCWkdnQWOT9pKxfMy7KYhSDT5ATWUBzJTVyQhtzWQzlahlsV/C5VSpGne3CW0NmQ2Nxx8vMsCiPUQw+QU5eAYcrqXEjd3SaWwx9VSwGPV210TLVE12hmZWhASgutFrNLdoMjGLwCUxLVw3Mw8AdS+fQHg0hGuJd0atZDCGZrtoiN1PRYmitrrCGxmHbLuu3RcZyMzCKwSfobbfnw2IYS+XRlQgX3VZl+iWxFg3YOSu2myyMwRe4FzlmXJTHKAaf4KhjmAe3zVgqh662iPK3ltuTQQ/iNlqmeqJbDK0SFzE0FlXg1mJjuRkYxeAT9HL9+ahjGE3l0RUPV53w3cHnVrmZpJiRUGs1/zM0DnfwuV5pzIwx/O5/PIEHd5yoy+v5AaMYfIJd0pXUuPcbT+fRFY+o1VP5GAP/HRGKoVInVj9ha1lJxmIwAM69Rfj/9Xnd4ekcHto1hEf2DdfnBX2AUQw+Qc9KUnUMDbUYcuiOhxEMVLYE5KoqGhaKoUXKiIsZKMZiMHCKvZLq60o6OpYGAAxPZevyen7glFcMuYKt+gY1k2IdQ+ODz5bNihZDsIrFII5HQ3yo5FpFMTgshiYL02LkLRvpXPPviXrjDj7Xy5WkFMN0ri6v5wdOecXwqXu34n1f29JsMUrGGBqlGCYzeTAGdLWFi66ksi0x+G+Z1pqvsNubnzB1DLXzO3c8gTM/dV+zxag7ehM9oH6upCNjGQDAySmjGBYMLw5N48BwqtliOPZ8JiIQNc6VNJrixW3dWrpqtToGaTGU66nkN0wdQ+08+iL3le8/Od1kSepL0WKob3LHkVHjSnJARB8lom1EtJWIvkVEMSLqIaIHiGiP+N1dL2EbwVg6h8lMvtliqEEqN8EJUuNaOch2GF1tEdU3plqvJBljyBdaTDGYrKRZs7o3DgC4f/txdeyuR/Zj+9GJZolUFyyXK6neMYaR6dyCSXSoWTEQ0TIAfwxgM2PsLABBANcD+ASABxlj6wE8KP73LWOpPCYzhaZ34JRmrlzBBxrY/G1MWAx6ump1i4G7klolxiAvXcRkJc2a/nbeP+un24rpl5/+8XZ87+nDzRKpLqhMtUB9izWPjnPFULAZJnywyKwHc3UlhQC0EVEIQBzAUQDXAbhLPH4XgLfM8T0ayng6j4LN1I5mzUKPMQDcYmjUhCYHb4cWY7CqFLhFlCupNSZZPSvJ6IXZkRNW4dMHR2HZDAXLRt5ivkjSmAvFOob6tsQ4OpZGUmx4tVDiDDUrBsbYEQCfA3AQwDEA44yx+wEsYowdE+ccAzBQD0EbQSZvIStugslM+X2P5wN9z2eAWw6NqhnI5vlnjoWDRYuhzITf6jGGkOmuOmvkPcEYMJUpICP+z+Rb47svx4zK5zoohkzewsmpHM5a1gFg4cQZ5uJK6ga3DtYAWAogQUTvmsXzbyKiLUS0ZWhoqFYx5oR0qQBoepxBurJkqmo0FFArt3qTLVjqParldDOXK6lVFIO0tiImK2nW6O7CiUxepa5mCq1uMTir+OvhPj42zjOSzlneBWDhpKzOxZV0NYCXGGNDjLE8gHsAXA7gBBEtAQDxe7DUkxljtzPGNjPGNvf3989BjNoZSxe/xIlmWwxarySAr+YbtUKTK8JoKOC58jnaoq4kk5U0e3IFG/EIXwhMZgrKhZRteVeSOytp7q85IhTBhkXtAIzFAHAX0qVEFCeeSnMVgB0AfgjgRnHOjQDunZuIjcNpMcxOMQxNZvGtJw7if144VpeVh4oxiG8kGg54WqEVLBu/2jM7i0sqhkgoULXyWbmSwq3lSpIWQzBoNn6fLbmCrXb2m8zklYXZyq6kwYkMnj88DqC+TfRkvG5VbxxEJsYAxtjjAL4L4GkAL4jXuh3AZwBcQ0R7AFwj/vclc3ElfeV/X8TN97yAP/jG09gzODVnWeREpiyGUNDTCu3BnYN49x1PYN+QdxmUYgh6txhkr6SWUQys2HfKGAyzI2/Z6E3wzKTJTAHpHP/Osy3sSvriL/biH366C0B9g88TaVkTFEF3PILh6YVhMYTm8mTG2C0AbnEdzoJbD75nXHMl1WIxSHafmMTpwpSsFXd761g44GmFJuWYjfzZgoVoKAAi0qqsS78XYwwB4vUAABoW96g3lpA7GDDB59mSK9hqy9eJTB6d8TCA1rYYxtPFhV8901WlC7ojFkZPIoLhU91iWAjMxWIYnc5h3UASRMC+wblXiFqudFUeY6i+QpMDfjaphNm8rWIG0mIoFzuwbIYAkWYxtMYkawu5iVpnn+pG8PXHDuCv/ntbycfuffYIPn73czOO5yxbbfmqxxhaOV1VT0evZjF88D+fwgPbvbXQlhZDeyyEjlio6dmN9eLUVgzpPEIBQoBmbzGMpPJY2tWGZV1ts3LjlGOGKykc9BRjGBXBr+wsVvLZgo1omAcXq1WB2ozLFG4xV5JlM+VKOpUrn3+1ewj3bys9yT26bxg/2XrMccy2GfIWQ0+iGGOQlkIrZyWlNWunUiZewbLxk63H8ajHFtoT6TyioQBi4SAS0RCmskYxtDyyw2gyOntNPzqdQ088jLX9yfooBuZWDN5cSWNixTKbjJFcwVYWQLXKZ8YYSLhkiFpIMTCGoHCV1cOVdGgkhRdE8LKVyBRsTOdKj+1M3kIqZzksKpmqmoiGEA0FeIwh3/rB57R2DSrtxzAtUnO9ehAmMnl0tHFXWzIawrRRDK3PeCqPzrYQ2mPhWZeyj0zn0J2IKMUwV3eFaqIXkHUM3lxJ0h2WmZXFYKkso+qVz0zJFA4GWqYlhm0zBAJ8m9R6GAwf/85zeNMX/hcP725OzU2tZPJW2clKTvi6m0V+v9FQQNwXC9GVVH4HN3mtvK78J9IFdMR4qDZpLIaFwVg6h654BO2z9A1mCxamsgX0xCNYN5BEJm+rfim1IscoqRiDN4tBBtBnYzFkC7YqWKveK6loxUSCgbIV0n7DYvV1JU2J8fGH33i6ZTYrAvi4yFusZEaRHF+6RZHTUpm5zzyvxla2pS2GmTGGUpbkrBWDZjEYV9ICYSyVR1dbGB2xsAoieX0eAGExJAAA+4bmFoCWk5duMXiZ7KUss44xuILPVoWWGFJZhYPkS1fSEy+N4N13PO6YsLlCE80I6+BKkq6+yWwBIyl/Z57kLRvv+NIjeHj3kJr8U9lSisGa8VhOS2WWCyb5GjnLnpcMr1t/vB13PbK/rq+pL7Iq7cdQdCV5tRjy6Ig5XUnNbshZD055xdAZD8/aYpDVjj2JCBZ1xAAUKx6f3D+CHzxzZNay2K6WGF6Dz2M1ZSVZSjFUjzEULYZwMOBLxfCrPUP41Z6TjnYEMitJZnnN1dU3kc4jIaqB/Z6S+ORLI3hy/yg+e99ONYZKrWTlmClnMbTHwpjM5B1umGzBwuHRFP7twT0NmwDv23YcP9vhLSvIK6WykuriSsoUijGGWAg2g+eGnF/51Yt4yad7XiwIxfDovmG1x8BsGE/n0dUmXElZ7xbDqKYY2oV/USqWux7ZrwppZoOcuEgLPuctVnGFxhjDeA0WQ86yVbdUqhKgtUU9ACBiDAX/rYaOi341utWnZyUBc2+YNp7O47T+JIDiwsCv3C9SLc9b0VVy8pcoa0Jzs0jFH3ZYDJbjOfc+exT/+MBu1SfIK4wx/GrPUFWFMp21MFrlfs7krZKZQ2OpHLbsH5lx3OFKqtBETyqEqVlZDHwOSIgOq16UynS2gE//eAd+9NxRT+8z37S8YsgVbNzw/x7DeX/9wKyUQ96yMZUtoCseFiujWVgMKV0x8NWCnJTSOaumIN3MAje+Oq30Wum8pYKFs3Il5YsxBvmelfZjUDGGkD8thuMTQjFoCQSWkDug3Aa1K4a8ZWM6Z+E04TY8OZVFOmeVneAy+fKPNRrGGO7fxjfYYdDiCKVcScKa0IPTWYfF4HQlAfyzHdE2ppEULHtG8WMm78x4evylEbz7jifwXJXsrqlMAaPTlRdq//3cUdzw/x5Tm+RI/u5/duLtX3oUe05MqmOMOdvqh4Pls5JSQoG6s5JKTfaM8f0XOoXF0C4Vg4e5RF7zvE9rbFpfMWgT1WxW6rIwrGsOrqTueASRUICn9YkvOp2vTTHIFbsqcBMr+kqvNaoV6M0u+FzMSgL4CqrSfgykXEn+jDGckIohXfwObWkxSMUwB7HlWDmtj1sMu45P4vy/ub9khlK2YOHSv3sQP2zSSnDv4BSOipV8RluklMpMUjEGbTUt76eIykpyupIyeau4laWmGD75/a14/9eLe6fbNsMrP/sL3KnFCuR9M14hnpct8MVONatMWhRHXIohL77oLz38ovaazi8/WGGxMJWVFpal7snnDo3h3L+6H9uOOhVaJs/3qdCDz0BpJTzzffj3Ue6+azYtrxj0oOlsTFsZtO1sCyMWDqoNSbwgB22XaBUgfbEAv8nSNawYi030XBZDBUtAt5BqDT4D/EYpV9HMXK6kUorh8GgKb//3R2py59UD5UpyWAxcydbDlSQnspW9bQgFCI++OIxM3i7pH55IFzCWymP/yebsI65P1qlccb+RUopBulf0x+SqPypcSamcpVbRAJ8Mi1tZFtvCHB5LYdfx4ir95FQWg5NZbNO2A5WToe7WcSMn1XTe8nSeXBRIpCV877NHMDiZKfl+8l6rFGMAiu63h3YNwbIZtuwfdZyrNryKScUgOtKWcEv/64N78Pmf7VH/S2Xs1yy/llcMeU3jevULAsU0z862sPK3e83RH53OoSMWUiZpRyykeqZk8hZsBhUf+Jef7Z5h7pZiZoFbdVfSuG4xzKIqVU9XBaTFUMaVZBdlCgUDyFkM//rgHnzk28/gl2LF/PzhcWw5MIq9dWgmOFvSOUtdez3GYLNiHQMwt06aResygp5EBNuOTIj3Kz/ZpsoUlXllZDqHf35gd8V9Mr7w8z1KKUr099Xbyk+XmGTlosNhMbiCzwCf5IvPsdR41oPwmbyNoamsmmzlSl4f+6ls8R4phz4xV4ozyPPKff6CzbD3BB+P0uL5+DWn4/++4Uys6OZ7Wpe63VPa+8v5ZMsBHrNwWwxyvHW0cUuhPRoWss38fPc8fRgP7CjuoS2VpF9bzLS8YtBvnMlZ5BDrN7tcPXvN0x5J5VXLAAAOV5SqEi1YeP7wGP7lZ3vwfQ9ZSirGoAWfgco30Zg2Ec6mKjU3w2IIVIkx8L8jQcJ0toB/emA3fvDsUXzt0QMAioN8NlZLvdBXjPqeGrbNK5/rkZUkx0pnG2+UJhcQpYoiU3kuQ7lqY6/8YucgPv/gHuwZnCz5+OHRND53/27cJ1pajEznsPXIuHKF9CWjDj+922KwbaaUQPmsJD7hDU4UFcPgREYpGd3dky3wegl5rY6O8e9Fr++Rz5P3yMHh1AyrS/flV3InSZndFsN01lL3jow9yfdb2RvHe19xmmptX8mVJGUpWDaePsAthe3HJhznlrMYprMFWDbDL3fzQHu2YOHgSMqxkJg2rqTGors2pmaRWSRdSV01WAxjqRw647picLqSAD6hS9PTS8sMS2Ul8f9lL6NKk638DO3R0CwtBsuhGNwxBqmMcgUbBZtpMYaAw1007crgKKXEGt0i4LiuGEplJdUh+DyhKQbZddT9fhL5/ZeqG5gNcjLTrUKHTHK8ifO+/Mt9eNcdj6vr3ZeMOFbc7uCpngrtcCVpMQYZVD0+nkFYVAvr9ToOxSAWJrLbr7QUjo1llFKWMshr9KkfbsVHvv2MQ64pzxYDf43jE84216lcAWtELEgpBvF+beKekhZwNVfSZKaAHccmMZ2zsKyrDbuPTznmm3FlMRTrGAC+QP313pP4na8+gV/sGsSB4RRs5lxITJngc2ORE+ps+x0pxRAPK7eKV4shlbNUTjvgtBgyUjHkbDwp0ua8FL+5W2LEQtVdSdJVsKgz5ll2voJxWgyhYDEracexCWy65ad46eQ0rvvir/H9Z46oFVY4GHAEDuWqbbqMxXDf1uPYdMtP8aPnGxeI1VeM425XEpGaBOYSY9DjUXIDG6C0xaD89nO0GOT3Xi5QK1efUgGNTOUwlsorZdXfHnVMrG7Xlm5hTpcpcJM1OpPZAjrb+Od+UYzlADnjGVLRSMUgXUk5y8ZJEYtwu5JGpnPYfcLZTsazxSDOO+FyJU3nLPS3R9EeDanH5Pu1RZyKodQ6UP/eJjJ5/MevXwIAvPuyVchZtmORJ78DaVklYzL4XFAurvu2Hlcu1ol0XimjYozBWAwNQfrouuJhTGW8Vx2OpfMg4qv9osXgbZWXyllq60NAKgb+pcsVXDpvYYswQfcNTs2Qy7YZ3nvXk2r3telsAdFQQMUtpDlcacIfT/HOjp1tYc+dL/MWA2NFiwRwxhheHJqGZTMcHk3h4LCcBHSLIS+OFS2FqTK+4xdP8hviD7/5DF6sQ6PBUsgbcHFHzBl8bkBWUmdbWG1gA5SOMSiLoULg1AtSyZZVDC4LVU5ocpXcl4w6/Nduv7f+XaVy3PXx+3c+iV+K8RgJBbBYKAagmGghv9N1A8mSFsOgSzEARbeSdNNI5TklGvQd05S7Hiccmc5hz4lJvPHffjVDScgxd2LSFWPIFpCIBLGoM4YTwpqQ1pe0GIIVXEnyPgSAf3lgN+555gj++Kr1uOqMAQA8Q0mXDwB6hPegLRxU94VUhj/bMYjdInXWZkV3mlRsCzL4TERdRPRdItpJRDuI6DIi6iGiB4hoj/jdXS9hSyEntO54BAWbefZzj6dy6IiFEQyQGghe/fTpXAFtkeIeRx2iDkIvSNt+bBwj0zlsXNKBqWxB3TCSkVQOP9sxiId38RtR77kCeAs+j6Xy6BYxEq8Wg3Q5ye6qgLOOQU5E09mCUnLFOobieYs6YsViIKUYnDLoee0vHGlMZ9LjExkkIkEs7Yo5Juq5ZiVNZwv47H07MZ7OY1xUPYeDgaoWQ8plRdVKtqrFIBWDfL9ilk4sHFBuDckMV5I2rqZzFoYms/j5zkGVUBAJBdCXjKgYTbdUDEPTiIQCWL+o3RVjmOlKWtLJFYtMb5XXRE7UMia0T0tacASfp3P46x9tx9YjE3jiJWcxm1SIx8czjkUXX7SFsLgjhuMTGXzt0f14SizQ5D0lXaPfferwjGK46ayFxULu5w6P45zlnfjo1euxtj+JNX0JfP2xA+r9RlM5BAjK5UZESER4vyQZmB+ZzuF7Tx9Wry+/N/l9lIvtNZu5WgyfB3AfY+wMAOeC7/n8CQAPMsbWA3hQ/N8wpM9Prmi8upPG0nn1nOgsYwypnIV4WLcYwkjlLEdRzI5jfJXwhnOWAHAOfqB4Ax3XcvBlBSWgp6tWdiV1xXm6rVeFKM9z1jEEVNqvdE8NT+dmNPYLa8pkoIRicMc59Jt8aLIxWx6OpfLoSUbQ0cZz7n+99ySOj2fw7MFRrOxNFAvcZnkD3vo/O3DbQ/vw4+ePYTxdLGKSG9gkoyGlGI6PZ/CsWEmm62QxZKpaDE5/vbzWx8YzSERCym0icbuS9NqEVLagvh/pHooEAwgFA+hv5xaSdCWNp/NY1tWGvkTEsfG9VDRDU0XFsHl1j/obKFo18r1lTFDPZpNjKRIMYCSVU+muuoWrf95swZ7h3kxEg1jUEcO+oSl86t5tuONX3B3kdiU9vHsIX3p4n/N1cwXlQgOAc5Z3gkSh5PtfeRq2HpnAr/acBCA6LMcjaowB3J00nS1geCqL/vYoFnfEcGgkre4ht6VXWGjBZyLqAPBKAHcAAGMsxxgbA3AdgLvEaXcBeMvcRKyMXKHLG9drj5OxVPFmj8wyKymdsxw3nsre0CY/meJ3/souADMD0PJGPKFV7XY6LIbqVsyo+AzRUMBzUZ3KU9diDJFQoBjsFDfZycnialAOe4diaI+qXv7TZSyGqWwBfckoIsFAwxRDOmchHg6hIxbGvsEpvPMrj+Oaf34YE5kC3vvyNSpoKhcQuUL1RnCZvIXvbuGrvFSO1yVIa+70Re2IBAO4ZE0PJtIFHBlL4623/Ro3fvUJ7kqUbpI5WgzVYwxOi0G+3/HxDBLRkHKbSKZmuJK0GEPOwtAUH4dyMSDvCelOkosoAFjZE0dPIoqJTAF5y1ZxK4CP61SugNFUHmcsbkcyGlJuJTVOchbylq1k0O8N+TmWdbdheCqnrBJ3LcJUtqDuOz0BIZUVFkNnVC0SZbZiMfhcfB13/G86W8BAe9FduHFJp/r7Ny5Yhs62MH78PM8EG03x1vs6ssPq8HQOy7vb8J/vvRh9yQjOX9EFoOh+XMjpqqcBGALwH0T0DBF9hYgSABYxxo4BgPg9UAc5yyI1bne8uOOUF8a0VaAKPnvw08s4gjvGALgVAx/Qa/uTaI+GsOuEM+2wqBj474m005UU9RB8Hk/lRfA8MHuLQatj6EsWNzGXE46euy5vSrdiAPgKq1yMYSproT0WQn97tHGKIW8hFgmioy3k6Iz5snW9OHdFF+LC5Scn7Bu/+gT+8oelt7qU3L/9hLIej49nMKFZl+ev7MbWv7oWm5Z2YCKTx833vICj4xmMp/MYmswqBTtni6GaYnCtPOXvwcksVwza+OyKh2e4tqSrKhEJIpUrzPh+pKtxQCqGNpdiEJbT6HTOYWkPTWaVhbC8uw1Lu2KaYijG33R5dMUwnS2gLRxEXzKikjfkcZ1UzsJa0btKvl+uYCNn2UhGg474iMSdlQQAB0dSjvuej9mwcsVtWtqhHouGglje3abujZHpnIovSOSeDCencuhNRLFuoB3/++dX4pY3bQJQvL+KMYYFZjEACAG4AMC/M8bOBzCNWbiNiOgmItpCRFuGhmrf/EQGb6QP1GuR23iK78UAaK4kD5OrbD3sVAz8vfUMmZPiRktGQzh3RReeOjDmeB1pch+fyIieKwWVDw14sxjG0jl0tUWEK8nbRCTP0y0GfeIeL6EYTopVm1x9A1Dm9lS2gHJ1DNPZApLREPrao+rzVuIHzxzBzfc87+lzSNJ5C23hgLp2AQK+98HL8YUbLgCgVaNmeID16YOjVTtaHhCPL+3kfmrdlQSIvQrawmAMeGr/CJYKn/Teoam6xRjk914tK8kdzLRshkQk6BifvYnIDHmkAutJRpDKWg7FEKBiB1I5weqvt6o3jl6xUh6ezjnG6NBkVq3C1/QlsKI7jkMjvApcVT7nLbWajwQDjlX7VNZCIhpCTyKiFle6vABfnE3nCjhPrMJ3iopruYCJR0JKoekoV5JmMlg2w8HhYpV6KldAMhpEMhpCgIANi9sdr9GbjKr7YXQ6j+5E2PG4VAzDU1nldoyFg2r8SIUuleRCjDEcBnCYMfa4+P+74IriBBEtAQDxe7DUkxljtzPGNjPGNvf399cshHQLyEnea5Eb76zqjDF4WXWrnGhH8Jn/rd9cw9NZEPEbavPqbuw8PuEIVspzcwUbYyke4JQVlIDHdFXNYpA3531bj6sAoptf7BrED57hqaMRl2I4OZWDbbOSikEqzJIWQ7agBvkMiyHD/b0DHi2Gn+8cxD1PH/GUWfb1R/djz4lJZPIW2rQbb01fAheu6lYmfrEatYCjY2lkC3ZVq/L4RAbd8TBW9MRxYiKDQ6MpLO1qc5wjFdF0zsIVImNl3+CUWrlnC/acVoPyWo5VqWNIu1xJAHdnxDRXUm8yOiN9Vo6X3gR/TP9+9LEhA7G6j39FT1wVeI5M59RiIxggDE5mVMzgtP4kVvbGcXAkJdxsxZYYUt6NSzswNJlV4066iH7vZWvwjguX44NXrAXgzKpK5SwwBizpjGF5dxu2iziE/IwJzWLQkyzkfa67koCixWLbTAWvk7EQ1vYnHdcRgCO2MjydcxS6yveeyhQwMp1zJCpIb8D4DIthgSkGxthxAIeIaIM4dBWA7QB+COBGcexGAPfOScIq5JVi8G4xyAlQPicyC4tBrlxKWQyDmsUwPJVDMhICEeGi1T1gDKqCEnAqkeMTGceGHwBf1USCgbLB50ye98HpjIcR1SyGz/xkB778y30zzmeM4ZZ7t6lgm+5K6k9GYdkMo6mcmohOlthzQFcMMig5mSmo1Z9bsU4Ji8GrK4lPMrajgrncZ/+Le7fhu08dVvEeeeNtXNrpOFdaDFPZAvaKCaBagsKJiSwWdcSwqCOGbUcnHG4Lia7EL17dg0QkiH1D0w5feMpj3KfkZxTXstwGUsUJxkLBsh3XPhkNlbAYSqer9kmLQVsI6JOptAz1CdJtMcjY3Nr+BEZTeTz+0ggWd8SQjIawqieOVM7CyamcYwEhvwO56pfpzNNZvpi49LRe/MM7zsWfXbsBREUFeGB4GjtEBXIiGsLGJR2qIlkqnngkhPWLkrh8bS9+66IVALgbSWYjBcmpGaTFIhVLMhrC689eop6r05uMYHgqB8b4/dLtciUt6WzDSyenUbCZI7VZLh6Llp7MSlp4riQA+CMA3yCi5wGcB+BvAXwGwDVEtAfANeL/hmHVEGPYdnQCNtNWQyrGUP1LSuVKKQb+pZ/QqjALNlPdFs9f2YVggBxNuIYms0ohyYGkxxgAnjlULiBerNyOICZiDLL0vpR/e+fxSRwcKZrMelZSfzu/DkNTWS34PHMijwhXUjwSVMpwOlv0F7s7vCrFkIxieDqHvHDDuS0L2Z5ZBhqrKRE9yyWdtxALB5VS1X3CQLHoaCpbUJlh1RTPiYkMFnfGsLgzpq7lugGXYtCU+KreONYO8L2/9Wvvtfq5lFXoNficzlsz+iDFI0FH8Lk3OdOVJBccPYkIpnMFR9uLiLZokCtv/fVWdGsWw1RWLUpetq4PAN84ae0Ab1G+spf3Jdo3NKViEem8pTKSpGKQVsZUpuBItSUixMNB9Rk/8l/P4g++8TQArvQ3Lu3ASyenkcoVVIA9EQ0iHgnhm++7FK8+g3sj9JgLaYphaWdMjQv53SWiIXzsmtPx3lecBje9ySjSeQsnJrKwbDbDYrhwVbdyD+kWQygYQCISVJbeQg4+gzH2rHAHncMYewtjbJQxNswYu4oxtl78nrlrRh2RF7ZTWgweXElfengf2qMhvOncpQB0i6H6jewurwf04LOz2EZOSvEIX9k8qxXHDE5mcKbwX+4Rzb70yQYQu7iVWXXKtNIuYTEwxl/HZqW7V96/zbkjlh5jGOjgK5vBiaJiKOWSkxZDPBJSN+94Ou/oD6XDV38hZV0MT+Xwf3/wAt562yMqfTRXsPHqzz2E23/1oqrUra4Y+HVOZS3lSpL+3HOWOS0GKedUtqBcBl5cSYuFxSCZaTE4g7Fr+5MOVxLgrfp5x7EJnHXLTx37BwDOOoZSrjW54uauPOf7uIPPvYkosgXnfglyjPQmo7AZcGhUWzRoY2NFD3eh6TGWRDSErngERNzKk26pzat6EA0FwFjxeq3sSajPqd5bsxg2Lu1AOEhq1S4XEzptEd7ltWDZ2HZ0QiV5JCIhbFraCcZ4erisrI5rbt5FJRSb7kpav6gd/ytSnOW4kFZmKaSlJHtYuRXDRSJFl58bdTzW0VbcQlguGuZjq9RaaPnKZ3lhE5EQIsGAY0KzbIb33PkkfvLCMXXs0EgK/7P1GN592So1Ec8mxpDSAlySYvDZOaEltAHel4w4Vn9Dk1nl9tgtBpnungB4ADqTt/DcoTG8+47HHUpC7/Uk5Xf7WnV+tuOE2rmKf2anKwngE2IlN0tYvE9CBOf4Zy4qQ7d1M5ktIBkrKoahySyePjCG7ccm1C5jj780jGPjGew8NlG0GKoEqnWLIZXjiuHiNT345vsuwWVrex3nymrU6WwB+wb55OOeJHXylo2TU1kMdMTUarkjFlKKR6Lv89uTiGDdQBJHxzOO2IwXi+GFI+Mo2GxG2qScbAs2K9kZVU4w3PXmVHRJLV01GCCl+OX1/dh/PYt7nuaNHeVEd2Iiq75TPcawqjeB73zgMrxm0yLHewQDhO54hLuSxLVMxkLKApAW1vLuNhAVFUM4SEjnbLWA62oLY3VvQiltXofgvA8SUZ459eLJacf3loyGsFFYiO/72hb8u3CTJrR7c7FyhTkLOiUff83pSOUs/P6dT6q2/aUymiSyV5ZczLnTVRd3xpQy7S0xZorBZ2kxLExXUtORFzYYILTHQo4Yw0+3HceDOwfxjw/sVsf2DE6CMeCajcWBPpsYg/Rj6isy2XDsmOgkKS3Vdm2AxyMh9dxMnreKXtYVQ28iolaLMyyGUBCZvI1f7BrEr/acdLiCVP+eeFExyLbAcjU4NJnF39+3E1PZAl4cmsIFK4tF6O7gM1C92Z9uMchVla4YdIshL3b0SkaKiuH4REZlBMmb+Kdit7GXTk7PqJ4th57+mM7zGAMR4fK1fQ43ASCqUUUfrX1DU2pSKGc1DE1mwRifHBZ3crnXDiRnvK5cQa/oiYOIsLybTwb7hqaUAvZiMciMGHfLB/1ajqfz+MkLx/DIXl5YZdkMk9mCcmfqNScAX5DIhUssFMCAdBVOZpHJW7jnmSPYfmwCRMDla/vU89aKyVzPPgP4KjgcDOArv7MZX3/Pxep4TyLC40L5YqabXDFLiyEW5oFgWfDZm4gik7fUfSqDvHLsuV1JAFfuqZylFj6SeDSEpZ0xfPTq0xEOkio8i2sr/p5EBJFgwHG/SgMsFg7gnOVd+KMr12H7sQn1+u5EAx052ct4lTtdFQAuWtXjOFfS0RbCzuOT+Kf7d6kF7ELMSvIF0mIIBwnJWEitRBhjuO2hvQCAszX3gvSl6ulsoQBvzzybrKS4q7J0WVebap0tJw3dJOVuIf76clXZ3x7Fip64Wi26YwyxcBAZETcAnL3nZZfT7nhEZYzIKtHprIXxdB4v+/uf498f2oef7xzEdM7CeaLYDnC6CxIiWOmuzgaA97/yNHzgVTwzRMYYktGgcpOVsxjkiigRDakMpmcOjiJn2VjZE8dzh8aQyhWUi0uv8yilGMZTebwgtoSUimE0xauz3ZkjbpLREE5OZTE8ncNasT1nuTiDLJZa3BlVbgi3GwkouglX9XAfumo4lymoCcHLngzyu5VutP0np7F3kGdbyXE0nsrjg994Gr/9FZ4AKCdVGSOTxWlRzaKTFkMsHCxabFMZx+IiFuI++tUiDiCVm75o0Ll64yK8Yn0xg7An4bQYoqEA3nTuUlyypgdnLy/ecyt64ipA3NceUa6kAPFJf+1AAgeHU2q7XbdiSET5osq9H0IyyhcEH756PV69oVgupVsMRNxi0l1JHW1hXLy6B19614UAoPbyfvTFYRAVr2spepXFUNqVBABvv3A5rjpjYKYrKRbGgeEU/vXne9WxhVjH4AtkulcwQI4OqwdHUtgqNlTRc/zlpKO7BogI0VDQU0uMUsFnoLjKkJYL4HQlxUUhEVDc4KQ3EcXGpR1KuektMQAeuxhN5dWqUq/wlHsxdGkWg7z50nkLP3jmiLKAtoo+RacvKuZkR103f397VAUApaUTDQVw8+vPxCdedwYAp8UQDQURDpKSKRQgxyp3UlsR9rdHEQ6Sch9dvIavqJ47NI7BySy64uEZufBu7vjfF/G2Lz2CbImNYtzfhZtkNIQD4hqu6UsI+UpbDLIj56KOGAbauUW3edXMdl/BAGFNXwLnCteJ7n6Q7gYvWzweGClaDNuPTuCKzz2Ed37lcWTyNhYJF5Cja6zNlDtCvqe8XnJC01ti6IphcCLryNmXCuCWN/Piq8tO4244PSupEr3SYhDfeywcxIbF7fiv91/msH7X9ifVGO8TGXAjqRySUZ61d/ayLhRshhu/+gSyBXvGip3fOxa2H5vASqGIAef9pSvvuCtGsGlph4p1APy7u/sDl+EKoUzkaz750ggWtccc2XelPjMA7BH3SinFcPm6Ptzxuxc5XFaAc/8UibEYGkRBWQy8cZhcTen+fv0GHZrKijYSzsETCQU87ZssUxDdvWiWdRWDXHJ1oruS2iLBGW0nuuJhbFxSzKJxWwwbFrdj9/FJ7Bc38wmHxZDnJnI4qD5LKmepwajvnCVX2nqpv7v3TH8yqt5HTjDuCVcWPUlLKBkNqevck4i42iwUU/+ioSAuW9unFM+FYqKV7oOztBTTYIBKxhiOjmeQK9g4MJwqKgZRre1u/+AmEQ3hgOgUu1ophioWQ0cMkVAAj9x8Zcm0RQC4/6OvxPtfyTNX9FVmUTFUtxhk8dfodA4fFnsTnJzKIZO3lBUyru3EdmAkpcaPfE9pBUtFoQefo+GAWgQNTWaVIuKvy1/n1RsGsPNvXotXnc6tgXIWgxvpSpLfu3uxIXn1hqKVIVfRQ5NZFZu7dtMivPOSlXhk3zCuPnMAv33JSsfz45EgUlkLu45P4rLTetUCSo/zySwoAI4+ZgDwxd++AJ99+zllP4dUDNM5C0u7ylsLAFd+iUgQY6k8YuFA1UWJjqybkoo6FKCFV8fgF2QesFypy9WUzBDqSUQcWTpDk1n1xeh4bSuR1nKldeQqpy0SVK4NfUXTJlxJ+oqvoy2sgmfAzBjDxiUdSOct5XrSWwyPp3PojIdBRI7A2gXCXXR8IoN4hBeXbRUmuP653TexDFACwBLxWdyfMazSVYsWkXRv9SajztYCmaJiAIDXiJhOXzKCVcJ1IXv7n7mkaMms7o2XtBhkUdGeE8XN7mVGmltJu+EN77g8a3orWwzHJ/imNN2qKj44I74gCQcDjj26i432ZLsQvdVCATfc/phqwSxlkLGFwcmsUpTxCG+KKF9Hd3ttPzox02IQ10Z2M3W4kkJ84dDZFsbQVFYpIjexcFBl9kVC3ia73gTfDEha0e7FhkR3P/W1R9TnlWODiPA3152Fr7/nYnzxnRfMWLHHIyGMp/M4OZXDsu42lQKr74kiLYZoKKAWMJJQMDBj9a7TFilaVZXiCxLZUuP1Zy8pOzZK8dm3n4Mf/dHLcb1YaBRstmDrGJqO1LjhQACdbRGVrSEnl1W9cUcQcGgyq7JwdCKhgMfg88x0VUBTDOGgqlpOxpwWA8CDirLIpSMWxpmLO5Sv1b1S2+jKyT8+Xpwwx1J65XZRFrnqOzGRQXsshEUdMbU61hVDyHWjvO2C5ervJSXaIABFF4O8IZPRkLKC+pJOi2FKizEARcVwWn9STbqyt/+ZmtW0YXF7ScUgJ9An948gV7AdsnmJMUhW9VaOMRw4mcKKnrijbYJX5EQtJz99/+Bdxyfx6IvDuH9bcd9f6e8nAnYe57U1A+3RGd+XvrfytqPjalc3OebkwmFRZ9FiCAYIkVBALRpkkeGB4WksKzP5JSO8DYRXV1JPIgLGinGmWBmLQVfcfcJiODmZVS5XgBd0vmJ9/wxLHuDjUFpy/e1RrOpJIBZ2KoDl3XFEgoEZGU1ekbGiZd3VFYMMHH9QxN680puM4qxlnfjQq9fhj69ch7eev2xh1jH4AWUxBAk9iTBGtFz4UICwtKvNaTFMZR2rY4luMTx9cLTs7lHpHN8W070CkQMqHgkipk2cEjmRpXKWZjFwk39NX2JGqioArB9oV6v07ngYJyYyeObgKKayBdUOA3AWq8k0wRMTWXTEwsodIdMLP3/9ebjyjIEZK52rzlyEf3j7OXjZul6VbhePui2GgOO4/vl6ExFHOq1UDPLmH+iI4Z2XrMSbz12q5JYZSlIBBoiv/Eams8onvefEJAYnM6oS+7+f4y09ztGCm15cSZI1VVxJ+4amSgabvSAn5s62MCLBgMNikJOnvm+wXL2vH0iqz6crSekC0gP8249NYFQoBjnBD07w9isy+0h+L3HNepVtSQ6MpBzXTicQIHRo6c/VkKmaMhuvnMUAAHf+3kV45en9WNJVjIskY94mcX0R0J+M4nVnL8abzlnqOEfGfGbj2tGR7qRySlPnE687A7/3stVYr8XsZkMsHMTHXrMBXfGIb+sYalOvPkLGGEIBQrfwc6fFxiN9ySiSkRCmc3xnt5xlY3CinMXAzXfbZnjrbY8AAJ76v1erLASJe/c2iRxQsXBQrZz07Ah5g6ZzFibSeYQCpCa0y9f2ldz0PRIKYP1AO7Yfm8Dm1T14ePcQfuO2R3DDxSsxJvriA0W3UFs4qHo4HR/P4Mwl7SrlsjcRQTBAuO68ZbjuvGUlr+U7Nq/AOzavwF2P7Acw01er6hjE55c7YLWLIjbdFTftshgA4NbfOBtAsar30EgKsXAAq8UqvjsewaKOGGzGJ47FnTG892tbsHlVj4onDE/n0NkWxqtOH8BjL/LayWqupPZYcaIstvIoEQi0bOwfnsZVZy6a8ZgXFonXjoeDiEeDGJzMiP21g2py33Z0AtmChQARth2dQIB49e9ukRe/cWkHHha9rnjPo4BDMew5MYWL13AlIi2GoaksEpEQ1g0k0RYOqlhSW7ioGPrbo3j64ChOjGdxzZmL0B0Pz8jBB7iSWu5h1QwU4wWy4LCSQrliwwCu2DCAh3bx1mm8C6pXxVA8r789inNXdOGNLsUAAJtXd6skg9ki3VNLO6t/9g/M0lIoRzhIvq1jaH3FYBUVg8wYGEnllGXQJjIaPvmDrfjm4wcBoEKMwXJkgHz6xzvwz791nuM82WTLTX+SZ97oqzR9RSSVSTpvqd3a5Kr9ljdtRLl1wznLO3F0PI0zF7fjAZHVc3Iqi/FUTrV/kOb3W85fpibtdJ63D5bujVJWUjnkROuuAHXHGJIiJvKFd17AU1GFYg0ESLUnSJa4VrFwUFloPXHeHbZLTFRS2R0dT2NxZwyDE1m8cGQMmbyNoNiC9KozBhyVuNUtBv54b5Irx0QkWNJiODSaRt5iKqV1thSD9iF0tYVxz9NHMDSZxdffc4lyhRwYTuE3vvgIlnTydtSbV/c4Mm10iyEWCiIZDavnrhRN/WTgU3b2HEvlMdAexSvX9+HZW65R46ErHlHXqT8ZxaERvrJf2RvHE5+82tF+WvLN911a8ngpZEbO0fE0ggGqmM0jcXYMCFc4s4i+EKs0jv9KZFfVgkxZlfGv+UDfa91vtL5isIvpqtJ3PTqd4yvOjhgS0SDSOUspBaC0YpAxBn2D88OjM1cf6Xyh5Ao1ECAs7oyJVRq/QZKu4DMgLQbnbm3uYJnOn1y7Ae++bJWjnUZHLIxRLcawbiCJb77vEly0uscR3JQxBgAlraRySEunzTWpR1xZSZ9569kYmc7hrGWdqjgoZ9mIBYIq+FyuvUB3PMK7mIrJZXFHDB2xsFoFHx1LY9NSHnyXqYFnL+vEs4fG8JpNix2B7moxBmm1yBVueyxc0mKQdRxrB2p0Jcn2C5EgvvDbF+CT339BZVDpGWXbj00ol9JfvHGjGi89iYgj7TUWDqI9VgzwrxKdSo+MptHVFnFYpDL1U/fR/9sN56sxqI/5azctLjuJe5ncJbJe49hYxrP7Sb93LlnTU+HMInGHy7L8OK50H1Xj9WctRt/7LqnZPVQLwUAAls3AGJtVEHs+aPkYg2XbCAUIRORoBTwoso/ikRAKNnOkhVbKSpLZL3pNhE45VxIA3Py6M/GeV6xRSiDpSleVzx93bcpTib5kFJuWdjomjOMTaaTzlmNXrcvX9iEcDMxo1aEUQ4nPXA75+RIRt8VQrGMAuCvjLFE8WNw/QuwPkCvMCBDqSNnld/bx12zAh65cp9IFj46llfUmK1Xffekq/PGV6/DqM/odn7OqK0l8D9Jn3x4L4fnD47jd1YVWVrPWGmPQ9y44a1kn1g4klXvt+ERGuWgWd8SUe+s1Gxep6tmlXbEZu/gloyHVG0haFnsHp9AVDztW36WCrusGksqKkcrzFev7VLbTXJHuyXTe8q4YNJlffYa3PbykS7M7HvacSjtbQsGAowp8PgiLOKUfrYbWtxgspgLBcvV5ciqr9lyVk5yemVROMQxrFsPqvjhGp0tt9m6VXaG+/my+v/PDu4o+Yom8ITLSleTRjJacs7wLF67qxsmpLPaf5JZMZ4lyfH0y74iF1MRQi2JwT7ir+xK4eE2P6oejo/aoFplJgxOZiqs7qRiklae3KGmPhXBkND2j5fT6RUm87UKePaV/Tq/B56LFEMLTB8fwt/+zE7910Uo1Ge8bnEJ/e9QxOc+G81Z24aLV3dgk6jKioWK1+4mJLM5d0YWNSzrwhnOWYDydx7OHxrCiJ64quZd2tjmUvbQYZIBSKoaXTk7jglVdjsypc1eUDihLrtjQjwtXdeNvRZynHoSCASzu4C6xUtlEpdDvHa/XWVqdsxnDrYBcNBUshipDeN5pfcVgM7WSlSuvfUO8y2h/e1StZGSDrEQkWDLzIBriexpIi2F1bwIHhmdueJPOWTN6oMx4rRIWg77F5EQ67ynIpdPfHsX3Png5PvztZ3Dvszwzpzs+88Zy70W9rKsNHbEQNizumHFuOaSsbsuosy2Mu99/WcnnFBsRcoth79BURZeMVAilKkeXdbXhyFhmRstpPREgXkLplkN+D/J7OzhSLP6b0HZnOziSUu0haqEvGcV3PnC5+l/GrRhjOD6ewZVnDOAv3rhRPf474lLKa7C0q62kxSCR/u+cZavrJ4/pr1uKVb0JfO+Dl1c8pxbk1p16LU0l5OLtI1ev9/we0qW50BSD2o/cttEGf2mG1lcMlq0shs62MAIE7DrOXQL9yagy03IFG++6dCU++fqNJV0P0VAAOcvGyakciPjqbCpbUMFUSSpXwIpI5Um9qy2MUIAcKagqxiAa6JVKT/WCvjFIV9vMSdXtSkpEQ3jik1d7NvWBYkuBUkH2cugWg20z7BucxkUXl/chuy0GnaVdbQ5XkqRXUyK6xVDtsxUVA59Y+pIRlfs/lspjhRBzMlOoWvk6G/iWq3zjoXTeKtu1sz8ZRYB4cV9M1LPkCjaioaAjgUFv6yCv330ffiWioUBNdRf1gMeERj1bDMloCDv++rVV3X868rueTZysFZDzluXDWoaWjzFwi4Ff4IAIQO88zgN7uisJ4Iqj3IDkLTFsDE9n0R2PoDvOi3fcHTLTOQtt4coT5m9etAJ3f+Cykn7wdK4wY7e22aBPjl0lLIZggNREKf3YsXD56t1SJMpYDJWQ75nJWzg+kUE6P3PXM50uZTHM/AxLu2I4Op52bGuZdG1XKd1DsXD1SVFOrvLa/cfvXYS/vo5nsOjKp1QDt7kQFRO8DB4vKtOcrTsRwd3vvwy/dRFvBSGthlg46GirIutLgOL1a4sEm6YUgGLKbNSjxQBUjwmVO3+hWQzSlZT3YfXznBUDEQWJ6Bki+pH4v4eIHiCiPeL3zA5kdUSPMQD8Jjs8yl0Fa/uTjkFYaoUtkRbD8FQOvYmImlT1ALRls7IFcjrJaMjR4hooDu6xVB7Zgu05+OxGzz0v56OVk6bXdEA3izqi+LPXbsDrzlri+Tly0s4WbNXaoaJiELKXyqVf1hXHWCqv3H/Lu9tmuO9U5pQH5+zGJR0iaM2DnUs623DJGt4wzq0Yar1mpZCTpaxwXlRhYtu8ukeNkS6lGAJKqUWCAbTHwkpxlXIjNgNVv+PRYqiFxEJ1Jcng8wK1GD4MYIf2/ycAPMgYWw/gQfF/wyjYDKFA8WPIOMP6gSS6E86UvkrBLmUxTPFNvOUEoSuGY+M8z31Vz+z90HICk/2O3J1UvdJTxWLQ36u9xvcgIvzBFesqth92I33Mzx8ew5NiC1P3dpg6KsZQ0pXE31emdL7r0lV4w9lOJSUnUS+KIRQM4GOv2eD4/lVLa5X5xDCZyXuuxvWCnCyPi8pgd7FkOTq1VidyHOq1GEDlRc58sqwGi2G2LO6M4WXreuc9a6jR6MFnvzGnu4CIlgN4A4BbAXxMHL4OwBXi77sAPATgz+fyPpUo2DZCQd1i4DfSRSJHWneHVFqly+DzyekszlzSoVkMxRWlXPmtrEExyN41shtpzRaDmEhlm/FSyEmkVndVLUgf86d/vEO898xdz3TWDiQRDhJW9c0sJpNpnduOjKM9GipZaRoJBWZswDIb5OQrt0jNFmzkLVZfV5KYLGULC6+uuU7dYog6W7j3JiI4MJwquyiYb5a6qu8bQSwcxDfee2nDXr9ZhFS66sJzJf0LgD8DoH+yRYyxYwAgfntLVq4RbjEUFYNcUV+0mrty9OyVahaDzXjfmb4yriTZy35FDYoB4BODbG9Q66QtP1+XVjntRmZx1Gox1IKelRKPBHH6ovaKcY0LV3Xjhb+8tmSG2Gl93NLYP5yqqEDj0WDNiiEW5opFWgzu3k71QCpLuQmPF+sGcMUY5N4eEWcAvauEpdUMpHVXqU+SoTRyQevHOoaaFQMRvRHAIGPsqRqffxMRbSGiLUNDM9NCvVKwbIcrSa6oN4vt9fR+P5VWWXLFM5UtoDcZVSb8Q7sG8ft3PolcwcbBkZRqzFcLbeGgCkTWnJUkLKLOCp9FZnE0w2JoCwfxgw+9DJ95W/n+95Jy9SDdiYhSgJWUeSIS8jzZuiEidMaLm7O724TXA6ksZTdUr0qsMx5WLSaKFgN/rrTC/BJjaI+F0R4LNdRiWKjIecuP/ZLmche8DMCbiej1AGIAOojoPwGcIKIljLFjRLQEwGCpJzPGbgdwOwBs3ry5ZpVp2czhSnrrBcvRFQ8rd4S+m1M1i0HSm4yoGMAPnj2K8XQeu09M4sBICsu72yr2dq9EWySo9iCoddKWiq+r0kpadnedR4thWVcb/vjKdfjNi1Zgeffc+82s60/iiemRispc70tVC51t4RIWQx2Dz5rFECDv7pbfumiF6t0z05XELYZKC4P55pY3bVJdaw3ekdmUCyrGwBi7GcDNAEBEVwD4E8bYu4joHwDcCOAz4ve9cxezPHnL6UpaN5B0BD0jYpMOy2YVFYOeh336onY1QciJY/vRCRwaSWFlb+03gL66lS2SZ0s4GEB7LFTRlRCPhJCIBGtWYLUQCBA+9poNdXu9tQMJPLF/pOJ3du2mxSWzmrzS2RbGWCqPpw+OFrcirXO6KsBjDG2zSBk+Y3EHzhAFiXIcSrletq4P246OV6wqn2/efuHy6icZZhA8xVpifAbA3UT0HgAHAbyjAe+hsGxWcQIk4h1P0xV6HAHFQFCPtsdvKFDsfrj92AQODJfvZe8F+f7LutrmtOJbP5Cs2AF0VW8ca2rsEOoXZKprJcXwJ9fOTRF1toXxzMFRvPW2R3D1mTwUVs8Yg7RmxlK5GQ0JvVJsGc5/X7a2F5et7a2PgIamElZZSQvLlaRgjD0Enn0ExtgwgKvq8bpe4FlJlU30eIS3ea64YhMPffTq9eq89lhIZZQ8tGsQ4+m82jugFuREobdWroVv3XQpghU+y0euPh1/eOW6Ob1Hs/GiGOZKZ1tYfb+yO2wjLIaxdL5mhSPlSZbpUmtoXUKnmMUwrxQsVrXjYiISUhN/Od5y3jIs6Yzh5euKudLtor11OEjYP8wDz689a/GcZd60dG6KoVr7gWCAEAy09kQi3YG1pvV6QVc6ch/pumYlyeBzOo9FNboOZZyo1i0rDf5FVT770GJo+VSCgs2qWgxtkWDVlWckFMAr1vc7rAo5SUhl8ebzls4psCq3cnTv5WyYyfLuNvzptRtmbOFYT0qNiUYUuDEGtd3rbAkHA7jlTRvxG+eX3nXP0LpIi8GP23u2/DKkIPZjqMTVZy6qqTJTKoZ3XrIKI9M5/OGr5+ae2S/qIDbO0ZV0KkBE+NAcr3c13IohEgx4bgbnBX3MubdJnQ2/97I19RDH4DNkNmV+IWUl+YWCKyupFB+95vSaXltmhFy6thdXa/sF1Mpvbl6Ou7cc9rynrqGxSMXA995ldU/v1ZVMrYV4hoWLCj4vwMrnplNw1THUk75kBIs6onULSP7dW8/Bzr95re+28TtVkU3ZZA+eegaeAWc1uFEMBjchHzfRa3mLwXI10asnH7n6dLz70tV1e72FEBReSLx8XR/u+v2LcXIyi4d3D9W9hUhEi33VWqFtWLjIecuPWUktbzHkreoxhlpZ1BEzgeIFTCBAeNXp/aqLbL0thlAwoMbmbPa2MJwaqF5JJiup/rhbYhgMs2WR2FmtEU0HZS2DsRgMblTw2VgM9SdvMQQb5EoynBo0ymIAikWNJsZgcBMO+LfyueVnVMu2VTMqg6EWktEQuuNh9DSg/5CxGAzlCAZNHUPDcG/taTDUwtd+/5JZ7VjnFblPgYkxGNyEVdttoxjqjnujHoOhFs6eQ3PESkiLYS7twQ0LExN8biBemugZDM2iaDG0/BrMUGfkgtYEnxuAsRgMfkbFGCItf6sZ6gwRib1ijMVQV2ybgTE0rMDNYJgrKispbCwGw0xCAfJl5XNLz6h5oWlNHYPBrxQtBhNjMMwkHAz4Mvhcs2IgohVE9Asi2kFE24jow+J4DxE9QER7xO/u+onrRKZ5GVeSwa9IxWCykgylCAUXniupAODjjLEzAVwK4ENEtBHAJwA8yBhbD+BB8X9DkJrWpKsa/ErRlWQUg2EmoQAtrOAzY+wYY+xp8fckgB0AlgG4DsBd4rS7ALxljjKWRVoMYZOVZPApxpVkqEQoEFi46apEtBrA+QAeB7CIMXYM4MoDwEA93qMU8oIai8HgV+SeDMZiMJQiFFygwWciSgL4HoCPMMYmZvG8m4hoCxFtGRoaqum9C8piMIrB4E/kngxGMRhKEQ4GfNl2e045dEQUBlcK32CM3SMOnyCiJYyxY0S0BMBgqecyxm4HcDsAbN68uaYrU1AxBuNKMviT685bhp5EBAFj1RpK8OoNA1jmwx0da1YMxLchuwPADsbYP2kP/RDAjQA+I37fOycJKyC3xDMWg8GvbFjcjg2L25sthsGnfOpNG5stQknmYjG8DMC7AbxARM+KY/8HXCHcTUTvAXAQwDvmJGEFpAlmYgwGg8FQP2pWDIyx/wVQbka+qtbXnQ3SlWQqnw0Gg6F+tPSMKl1JpsDNYDAY6kdLK4aOWBhvOHtJQ/roGwwGw6lKS3f2Wt2XwBffeUGzxTAYDIYFRUtbDAaDwWCoP0YxGAwGg8GBUQwGg8FgcGAUg8FgMBgcGMVgMBgMBgdGMRgMBoPBgVEMBoPBYHBgFIPBYDAYHBBjze8FTkRDAA64DvcBONkEccrhN3kA/8jkFzl0/CSTn2SR+EkmP8ki8ZtM5eRZxRjrr/eb+UIxlIKItjDGNjdbDonf5AH8I5Nf5NDxk0x+kkXiJ5n8JIvEbzLNtzzGlWQwGAwGB0YxGAwGg8GBnxXD7c0WwIXf5AH8I5Nf5NDxk0x+kkXiJ5n8JIvEbzLNqzy+jTEYDAaDoTn42WIwGAwGQxMwisFgMBgMThhjdfkBsALALwDsALANwIfF8R4ADwDYI353i+O94vwpAF9wvdZDAHYBeFb8DJR5z1sBHAIw5Tr+MQC7xWtPi7/nIk8E3Me3G8BOAG8rI8+FAF4AsBfAv6LoqnslgKcBFABsrdM1qlkm8V3tAZAFkBGvsapJ1+YD4vg2AGMA9jXz2rjG8mEADMDnmijL7wIYEtdkEsAxn1yfrdr4eaqJsvwz+ByxDUAKgOWD67MSwCPi+8qA12g1U55VAB4E8Dz43Lq86nxe7QSvPwCWALhA/N0uPsRGAJ8F8Alx/BMA/l78nQDwcvCJoZRi2OzhPS8V7+tWDK8GcBqACwB8EMD35ijPXwH4tPg7AKCvjDxPALgMfPL9CYDXieOrAZwD4DsA/rRO16hmmcQ1uwlAXMhxQjzWjGvToY2fjwC4r5nXRpPl5QB+Kc450ERZfhfAF1Df+2uuMl0OPkF1C1n2NUsW1/zz9wC+6oPrczuAPweffzaK8dNMeb4D4Ebx95UAvl51bq12Qq0/AO4FcA34yn+J9uXtcp33uyUuxEPwoBi086cqPHY+gF/PUZ5DABJVZFgCYKf2/w0Avuw6504Ab6/TNaqLTNr13uoDOW4A8BM/XBsA/wLgjeLaPNwsWUq9ZrOvD/jk9l4/yOI67xEA1zRbJgBfBvDn4u/LhFzNlGcbhJUArjQmKr0WY6wxMQYiWg0+IT8OYBFj7BgAiN8DHl/mP4joWSL6CyKiOYjzHgCP1ioPEXWJP/+GiJ4mou8Q0aISpy4Ddz1IDotj5V53tR9kEnJcCOD7zZKDiD5ERPvAJ5w/bva1IaLzAaxgjP0IQAzAhmbJIngbET1PRN8lohXNvj4ATgdwOhH9moieAZ/8mj2OVwFYA+DnPrg+fwngXUR0GMD/APjbJsvzHIC3ib9/A0A7EfVWet+6KwYiSoK7bj7CGJuo8WXeyRg7G8ArxM+7a5TlXQAuBjefapUnBGA5gF8zxi4AVzKfK/V2JY6xMnLN9RrVRSYhxy8AHAXw6WbJwRj7ImNsLbj5/Zdo4rUhogC43/rj4vpsAvCPTfye/hvAasbYOQB+BuA/0fyxEwKwHsAbxN8B1DaX1PPeuh7AdwG0ofnX5wYAdzLGloNPyN8B8NEmyvMnAF4llPirABwBj3eWpa6KgYjC4F/KNxhj94jDJ4hoiXh8CYDBaq/DGDsifk8C+CaAi4koKCyIZ4norz3IcjWAT4IHn/9zDvIMgwe1vi/+/w6AC0rIcxj8C5QsB59w3QQx92s0Z5nEd/UL8DjDKxhj2WbI4eK7AH4Tzb027QDOAnchDYFbDB8los1NkAWMsWHx3QDAf4D795s6dsRjPwLwX0Km58AVRTPHz/UA7oYP7i1wL8Xd4h77c/F6v2qWPIyxo4yxtzLGzgefE8EYG6/0pnVTDMLdcweAHYyxf9Ie+iGAG8XfN4L72iq9ToiI+sTfYXA/71bGmMUYO0/8fKrKa5wP7ufbCeC5ucjDuGPuvwFcIQ5dBWC7Wx5hFk4S0aXiWvxOmdf+IOZ4jeYqk/j7HvCb+eWMMTk45/3aENF68ZvAA88jzbw24obpBw88fxk8PvVmxtiWJl0fOYnI63PSB+P5BwD+D3h23dfAXUsvNkkWENEG8ED4+9Dke0s856B43h0AjoNnbw01Sx4i6hOWMADcDB6grwyrEoTw+gMeUWfgKVHPip/Xg6dhPQienvUggB7tOfsBjICnaB0Gj9onADwlXmcbgM8DCJZ5z8+K59ni91+K4z8Tr8sApAGM1yqPOL4KfKJ4XjxnZRl5NoMHcfeBZ5LIdLGLxOulhUyZuVyjucqkfVd5IVMawGNNujafF9/zHiGTnqbcrO9LH8tTQqZmyfJ34vrs9eH1yYCPnf3NkkU89pfgLrY5zz91uj4bxXPl/LOvyfK8XbzfbgBfARCtNp+blhgGg8FgcGAqnw0Gg8HgwCgGg8FgMDgwisFgMBgMDoxiMBgMBoMDoxgMBoPB4MAoBsMpCRFZojhoGxE9R0Qf03K9yz1nNRH99nzJaDA0C6MYDKcqaVEctAm8udnrAdxS5TmrARjFYFjwmDoGwykJEU0xxpLa/6cBeBJAH3hB0dfBiy0B4A8ZY48Q0WMAzgTwEoC7wHvefwa8KjUK4IuMsS/P24cwGBqEUQyGUxK3YhDHRgGcAb7Bis0Yy4iWHd9ijG0moisA/Alj7I3i/JvAN5H6NBFFwdtnvIMx9tJ8fhaDod6Emi2AweAjZIfKMIAvENF54DuCnV7m/NcAOIeI3i7+7wTvP2UUg6GlMYrBYIByJVng3S5vAd/V7lzwOFym3NMA/BFj7KfzIqTBME+Y4LPhlIeI+gF8CXznLAa+8j/GGLPB9wIJilMnwdtyS34K4IOiCzCI6HQiSsBgaHGMxWA4VWkjomfB3UYF8GCzbNd8G4DvEdE7wPesmBbHnwdQIKLnwLdp/Tx4ptLTotXxEIC3zI/4BkPjMMFng8FgMDgwriSDwWAwODCKwWAwGAwOjGIwGAwGgwOjGAwGg8HgwCgGg8FgMDgwisFgMBgMDoxiMBgMBoOD/w+WlCbTX6V8BwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='Fire'].groupby('Date').count()['lat'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='EMS'].groupby('Date').count()['lat'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='Traffic'].groupby('Date').count()['lat'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Now let's move on to creating  heatmaps with seaborn and our data. We'll first need to restructure the dataframe so that the columns become the Hours and the Index becomes the Day of the Week. There are lots of ways to do this, but I would recommend trying to combine groupby with an [unstack](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html) method. Reference the solutions if you get stuck on this!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>275</td>\n",
       "      <td>235</td>\n",
       "      <td>191</td>\n",
       "      <td>175</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>372</td>\n",
       "      <td>598</td>\n",
       "      <td>742</td>\n",
       "      <td>752</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>980</td>\n",
       "      <td>1039</td>\n",
       "      <td>980</td>\n",
       "      <td>820</td>\n",
       "      <td>696</td>\n",
       "      <td>667</td>\n",
       "      <td>559</td>\n",
       "      <td>514</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>282</td>\n",
       "      <td>221</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>204</td>\n",
       "      <td>267</td>\n",
       "      <td>397</td>\n",
       "      <td>653</td>\n",
       "      <td>819</td>\n",
       "      <td>786</td>\n",
       "      <td>...</td>\n",
       "      <td>869</td>\n",
       "      <td>913</td>\n",
       "      <td>989</td>\n",
       "      <td>997</td>\n",
       "      <td>885</td>\n",
       "      <td>746</td>\n",
       "      <td>613</td>\n",
       "      <td>497</td>\n",
       "      <td>472</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>375</td>\n",
       "      <td>301</td>\n",
       "      <td>263</td>\n",
       "      <td>260</td>\n",
       "      <td>224</td>\n",
       "      <td>231</td>\n",
       "      <td>257</td>\n",
       "      <td>391</td>\n",
       "      <td>459</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>789</td>\n",
       "      <td>796</td>\n",
       "      <td>848</td>\n",
       "      <td>757</td>\n",
       "      <td>778</td>\n",
       "      <td>696</td>\n",
       "      <td>628</td>\n",
       "      <td>572</td>\n",
       "      <td>506</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>383</td>\n",
       "      <td>306</td>\n",
       "      <td>286</td>\n",
       "      <td>268</td>\n",
       "      <td>242</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "      <td>402</td>\n",
       "      <td>483</td>\n",
       "      <td>620</td>\n",
       "      <td>...</td>\n",
       "      <td>684</td>\n",
       "      <td>691</td>\n",
       "      <td>663</td>\n",
       "      <td>714</td>\n",
       "      <td>670</td>\n",
       "      <td>655</td>\n",
       "      <td>537</td>\n",
       "      <td>461</td>\n",
       "      <td>415</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>278</td>\n",
       "      <td>202</td>\n",
       "      <td>233</td>\n",
       "      <td>159</td>\n",
       "      <td>182</td>\n",
       "      <td>203</td>\n",
       "      <td>362</td>\n",
       "      <td>570</td>\n",
       "      <td>777</td>\n",
       "      <td>828</td>\n",
       "      <td>...</td>\n",
       "      <td>876</td>\n",
       "      <td>969</td>\n",
       "      <td>935</td>\n",
       "      <td>1013</td>\n",
       "      <td>810</td>\n",
       "      <td>698</td>\n",
       "      <td>617</td>\n",
       "      <td>553</td>\n",
       "      <td>424</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "\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",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayHour = df.groupby(by= ['Day of week','Hour']).count()['Reason'].unstack()\n",
    "dayHour.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hour', ylabel='Day of week'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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35c8M+VRv/l3nqx7J///79eP5/3+/fSr/c1u9ZVN2bKfsSDX10yhuWb0WICLurLkTysysK+wwberA1oh4WqkGKGk023C3k5lZFfVVfEjdXDlJ/WpJHwfGpwumfw78R7nFMjPbvrql+SWnMXoB8CDF0Ll/Avw3+eOpm5mNCP0oe6mynJr6scC/R4T7pptZ1+rkHaXDKSepvx/4sqRNFANz/Q/ws4h4uMyCmZltTzvMhdI0cTRpjtGTgH8F9sl5rZnZSNG3oyR1Se8F/gB4KfBb4IukoXTNzLrFDlNTB74A/JrihqMrI+LuVk6Q5tqbWnuuiLi3lWOYmZUtKn4BNFdO88sekg4FXgsslDQDuCMi3jfUayWdDpwDbOTZu3ADGGwqPDOz7a5/R7lQKmkSsD9wAHAgsCv5wyScCRyShpw0M6usbml+yemn/jPgLcDNwLsi4pCImDfEawbch6evM7MRIFD2MhRJu0n6rqTbJa2W9GpJUyQtk3RnepxcE3+2pDWS7pB0/La8j5zml21pKrkLuErSfwFba4553jYc08ys4zrc++WfgMsj4qQ0AfUE4OPA8og4V9ICihs7z5I0E5gLHErRs/AKSQe3O/tR56b6aOxeYBkwFtilZjEzq5T+yF8Gk5qsXwt8DSAino6IzcAcYHEKWwycmNbnAJdExNaIWAusoRhEsS2l9jWPiE+VeXwzs07pYJv6CyiGVrlA0mHA9RTXF6dGxAaAiNhQM9rtvkDtnM7r0r62NK2pS/pcejy53YNLulLST+qXQeLnS1opaeXap25o97RmZi2LFpbaXJWW+TWHGg28AvhSRLwceIKiqaWZRt8mbffFGaym/keSPgGcDXynzeP/Rc36TsA7KGbLbigiFgGLAN6xxye7pIORmY0ErdTUa3NVA+uAdRFxbdr+LkVS3yhpWqqlTwMeqImvnQlnOrC+lbLXGiypX05xB+nOkh6l+DaJgceImDTUwSPi+rpd/yvp6nYLa2ZWlk5NZxcR90u6T9IhEXEHxSxxt6VlHnBuerwsvWQpcJGk8ygulM4AVrR7/qZJPSL+EvhLSZdFxJx2Di5pSs3mKGAWsHc7xzIzK1OHe7+cDvx76vlyF/ABihy4RNKpFJ1ITgaIiFWSllAk/V7gtHZ7vkBel8Y5kqYCR6Rd10bEg5nHv55n24Z6gbuBU1stpJlZ2Tp5R2lE3ERRia3XcG7niFgILOzEuYfs0pgulK6g+FZ5J7BC0klDvOYISXtHxEER8QLgU8Dtablt24ttZtZZnbz5aDjldGn8BHBERDwAIGlP4AqKxv9mvgK8PsW/FvgsxZ8jh1NcXBj0S8HMbHvbYcZ+AUYNJPRkE0PX8Hsi4qG0/i5gUURcClwq6abWi2lmVq6q18Bz5ST1yyX9CLg4bb+LYp7SwfRIGh0RvRRtSPV9OM3MKqW3U91fhlnOhdK/lPR24GiK7oyLIuL7Q7zsYuBqSb8FniRNqiHpRXiALzOroB2ppk5EfA/4Xu5BI2KhpOXANODHEb+b0nUURdu6mVml7Eht6m2JiGsa7PtVWeczM9sWXdL64vZtMzOA2FEmyZB0gqSyh+g1MxtW/S0sVZaTrOcCd0r6B0m/V3aBzMyGQ1/kL1WW0/vlvWnQ93dTjA8cwAXAxRHxWFkFu7H/5uzY9U/kD9M7Yezu2bEPP74qO7anZ+fsWIDRPflzhRzZ8/rs2Lful1+PeMsxv86OHbPPuOxYje3Jjt26Zkt2LMBOx+YPM/3C9+yZf+C1LQyK9/IX58c+tDk7tH9mC8cFdv+XS7Nj779ubHbswVt3yo7dqSf/5+JnW/P/4J+o8dmxnbIjzVFKRDwKXApcQtGj5W3ADZLck8XMukIr46lX2ZA1dUlvAT4IvBC4EDgyIh6QNAFYDfxLuUU0MyvfjtSl8WTgHyPip7U7I2KLpA+WUywzs+2r6hdAc+W0qZ8yyHPLO1scM7PhEV1SU8/p0vgqSddJelzS05L60kxIZmZdoy+UvVRZzoXSL1L0fLkTGA/8H9yObmZdppP91CXdLekWSTdJWpn2TZG0TNKd6XFyTfzZktZIukPS8dvyPnJ7v6yhGE63LyIuAP5wW05qZlY1/ZG/ZPrDiDg8IgZmQFoALI+IGcDytI2kmRT3Ax0KzAbOl5TfL7hOTlLfkubZuyndgPQRoLVO2WZmFbcdujTOARan9cXAiTX7L4mIrRGxFlgDHNnuSXKS+vtS3P8FngD2A94x1IskrZV0V/3SbkHNzMrUSk1d0nxJK2uW+XWHC+DHkq6veW5qRGwASI97pf37AvfVvHZd2teWnN4v96Qp7IiIT7Vw7NpJV3ei6Bo5pbXimZltH63c/h8Riyim5mzmqIhYL2kvYJmk2weJbXTlte0/CJrW1FX42zTRxe3AryQ9KOlvcg4cEZtqlt9ExBeA17VbUDOzMnWyTT0i1qfHB4DvUzSnbJQ0DSA9DkwTuo6iBWTAdKCFcSuea7Dmlw8DR1FMOr17REwGfh84KrWrD0rSK2qWWZL+FMgf8MTMbDvqVJu6pJ0l7TKwDrwRuBVYCsxLYfOAy9L6UmCupHGSDgJmACvafR+DNb+cArwhIn47sCMi7pL0XuDHwD8Ocez/V7PeC9wNvLPNcpqZlaqDwwRMBb4vCYoce1FEXC7pOmCJpFOBeymapImIVZKWALdR5MrTIqKv3ZMPltTH1Cb0ARHxoKQxQx04Itzt0cxGjE7NURoRdwGHNdi/CTiuyWsWAgs7cf7BkvrTbT4HgKRxFL1kDqw9T0R8OrdwZmbby44woNdhTYYDEEVvlqFcBjwCXA9szSlM6vozH2D3CS9j0rgDc15mZrbNqj75Ra6mST0i2r6jKZkeEbNbeUFtN6EXTJnTJR+xmY0E3VJTL3Pu0Z9LemmJxzcz65ho4V+V5Yyn3hJJt1KMeTMa+EC6i3QrRbNNRMTLOn1OM7Nt1S019Y4ndYrbWw8v4bhmZqXpkpxeSlJfGxH3lHBcM7PSuKbe3F6SPtrsyYg4r4Rzmpltk67v/bINeoCJNB6kxsysklxTb26DbzAys5GmW+YoLSOpu4ZuZiNOzjR1I0EZSb3h2AZmZlXmmnoTEfFQp49pZla2vi7J6mXU1M3MRhxfKDUz6yJdktOd1M3MwDX10vXmjdYLwMRxe2fHbt5yV3bsqKHnAvmdsaMnZ8cCTBi7R3bstPE5Ix0XDp70cHbsuKNbmLD8md7s0Hjw8fwyHLxza79NrcQ+sSU/dsZ+Q8cM2NxoROrG+l/+vLkSmutrbbKbnlkHZMdOWJ1/k/ekR4ecLuF3xm8dmx07eVx+x7hlT96aHdspnU7qknqAlcBvIuIESVOAb1PMMXE38M6IeDjFng2cCvQBZ0TEj9o9b5mjNJoNrVuqRzbilTBK45nA6prtBcDyiJgBLE/bSJoJzAUOBWYD56cvhLY4qZuZAX39+ctQJE0H3gz8W83uOcDitL4YOLFm/yURsTUi1gJrgCPbfR9O6mZmQD+RvUiaL2llzTK/7nBfAP6K597TNDUiNgCkx73S/n2B+2ri1qV9balsm7qZ2fbUSjf12lna6kk6AXggIq6XdGzG4RpdbGi7XdJJ3cyMjg4TcBTwVkl/RDGf8yRJ3wI2SpoWERskTQMeSPHrgNor9dOB9e2e3M0vZmZARGQvQxzn7IiYHhEHUlwA/UlEvBdYCsxLYfOAy9L6UmCupHGSDgJmACvafR+uqZuZAb3lDxNwLrBE0qnAvcDJABGxStIS4DagFzgtIlrr31rDSd3MjHIG9IqIq4Cr0vommgx4GBELgYWdOKeTupkZRe+XblBqm7qkoyV9IK3vmdqLzMwqJyJ/qbLSauqSzgFmAYcAFwBjgG9RXBk2M6uUbqmpl9n88jbg5cANABGxXtIuJZ7PzKxt/VWvgmcqM6k/HREhKQAk7VziuczMtklfl9TUy2xTXyLpK8Bukj4EXAF8tcTzmZm1rZVhAqqstJp6RHxe0huARyna1f8mIpaVdT4zs20x1E1FI0WpXRpTEs9O5GlQnPkAkyfMZOK4Fsa4NjPbBlWvgecqrflF0mOSHk3LU5L6JA06u0BELIqIWRExywndzLYnN78MISKe09NF0olswxjBZmZlik4O6TWMOl5Tl9TwiyIifgC8rtPnMzPrhF76s5cqK6OmvgJ4haS31+wbRXEjUrX/bjGzHVZ/xZN1rjIvlL6FZ5N4L8VEq28t8XxmZm0LOak3s5ekjwL104EH8D7gvBLOaWa2Tap+ATRXGUm9B5hI4ymazMwqyc0vzW2IiE+XcFwzs9L0qbcjx5G0E/BTYBxFjv1uRJwjaQrwbeBAiubod0bEw+k1ZwOnAn3AGRHxo3bPX0Y/ddfQzWzE6W/h3xC2Aq+LiMOAw4HZkl4FLACWR8QMYHnaRtJMimnvDgVmA+dL6mn3fZSR1BvO7GFmVmWdSupReDxtjklLAHOAxWn/YuDEtD4HuCQitkbEWmAN23BPT8eTekQ81OljmpmVLf9+0qHb3iX1SLoJeABYFhHXAlMjYgNAetwrhe8L3Ffz8nVpX1tKnfnIzGyk6Kcve5E0X9LKmmV+7bEioi8iDgemA0dKeskgp27UZN12VxzPUWpmRmvDBETEImBRRtxmSVdRtJVvlDQtIjZImkZRi4eiZl472NV0YH12Yeq4pm5mBvTqmexlMGk+5t3S+njg9cDtwFJgXgqbB1yW1pcCcyWNS/M4z6C4M78tla2pb+1/fOig5KnezdmxUv73mHp2ZkzPxOz4J7euy47ddaf8USj7WhjneY9JT2THsjX/Anvfmk35xwXi6fwyxzP5sWNetNfQQQPufzg/duvT+bGbHssOHXXnfcSx+de8Yp998svRQpkfeXh8duz4MYMnrVpqoZXgiCnPcPtjY7JiT93zSC7f2ML/Xwd0cECvacDi1INlFLAkIv5T0i8oJg86FbgXOBkgIlZJWgLcRnH3/WkR0dfuySub1KugrITe7cpK6CNRaQl9BMpN6MB2T+gA/e3n0eeIiJsp5meu37+JJr0DI2IhsLAT53dSNzOje4bedVI3MwOCztTUh5uTupkZHvvFzKyr9Ef+BeIqc1I3M8M1dTOzruI2dTOzLhLhmnpTkm5hkLELIuJlZZzXzKxdbn4Z3Anp8bT0eGF6/GNgS0nnNDNrmy+UDiIi7gGQdFREHFXz1AJJ/wt4ZiQzq5RuaX4pe0CvnSUdPbAh6TXAziWf08ysZUFf9lJlZV8oPRX4uqRdKdrYHwE+WPI5zcxa1i019VKTekRcDxwmaRKgiHikzPOZmbWrW5J6qc0vkqZK+hrw7Yh4RNLMNOykmVmldHDi6WFVdpv6N4AfAQNjiv4K+HDJ5zQza1l//zPZS5WVndT3iIglUHy1RUQvNL/KUDvv35NP319y0czMntXJiaeHU9lJ/QlJu5NuRJL0KoqLpQ1FxKKImBURs8aP3bvkopmZPSuiP3sZjKT9JF0pabWkVZLOTPunSFom6c70OLnmNWdLWiPpDknHb8v7KOuO0g8D/wv8FcU8fC9I/dP3JE3hZGZWJR28UNoLfCwibpC0C3C9pGXA+4HlEXGupAXAAuAsSTOBucChFE3VV0g6uN0p7cqqqU8H/gm4PJ1jGXAR8JqI+GVJ5zQza1unml8iYkNE3JDWHwNWA/sCc4DFKWwxcGJanwNcEhFbI2ItsAbInwexTll3lP4FgKSxwCzgNcDrgL+WtDkiZpZxXjOzdrVSU5c0H5hfs2tRRCxqEHcgxXyl1wJTI2JDca7YIGlgFvV9gWtqXrYu7WtL2TcfjQcmAbumZT1wS8nnNDNrWdGPIzc2FgHPS+K1JE0ELgU+HBGPSmoa2ugU2YWpU1ab+iKK9qHHKL6hfg6cFxHbf4pwM7MMnbz5SNIYioT+7xHxvbR7o6RpqZY+DXgg7V8H7Ffz8ukUFeC2lNWmvj8wDrgf+A1FoTeXdC4zs23WqTZ1FVXyrwGrI+K8mqeWAvPS+jyKTiQD++dKGifpIGAGsKLd91FWm/rs9MYOpWhP/xjwEkkPAb+IiHPKOK+ZWbs6WFM/CngfcIukm9K+jwPnAkvSXfX3knoCRsQqSUuA2yh6zpzWbs8XKLFNPSICuFXSZoq+6Y9QjLN+JOCkbmYV05nRFyPiZzRuJwc4rslrFgILO3H+strUz6CooR8FPEPRZ/0XwNfxhVIzq6BWLpRWWVk19QOB7wIfGejCY2ZWaV0ySmNZbeofLeO4ZmZlifZ7EVZK2f3UzcxGCNfUzcy6R3RHTZ2IGFELML9bY6tSjirEVqUcVYitSjlGWuyOugx7AVouMKzs1tiqlKMKsVUpRxViq1KOkRa7oy5lj6duZmbbkZO6mVkXGYlJfdCR0UZ4bFXKUYXYqpSjCrFVKcdIi90hKbVTmZlZFxiJNXUzM2vCSd3MrIs4qZuZdZFK31Eq6cUUk7LuSzG903pgaUSs7tCx9wWujYjHa/bPjojL62KPpBhN+Lo08/ds4PaI+O+M83wzIk7JiDuaYljiWyPix3XP/T7FgPuPShpPMQv5KyjGX/77iHikLv4M4PsRcV/GecdSzGS+PiKukPQeihE2V1PMu/hMXfwLgbdRzNTSC9wJXFxfBjMbHpWtqUs6C7iEYlziFcB1af1iSQtaPNYH6rbPoJh15HSKMd/n1Dz993Wx5wD/DHxJ0meBLwITgQWS/roudmnd8h/A2we262JX1Kx/KB13F+CcBu/v68CWtP5PFPO9fi7tu6DBW/4McK2k/5H055L2bPzJQHr9m4EzJV1IMXD/tcARwL/VlfkM4MvATun58RTJ/ReSjh3kHCNWzeTAnT7u7mUct1Mk7SrpXEm3S9qUltVp324tHOeHdduTJH1W0oWpAlH73Pl123tL+pKkf5W0u6S/lXSLpCVpOjhrZLjvfhrkzrFfAWMa7B8L3Nnise6t274FmJjWDwRWAmem7RsbxPYAE4BHgUlp/3jg5rrYG4BvAccCx6THDWn9mLrYG2vWrwP2TOs7A7fUxa6uPUfdczc1eL83Unxhv5FiWq0HgcspptDapS725vQ4GtgI9KRtNXh/t9Q8PwG4Kq3vX/+5pf27Usz2cjuwKS2r077dWvj/+2Hd9iTgs8CFwHvqnju/bntv4EvAvwK7A3+b3scSYFpd7JS6ZXfgbmAyMKUudnbd+/wacDNwEcWs8bWx5wJ7pPVZwF3AGuCe+p+Lmp+jTwAvzPhsZgFXpp+7/YBlFBPSXAe8vC52IvBpYFWKeZBiFvv3Nzjuj4CzgL3rPsuzgGV1sa9osrwS2FAXe2n6PE6kmMbtUmBck5/tyykqXgvSZ3tW+lk7HbislRywIy3DXoBBflhvBw5osP8A4I4G+29ustwCbK2Lva1ue2L6ATqPuiTJc5PvjXXP1ceOAj6SfrEOT/vuavL+fpmSxe7U3frc4DzfAT6Q1i8AZqX1g4HrGhy7/pdjDPBW4GLgwbrnbqX4opxMMVH4lLR/J2q+TNK+W2p+AScD19cep0E5RlRioBimb23d8kx6vKsu9oaa9X8D/i79bH4E+EH951azfiVwRM3/3/Nue0/n+zzFlGcr0jH3afJztAJ4E/Bu4D7gpLT/OIqpI2tjLwPeTzGx8UeBT1LMh7mYohmvNvZ5v2PNnqOYMugn6b3VL08O8Tvz1xST6Oze4P+u9nevvmJ2U7Py7ejLsBdgkB+c2RS1mR9S3HCwKP2CrqGmllQTvxE4PP1i1S4HUrQX18b+hJR0a/aNBr4J9NXtvxaYkNZH1ezftf6HsOa56RSJ+Iv1P4w1MXdT1NjWpse90/6JDX7wdwW+Afw6leeZ9JqrgcMaHPvGQT7X8XXbH0nHugc4A1gOfJUigZ9TF3smRWJcRPGlO/BFsyfw0wbnGlGJAfiL9DP20pp9a5uU/4ZBjlO/fTswOq1fU/fcLUMc+w+A8ykmcb+SugGthnh/N9Zt/7Ju+7qBn2uKa0S1z/0Y+Ctq/uoAplJ8KV5RF3srMKPJ53Rf3fZqan6P0r55FH893NOsvMDfDfW5eUmfzXAXYNDCFT9srwLeAZyU1nuaxH4NOLrJcxfVbU+npvZY99xRddvjmsTtUfvL3yTmzdTVgDLe8wTgoCbP7QIcRlF7nTrIMQ5u8Zz7kGqCwG7psz6ySeyh6fkXZxx3xCUGnv1CPi993s3+0lpHUdv9GMWXomqeq2+2Oj19Fq+jaP75AvBa4FPAhQ2O/bzKAkUT4Gzggrr9v6BoZjuZ4ov5xLT/GJ7/F+DPB35HgLcAP6p5rv5LdjLFdZvbgYeBh9Ln/jme3xR1EnBIk8/pxLrtfwBe3yBuNnXNqhRNRRMbxL4I+G4rP+M70jLsBfDSvUtdYnioLjFMroutVGJISe8a4P4mz59TtwxcE9kb+GaD+GOBb1Nc77gF+G9gPqkGXxd7SQuf8WEUzVw/BF5McSF9M8UX3GvqYl9G0VyzGfgZ6cuf4i+tMxoc+8XA6+s/Pxr/pfxiiiafbYl907Yc10v6bIa7AF52zIXUdFPlWIqL4S+pSnm3ZzkomuLuAH5A0VQ4p+a5+iauVmJPLyPWS83nNtwF8LJjLjS51uDYapSD1nuIDWusl2eXSt98ZCObpJubPUXRtu7YutgKlaMn0k15EXF3ug/hu5IOSPFVi7XESd3KNBU4nuJCWy1RXLRz7PNjq1KO+yUdHhE3AUTE45JOoLgR7qUVjLXESd3K9J8Ufz7fVP+EpKsc2zC2KuU4hWIYiN+JiF7gFElfqWCsJR5P3cysi1R27BczM2udk7qZWRdxUrdhJ+nxuu33S/ricJXHbCRzUreuJalnuMtgtr05qVulSTpA0nJJN6fH/dP+b0g6qSbu8fR4rKQrJV1EcfOK2Q7FXRqtCsZLuqlmewrFkLpQjHT5zYhYLOmDFBOWnDjE8Y6kuL1/bacLalZ1TupWBU9GxOEDG5LeTzH5A8Crgben9QspBvMaygondNtRufnFRpqBGyt6ST+/kkQx0ceAJ7Z3ocyqwkndqu7nFBNjA/wxxZCxUIza98q0PodidiezHZ6TulXdGcAH0mBU76OYfQmK2ZmOSRN4/z6unZsBHibAzKyruKZuZtZFnNTNzLqIk7qZWRdxUjcz6yJO6mZmXcRJ3cysizipm5l1ESd1M7Mu8v8BLQMtzzxC8TMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dayHour, cmap= 'magma' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a clustermap using this DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f5de13fa0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dayHour, cmap= 'viridis', standard_scale = 1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now repeat these same plots and operations, for a DataFrame that shows the Month as the column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>1970</td>\n",
       "      <td>1581</td>\n",
       "      <td>1525</td>\n",
       "      <td>1958</td>\n",
       "      <td>1730</td>\n",
       "      <td>1649</td>\n",
       "      <td>2045</td>\n",
       "      <td>1310</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>1727</td>\n",
       "      <td>1964</td>\n",
       "      <td>1535</td>\n",
       "      <td>1598</td>\n",
       "      <td>1779</td>\n",
       "      <td>1617</td>\n",
       "      <td>1692</td>\n",
       "      <td>1511</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2291</td>\n",
       "      <td>1441</td>\n",
       "      <td>1266</td>\n",
       "      <td>1734</td>\n",
       "      <td>1444</td>\n",
       "      <td>1388</td>\n",
       "      <td>1695</td>\n",
       "      <td>1099</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>1960</td>\n",
       "      <td>1229</td>\n",
       "      <td>1102</td>\n",
       "      <td>1488</td>\n",
       "      <td>1424</td>\n",
       "      <td>1333</td>\n",
       "      <td>1672</td>\n",
       "      <td>1021</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>1584</td>\n",
       "      <td>1596</td>\n",
       "      <td>1900</td>\n",
       "      <td>1601</td>\n",
       "      <td>1590</td>\n",
       "      <td>2065</td>\n",
       "      <td>1646</td>\n",
       "      <td>1230</td>\n",
       "      <td>1266</td>\n",
       "    </tr>\n",
       "  </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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayMonth = df.groupby(by= ['Day of week','Month']).count()['Reason'].unstack()\n",
    "dayMonth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month', ylabel='Day of week'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dayMonth, cmap= 'magma' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f5dde27f0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dayMonth, cmap= 'viridis', standard_scale = 1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Continue exploring the Data however you see fit!**\n",
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project (Reine CSC)

# data[].attributes.attachment_link.title
Timothy Straughan 01-911 Calls Data Capstone Project

# data[].attributes.body
i got stuck on the last question - not sure why it didn't work even after i checked and copied the code from the solution file ):