01-911 Calls Data Capstone Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Capstone Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this capstone project we will be analyzing some 911 call data from [Kaggle](https://www.kaggle.com/mchirico/montcoalert). The data contains the following fields:\n",
    "\n",
    "* lat : String variable, Latitude\n",
    "* lng: String variable, Longitude\n",
    "* desc: String variable, Description of the Emergency Call\n",
    "* zip: String variable, Zipcode\n",
    "* title: String variable, Title\n",
    "* timeStamp: String variable, YYYY-MM-DD HH:MM:SS\n",
    "* twp: String variable, Township\n",
    "* addr: String variable, Address\n",
    "* e: String variable, Dummy variable (always 1)\n",
    "\n",
    "Just go along with this notebook and try to complete the instructions or answer the questions in bold using your Python and Data Science skills!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Import numpy and pandas **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import visualization libraries and set %matplotlib inline. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go \n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the csv file as a dataframe called df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() of the df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 9 columns):\n",
      "lat          99492 non-null float64\n",
      "lng          99492 non-null float64\n",
      "desc         99492 non-null object\n",
      "zip          86637 non-null float64\n",
      "title        99492 non-null object\n",
      "timeStamp    99492 non-null object\n",
      "twp          99449 non-null object\n",
      "addr         98973 non-null object\n",
      "e            99492 non-null int64\n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <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",
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       "  </thead>\n",
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       "      <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",
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       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
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       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
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       "      <th>2</th>\n",
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       "      <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  "
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
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       "      <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",
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       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
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       "      <td>Thu</td>\n",
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       "      <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",
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       "    <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",
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      "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",
       "\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",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12         Thu  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12         Thu  \n",
       "2                    HAWS AVE  1   Fire    17     12         Thu  "
      ]
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     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "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": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df['zip'].value_counts();\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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"
      ]
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     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 townships (twp) for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df['twp'].value_counts(); #value_counts by default sorts in descending order\n",
    "x.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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.groupby('title')['title']) #alternatively df['title'].nunique() or len(df['title'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <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",
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       "      <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",
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       "    <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",
       "  </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  \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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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",
       "  </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  \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  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_func(entry): #alternatively\n",
    "    return entry.split(\":\")[0]\n",
    "df['Reason']=df['title'].apply(my_func)\n",
    "df.head()"
   ]
  },
  {
   "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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ca82df0320>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(df['Reason'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12d3830b8>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAERCAYAAACD9ivUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f62fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['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": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timeStamp']=pd.to_datetime(df['timeStamp']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time=df['timeStamp'].iloc[0]\n",
    "time.hour"
   ]
  },
  {
   "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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lat                                                    40.1142\n",
       "lng                                                   -75.3385\n",
       "desc         PENN ST & ARCH ST; NORRISTOWN; 2015-12-10 @ 17...\n",
       "zip                                                      19401\n",
       "title                              Traffic: VEHICLE ACCIDENT -\n",
       "timeStamp                                  2015-12-10 17:47:02\n",
       "twp                                                 NORRISTOWN\n",
       "addr                                         PENN ST & ARCH ST\n",
       "e                                                            1\n",
       "Reason                                                 Traffic\n",
       "hour                                                        17\n",
       "month                                                       12\n",
       "DayofWeek                                                    3\n",
       "Name: 20, dtype: object"
      ]
     },
     "execution_count": 90,
     "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['DayofWeek']= df['timeStamp'].apply(lambda time: time.dayofweek)\n",
    "df.iloc[20]"
   ]
  },
  {
   "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": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'};\n",
    "df['DayofWeek']=df['DayofWeek'].map(dmap);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ca84d41828>"
      ]
     },
     "execution_count": 98,
     "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='DayofWeek',data=df,hue='Reason')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12f614048>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fGMnMt9WvF1F1p50OfDIiRoC9gFX1LrPoks9/M7Hf21StOda7M/ORuu69VK2U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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f6100b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ca84024ba8>"
      ]
     },
     "execution_count": 100,
     "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": 249,
   "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>DayofWeek</th>\n",
       "      <th>Date</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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  DayofWeek   Date  \n",
       "month                                   \n",
       "1       13205  13205      13205  13205  \n",
       "2       11467  11467      11467  11467  \n",
       "3       11101  11101      11101  11101  \n",
       "4       11326  11326      11326  11326  \n",
       "5       11423  11423      11423  11423  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth=df.groupby('month').count() #the count here counts the number of entries in each column\n",
    "byMonth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "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": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ca85054f60>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LGxQ5KaNJjY9jg4JCfKKg6KGoKOP7H5zN13+/la/+bis/XLObTxZM5o55mSF3Jc1Q19zaRk1DEyVVDTxctI+SygbGDI9hxcIcls+fRNrYEX6XOKjMjAW5AdbtrqW93fXL5EgivaGg6IXLJibw3D8soKjsKA+s38v3/rSTn71cxh1XZXJnweRez48cqU40tXDw2BkOHjtD9fHg76oG73nDGY6cbH5n2cmBUXx72QzeP2dCRF9xNj83wO/ePMjOQyeZnq6xx2RwRe7/vD4yMxbkBViQF2DbweM8sL6cX75SzsNFFbzv8gxWLMwmNzVyp69sb3fUnWp+1wf/Qe/xQe/xyabWd60TGx1FesJwMhJHsHhqCukJI8hIGEFWYBRzMhP1DZrgjXcAG/fWKShk0Gkq1H5QWd/IL18p56niSppa2llyyTjuXZwdlvMInG1tp+b4uz/4Oz6uaWjibFv7u9aJHx5DRsIIJiQGAyA9YQQZ3uOMxBEERsUpDHrguh+uJTNpJI/cOdfvUiSEaSpUn0xMGsm/L5vJF67P47FN+3l00z7ef/9h8iclcveiHK6flhpyH4TOObZXn2Dd7lp21JzgYMNfTwud/90iNT6OjMQRXJoxlqUzxr8rBNITRqgPp58syA3w9JYqzra2ExujS7Rl8OiIYgA0nm3lqc2V/PKVCg42nCE3dTQrFmZz22UZQ/o/+PEzLWzYU8faXUdYt7v2nb6CzKSR73zwn/s9wTsySEsYrqu/Bsmfth3inv/dwlN3X83cyeF3tCqDQ0cUQ8TI2Bg+UTCZj86bxPNba/jFunL++elSfvjiLu5aMJkPz80kfgh8y3bOsaPmJC/vOsK6XbVsOXCMtnZH/PAYFk5JYfGUFBZNTSE1Xp30Q8HV2clEWfB+CgWFDCYdUQwC5xyv7KnjF+v2snHvUeLjYrhj3iQ+WZBF6pjB/RA+0dRC0Z66YDjsruXwieBRw/S0MVw7LYXFU1O5fGICMbr7fEha9tMNDIuO4ul75/tdioQoHVEMUWbGwikpLJySQmlVAw+sL+fB9Xt5eEMFf3dFBp9emE3OAM3N7Jxj56GTrN1Vy8u7jvDG/mO0ekcN1+QFWDw1lcVTUgY9sKRvCnIDPLi+nFPNrWE5XIkMTfpLG2SzJiTws49cwb660zy0oZzfFlfxm+JKbpw+jrsX5fTLaKcnm1ooKqtj7a5a1u6q5dCJJgAuSRvDioXZwaOGzASNWRWCCnID/HztXl6vOMp108b5XY5ECAWFT7ICo/jObZfyxSVTeGzjPh7dtJ/V2w8zd3IS9yzKZvGUnl8p5Zxj12HvqGHnEbacO2qIi2FBXoBrp6ayaGoK43TUEPLmTEokLiaKojIFhQweBYXPAqPj+PKNU7l7UQ6/2VzJyg0VfPKRYqaMG83dC3MonJ3e6ZVSp5pb2bCnjnW7j7B2Vy01x4NHDdPGx/Opa7K5dmoKV0xK1FFDmBk+LJr8rESKyur8LkUiiDqzh5iWtnaeK63mgXXl7Dx0krSxw7lrwWRun5vJwWNnWLsrGAzF++tpaXOMjothQW6Aa6elsGhKqoYRiQA/X1vG//vTLjZ/fQkp8ZoeVXqnL53ZCoohyjnH2t21PLBuL6+W1xMdZbS1B/+tpo2PZ9HUFK6dmsocHTVEnJLKBpb9rIgf334Zyy7TkPfSOwNy1ZOZPQzcAhxxzs302j4IfAu4BJjrnCvusPzXgLuANuDzzrnVXvtS4MdANPCQc+4+r30y8CSQBLwBfMw5d7Y3OxGOzIxrp6Zy7dRU3qps4Nm3qskbN5rFU1MibvRUebeZGWMZMzyGjWVHFRQyKHryVfQRYOl5bduAvwPWd2w0s+nA7cAMb52fm1m0mUUDPwNuAqYDH/aWBfge8F/OuTzgGMGQkQ4um5jANwqn8+G5mQoJITrKuDonmQ1ldYTqGQEJLd0GhXNuPVB/XtsO59yuThZfBjzpnGt2zlUAZcBc76fMOVfuHS08CSwzMwOuA5721n8UuK3PeyMSIRbkBjjYcIYD9Y1+lyIRoL9PbmcAlR2eV3ltF2pPBhqcc63ntXfKzFaYWbGZFdfW1vZr4SKhZH5ucHrUDbr6SQZBfwdFZxf+uz60d8o596BzLt85l5+SktLHEkVCX3ZgFGljh7Ox7KjfpUgE6O+gqAImdng+Aajuor0OSDCzmPPaRaQLZsb8nAAb99bR3q5+ChlY/R0UzwK3m1mcdzVTHvA6sBnIM7PJZhZLsMP7WRfsiXsZ+IC3/nLgmX6uSSQsLchL5lhjC2/XnPC7FAlz3QaFmT0BbAKmmlmVmd1lZu8zsyrgauB5M1sN4JzbDjwFvA38Cfisc67N64P4HLAa2AE85S0L8BXgy2ZWRrDPYmX/7qJIeJqfE+yn0F3aMtB0w51ICLvhR+tISxjBY5/U9KjSM3254U639IqEsILcAK9XHKW5tc3vUiSMKShEQlhBboCmlnbePNDgdykSxhQUIiHsquykd6ZHFRkoCgqREDZm+DBmT0xQUMiAUlCIhLiCnAAlVcc52dTidykSphQUIiGuIDdAW7vjtfL67hcW6QMFhUiIu2JSAsOHRWncJxkwCgqREBcXE82VWUls3KugkIGhoBAJAwW5AXYfPsWRE01+lyJhSEEhEgYWeMOOb9yr0WSl/ykoRMLA9LQxJIwcpn4KGRAKCpEwEBVlzM9JZqOmR5UBoKAQCRPzcwJUH2+iou6036VImFFQiISJc/0UReqnkH6moBAJE5OSR5KRMIKiPeqnkP6loBAJE2ZGQW4ym8qP0qbpUaUfKShEwkhBboDjZ1rYXn3c71IkjCgoRMLIX6dHVT+F9B8FhUgYSYmPY+q4eA07Lv1KQSESZgpyA2zeV09Ti6ZHlf6hoBAJMwW5yTS3tvPG/mN+lyJhQkEhEmauyk4mOsoo0miy0k8UFCJhZnRcDJdPTGCDOrSlnygoRMLQ/NwAW6saOH5G06PKxVNQiIShBbkB2h28Wq6jCrl4CgqRMHTZxARGDIvWZbLSLxQUImEoNiaKq7KTFBTSLxQUImGqICfA3trTHDqu6VHl4igoRMJUwblhx3VUIRep26Aws4fN7IiZbevQlmRma8xsj/c70Ws3M/uJmZWZWamZXdFhneXe8nvMbHmH9jlmttVb5ydmZv29kyKRaNr4eJJGxSoo5KL15IjiEWDpeW1fBV5yzuUBL3nPAW4C8ryfFcD9EAwW4JvAVcBc4JvnwsVbZkWH9c7floj0wbnpUYv2anpUuTjdBoVzbj1Qf17zMuBR7/GjwG0d2h9zQa8CCWaWBrwHWOOcq3fOHQPWAEu918Y45za54F/yYx3eS0QuUkFugMMnmtlbe8rvUiSE9bWPYpxzrgbA+53qtWcAlR2Wq/Laumqv6qS9U2a2wsyKzay4tra2j6WLRI53pkfVXdpyEfq7M7uz/gXXh/ZOOecedM7lO+fyU1JS+liiSOSYmDSSiUkj2KB+CrkIfQ2Kw95pI7zfR7z2KmBih+UmANXdtE/opF1E+smC3ACvlh+lta3d71IkRPU1KJ4Fzl25tBx4pkP7x72rn+YBx71TU6uBG80s0evEvhFY7b120szmeVc7fbzDe4lIP5ifE+BkUytbD2p6VOmbnlwe+wSwCZhqZlVmdhdwH3CDme0BbvCeA/wRKAfKgF8CnwFwztUD3wY2ez//7rUB3As85K2zF3ihf3ZNRADm5yQDsHGv+imkbyxUL5vLz893xcXFfpchEhJu+vErJIwYxhMr5vldivjMzLY45/J7s47uzBaJAAtyk9my/xhnzmp6VOk9BYVIBJifG+BsWzvF+8+/JUqkewoKkQgwNyuJYdGm+ymkTxQUIhFgVFwMl09M1LhP0icKCpEIUZAbYFv1cRoaz/pdioQYBYVIhCjITcY52KTLZKWXFBQiEWL2xARGxUZTtFenn6R3FBQiEWJYdBRXZSerQ1t6TUEhEkEKcgNU1J3mYMMZv0uREKKgEIkgBbnB4Tx09ZP0hoJCJIJMHRdPYHQsGxUU0gsKCpEIYmbMzwlQtPeopkeVHlNQiESYBbkBak82s+eIpkeVnlFQiESY+V4/xYY9Ov0kPaOgEIkwExJHkpU8ko26n0J6SEEhEoHm5wZ4tbxe06NKjygoRCLQgtwAp5pbKanS9KjSPQWFSAS6OjsZM91PIT2joBCJQImjYpmRPkZBIT2ioBCJUAU5Ad44cIzGs61+lyJDnIJCJEIV5AZoaXNs3nfM71JkiFNQiESoK7OSiI2O0ukn6ZaCQiRCjYiN5opJCQoK6ZaCQiSCFeQE2F59gvrTmh5VLkxBIRLBCvICgKZHla4pKEQi2KyMscTHxbBBp5+kCwoKkQgW402PqnGfpCsKCpEIV5CbzP6jjVTWN/pdigxRCgqRCLcgN9hP8ZOX9rC3VnNUyN+6qKAwsy+Y2TYz225mX/TaksxsjZnt8X4neu1mZj8xszIzKzWzKzq8z3Jv+T1mtvzidklEeiM3dTSFs9N5+o0qrv/hOm7+8Svcv3avjjDkHdbX6RDNbCbwJDAXOAv8CbgX+DRQ75y7z8y+CiQ6575iZjcD/wDcDFwF/Ng5d5WZJQHFQD7ggC3AHOdcl7eL5ufnu+Li4j7VLiJ/69DxJp7fWsOqkmreqmwA4IrMBApnp/PeS9NIHTPc5wqlP5jZFudcfq/WuYig+CDwHufcp7zn/wY0A3cBi51zNWaWBqx1zk01swe8x094y+8CFp/7cc7d7bW/a7kLUVCIDJzK+kZWlVazqqSGHTUnMIN5k5MpnJ3O0pnjSRoV63eJ0kd9CYqYi9jeNuA/zCwZOEPwSKEYGOecqwHwwiLVWz4DqOywfpXXdqH2v2FmK4AVAJmZmRdRuoh0ZWLSSD6zOJfPLM6l7MhJVpUEjzT+5fdb+cYz21iQF6BwVjo3zBjHmOHD/C5XBlifg8I5t8PMvgesAU4BJUBXw1BaZ2/TRXtn23wQeBCCRxS9KlhE+iQ3NZ4v3RDPF5fk8XbNiXdC4x9/W0Ls76O4dmoKhbPTuX7aOEbERvtdrgyAizmiwDm3ElgJYGb/SfBo4LCZpXU49XTEW7wKmNhh9QlAtde++Lz2tRdTl4j0PzNjRvpYZqSP5StLp/JmZQOrSqp5rrSG1dsPMzI2miWXjKNwdjoLpwSIi1FohIs+91EAmFmqc+6ImWUCLwJXA/8CHO3QmZ3knPtnM3sv8Dn+2pn9E+fcXK8zewtw7iqoNwh2Ztd3tW31UYgMDW3tjtcqjrKqpIYXttXQ0NhC/PAYls4YT+HsdObnJBMTrSvxh4pB7cz2NvgKkAy0AF92zr3k9Vk8BWQCB4APOufqzcyAnwJLgUbgTudcsfc+nyQYMAD/4Zz7VXfbVlCIDD0tbe1sKKtjVUk1L24/zKnmVpJHxXLTpeO5dXYG+ZMSiYrq7GyzDJZBDwo/KShEhramljbW7qplVWk1L+04TFNLO+PHDOeWWWkUzk5n1oSxBL8/ymBSUIjIkHS6uZU/7zjMqpIa1u0+QkubIzNpJIWzg6ExdVy8QmOQKChEZMg73tjC6rcPsaqkmo17j9LW7sjz7g6/ZVYa2Smj/S4xrCkoRCSk1J1q5oVtwdB4vSJ4/crMjDEUzkrnltnpZCSM8LnC8KOgEJGQVXP8DM+XBu/RKKk6DsCcSYkUzkrj5llppMZrCJH+oKAQkbCw/+hpnvNCY+ehk0QZzMv2hhCZMZ5EDSHSZwoKEQk7ew6fZFVJNatKa6ioO01MlLFwSgqFs9NYcsk44jWESK8oKEQkbDnn2F59IhgaJdVUH28iLiaK66alUjg7neumpTJ8mO4G746CQkQiQnu7483KY6wqqeG50hrqTjUzKjaaG6YHhxC5Ji+F2BjdDd4ZBYWIRJy2dsdr5UdZVVrNH7ce4viZFsYMj+GmmcF7NOZlJ2kIkQ4UFCIS0c62tlPkDSGyevshTp9tIzA6lpsvDYbGnEwNIaKgEBHxBIcQOcKzJdW8tOMIza3tpI396xAil2ZE5hAiCgoRkU6cam7lz28fZlVJNev31NLS5piUPJLCWenBIUTGx/td4qBRUIiIdKOh8Syrtx9iVUkNG/fW0e5g6rh4CmenccusdLICo2F/hvMAAAnaSURBVPwucUApKEREeqH2ZDMvbAve2Ld53zEAZk0YS+GsdN47K430MBxCREEhItJH1Q3eECKl1ZR6Q4hcmZVI4ex0bpqZRkp8nM8V9g8FhYhIP9hXd5rnSqtZVVLDrsPBIUTm5wQonJ3G0hlpjB0ZuneDKyhERPrZrkMnea60mmdLqtl/tJFh0cbCvBQKZ6ezZPo4RsfF+F1irygoREQGiHOObQdPsKo0OIRIjTeEyPWXpFI4K51rQ2QIEQWFiMggaG93vHHgGM+WVPPHrTXUnTrLqNhobpwxnsLZaSzIHbpDiCgoREQGWWtbO6+W17OqpJoXttVwoqmVhJHDuGnmeApnpXNVdjLRQ+hucAWFiIiPzra288qeWlaVVPPi24dpPNtGYHScdzd4GpdP9H8IEQWFiMgQceZsGy/vOsKqkmpe2nmEs63tZCSMeGcIkRnpY3wZQkRBISIyBJ1sauHPOw6zqqSG9btraW13TA6MotALjbxxgzeEiIJCRGSIO3baG0KktJpNe4/S7mDa+HgKZ6dzy6w0JiUP7BAiCgoRkRBy5GQTL2w9xKqSaor3B4cQmT1hLIWzg0OIpI3t/yFEFBQiIiHqYMMZniupZlVpNdsOngBgblYShZelc9PM8QRG988QIgoKEZEwUF57iudKa3i2pJqyI6eIjjLm5yRTODud98wYz9gRfR9CREEhIhJGnHPsOnySVSXBcacO1DcSGx3FwikpFM5OY8kl4xjVyyFEBj0ozOxLwKcAB2wF7gTSgCeBJOAN4GPOubNmFgc8BswBjgIfcs7t897na8BdQBvweefc6u62raAQkUjinKO06jirSqp5rrSGQyeaGD4siusvGUfhrHQWT03p0RAigxoUZpYBbACmO+fOmNlTwB+Bm4HfOeeeNLNfACXOufvN7DPALOfcPWZ2O/A+59yHzGw68AQwF0gH/gxMcc61dbV9BYWIRKr2dkfx/mOs8oYQOXr6LKPjYrhxxjgKZ6ezIDfAsOjOhxDpS1Bc7LCHMcAIM2sBRgI1wHXAR7zXHwW+BdwPLPMeAzwN/NSCd5ssA550zjUDFWZWRjA0Nl1kbSIiYSkqypg7OYm5k5P4ZuF0NpUf9YYQOcTv3jhI4shhLJ0ZvBv8qskXP4RIn4PCOXfQzH4AHADOAC8CW4AG51yrt1gVkOE9zgAqvXVbzew4kOy1v9rhrTuuIyIiXYiJjuKavBSuyUvh27fN5JXddawqreaZtw7yxOsHSI2P472zgtO8XpGZ0Ldt9LU4M0skeDQwGWgAfgvc1Mmi585tdRZprov2zra5AlgBkJmZ2cuKRUTCW1xMNEumj2PJ9HGcOdvGSzsPs6qkmsdfO8CvivaR0cepXS/m1NMSoMI5VwtgZr8D5gMJZhbjHVVMAKq95auAiUCVmcUAY4H6Du3ndFznXZxzDwIPQrCP4iJqFxEJayNio7llVjq3zErnRFMLa7YfZlVpNRv78F4XM2D6AWCemY30+hquB94GXgY+4C2zHHjGe/ys9xzv9b+4YE/6s8DtZhZnZpOBPOD1i6hLREQ6GDN8GO+fM4FH7pzbp/Uvpo/iNTN7muAlsK3AmwS/7T8PPGlm3/HaVnqrrAT+x+usrgdu995nu3fF1Nve+3y2uyueRERk8OiGOxGRCNKXy2OH5lx9IiIyZCgoRESkSwoKERHpkoJCRES6pKAQEZEuKShERKRLIXt5rJnVAvt92HQAqPNhu37SPkcG7XNkmOqci+/NChc7eqxvnHMpfmzXzIp7ew1yqNM+Rwbtc2Qws17fgKZTTyIi0iUFhYiIdElB0XsP+l2AD7TPkUH7HBl6vc8h25ktIiKDQ0cUIiLSJQWFiIh0SUHRQ2Y20cxeNrMdZrbdzL7gd02DwcyizexNM3vO71oGg5klmNnTZrbT+7e+2u+aBpqZfcn7m95mZk+Y2XC/a+pvZvawmR0xs20d2pLMbI2Z7fF+J/pZY3+7wD5/3/vbLjWz35tZjybRVlD0XCvwj865S4B5wGfNbLrPNQ2GLwA7/C5iEP0Y+JNzbhowmzDfdzPLAD4P5DvnZgLReJOKhZlHgKXntX0VeMk5lwe85D0PJ4/wt/u8BpjpnJsF7Aa+1pM3UlD0kHOuxjn3hvf4JMEPkAx/qxpYZjYBeC/wkN+1DAYzGwMsxJuV0Tl31jnX4G9VgyIGGOHNZT+SC8xZH8qcc+sJzqzZ0TLgUe/xo8Btg1rUAOtsn51zLzrnWr2nrwITevJeCoo+MLMs4HLgNX8rGXD/Dfwz0O53IYMkG6gFfuWdbnvIzEb5XdRAcs4dBH4AHABqgOPOuRf9rWrQjHPO1UDwiyCQ6nM9g+2TwAs9WVBB0UtmNhr4P+CLzrkTftczUMzsFuCIc26L37UMohjgCuB+59zlwGnC73TEu3jn5ZcBk4F0YJSZfdTfqmSgmdnXCZ5Of7wnyysoesHMhhEMicedc7/zu54BVgDcamb7gCeB68zsf/0tacBVAVXOuXNHik8TDI5wtgSocM7VOudagN8B832uabAcNrM0AO/3EZ/rGRRmthy4BbjD9fBGOgVFD5mZETx3vcM59yO/6xlozrmvOecmOOeyCHZu/sU5F9bfNJ1zh4BKM5vqNV0PvO1jSYPhADDPzEZ6f+PXE+Yd+B08Cyz3Hi8HnvGxlkFhZkuBrwC3Oucae7qegqLnCoCPEfxm/Zb3c7PfRUm/+wfgcTMrBS4D/tPnegaUd/T0NPAGsJXgZ0LYDWthZk8Am4CpZlZlZncB9wE3mNke4Abvedi4wD7/FIgH1nifYb/o0XtpCA8REemKjihERKRLCgoREemSgkJERLqkoBARkS4pKEREpEsKCpFB4o1M+5kOzxdHyqi8EtoUFCKDJwH4TLdLiQwxCgqRTphZljdu/0PePA2Pm9kSMyvy5i+Y681n8AdvbP9XzWyWt+63vLkA1ppZuZl93nvb+4Ac70an73ttozvMf/G4d3e0yJAS43cBIkNYLvBBYAWwGfgIsAC4FfgXoBJ40zl3m5ldBzxG8G5ugGnAtQTvgt1lZvcTHGBwpnPuMgieeiI4CvEMgkN7FxEcAWDDYOycSE/piELkwiqcc1udc+3AdoKT3DiCQ11kEQyN/wFwzv0FSDazsd66zzvnmp1zdQQHmxt3gW287pyr8rbxlve+IkOKgkLkwpo7PG7v8Lyd4NF4Z6eJzo2J03HdNi589N7T5UR8o6AQ6bv1wB3wzmmkum7mKDlJ8FSUSEjRtxeRvvsWwdnwSoFG/jpkdaecc0e9zvBtBGcWe37gSxS5eBo9VkREuqRTTyIi0iUFhYiIdElBISIiXVJQiIhIlxQUIiLSJQWFiIh0SUEhIiJd+v/Fk2/ZnntgKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth['twp'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x133a3c080>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J8Ly+BLgH+JExJhL4FrDfWrsceBV4ZpjjEBE/hYc5KCvK5HJ3P4+v28TfvrGP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\nvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133a19390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ca851c67f0>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.lmplot(x='Month', y='twp',data=byMonth.reset_index())\n",
    "#byMonth.reset_index(byMonth, inplace=True)\n",
    "sns.lmplot(x='month',y='twp',data=byMonth.reset_index()) #Needed to have the month column be an index starting from 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1342acd30>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j0+s7HYVaopY9qdf/WGt9CHgYWAu8DNyjtY5iXoCrV0qtAW4DHrCecgfwFLAe\neNOa3hAFZBgGVZVlTKwsJhnry9va4rqaYu6+ZeAciv/6o+RQiLEh7yNhrfUe4LKsYw9mPV4BrMg6\n1gvcOMDrbcRcSSFsFgj4mVLrp629k/auXnyBYM7fw+UaOIfiDxv28OaOQ9y8aKbkUIhRTW7WECNW\nXlbC1NoKXMkIsVh+LtwNmEPR0cfDT7/FT3+/na5jkkMhRicnXJgTY4Db7WbShEp6eo7R0taNxxfM\ny4W7dA7Fb157n9VWDsWG7c00vd/KXBXivPNSkkMhRhUZCYucKioKMa2uGq8Ry9tytqDfw19cp7j7\nS5dQW10EmDkUr7zdyUO/kBwKMbpIExY5ZxhG/9548Wj+dow+o66Ue5fOPjGHYo+ZQ/HCut2SQyFG\nBWnCIm9CwQBTa6twp6Ik4vm5BTmdQ/Gtr85hSpW5I3U8keS3q9/nez/dyPv7hrSMXAjbSBMWeWUY\nBpMmVFJa5Caax3S26vIQfz6njKU3nEuR5FCIUUSasCiI0pIwkyeU5jUQKJ1D8cCyucytnwhk5lCs\nlxwK4UinXB2hlCrDvFHiaswciBeA71rreIUYMq/Xy9S6alqPttPdG8vb3XZmDsV5zKmfxFMv7qCl\nvZeO7gg/fnYbF5xVxZLrFBUlgby8txDDNZSR8ErM7N6/BL4CFAGP5bMoMbZVVZRRU1FELHIsryPT\nc6ZXcN9X57Bo3rT+vfG2vtfKA4+t54+bPxpS4L0Q+TaUdcLTtdY3ZDz+W6XU2ErQEAUXCgaYVuen\n+fBRYgn3kHZ+Ph0+r5vPLjiLS8+Z2J9DEYkmePrlnWzY3swtJ8mhEKJQhjIS3qWUujL9QCl1AWbm\nrxAjkr5oVxx05W1NcdpAORS7rRyKZ/8kORTCPkMZfpwFvKaU0kACUMBRpdSHQEprfUY+CxRjX0VZ\nCV5PD0c6+vI2Twwn5lD8ctVOtlg5FC+t30PjjsPcfL2SHApRcENpwjec+hQhRiZcXITH46HlSCeG\nx4/Llb+FO+XhAF/7/AW8tbOFX6zStHdFaG3v5eGn32LOeRP54jWfIBzy5e39hcg0lCb838Bz1tfX\ntdZyNUPkRTDg71890XUsis+f3xUM6RyK3772Pq9l5VB84ZOfYN75kySHQuTdUIYbC4EdmJtx7lRK\nrVRK3ZTfssR4VlVRxuQJpSRjvSTzlFWcFvR7WHKd4h+/fAl11cWAmUPxn8+/y0M/lxwKkX+nbMJa\n62bgCeCp7vUBAAAeCklEQVSHmEvTrsIMYRcib7xeL1NqqwkHDWJ5vNMubUZtKfcsvfTEHIq9Zg7F\n8+s+lBwKkTenbMLWFvXvA/cCfcBirfWEfBcmBEBZaQmTJ5ZBoi9v+RNp6RyK+746h5nTygEzh+J3\nqz/gez/dyAf7O07xCkIM31CmI7YA+4BKYAIwUSmV+y0UhDgJj8dD3cQqysNeYpH836hZUx7iG0su\n+lgOxQ+f3Cw5FCLnTnlhTmt9L4BSqhj4AvC/gKlA/tYSCTGAcHERRaEgzS1txBIuPF5v3t4rnUNR\nf0Ylz7y6i/VNzf05FG/vamHJQsWFZ1fLhTsxYkPJjrgeuAa4FnPk/AzmagkhCs7lclE7oZKu7h5a\n247lZV+7TOkcirn1k/hZfw5FVHIoRM4MZTriH4H3gD/TWl+otf4mx3c+FsIW4eIiptVV4kpF87av\nXaaZg+RQvCo5FGIETjoSVko9C8wCaoEzgH9SSqWfs7cg1QkxCJfLxaSaCmtfu568v9/Jcih++fJO\nNkoOhThNg42EbwU+CbyEuSztauvfedZjIRyhqCjE1NpKUvFjJPK8rhgkh0Lk1klHwlrrTqAT+Ezh\nyhHi9LhcLqorSgn5UvT0RfF483vbcWYOxdOrdvKW5FCI0yQ7a4gxpaqijOqyING+wuw5UB4OcMfn\nL+COz19AWdhcMJTOofjJ77fT2ZP/+WoxukkTFmNOKBRkam2FedtznrZSynbh2dV8+7a5XHXxZNKL\n1jZub+aBR99g3dYDsq2SOKn8JGlnUErNAX6gtb5aKXUm8DiQBJq01ndZ5ywDbsfcwWO51vo5pVQA\nc1ePGsxpkVu11keUUnOBh6xzV2mtH8z3ZxCjj9vtZkptNa1H2unui+P15T8VLZ1DMfu8ifzsxR3s\nb+nuz6HY0NTMpWfImEd8XF5/Vyil7gYe5fiNHT8C7tFaLwBcSqnPKKUmYIYDzQMWAd9XSnmBO4Gt\nWuv5wJPAfdZrPAIs0VpfCcxRSs3K52cQo1tVZRk1FSHi0cJtiXhG3cA5FD9ffURyKMTH5Puv5veA\nz2U8btBar7F+/QJmQttsYK3WOm5dDNyFuTTuCuDFjHOvUUqFAZ/Werd1/CXMm0iEOKlQMMCUSZUY\nyQjxWKwg75nOofhWRg5FIonkUIiPyWsT1lo/i7lDc1rmPZ5dQAkQBjJ/R3YDpVnHuzKOdWa9Rmlu\nqxZjUfpOu/Kwj2gB8ifSqjNyKAJe87d/Oofi53+QHApRgDnhLJk/h4WBdsymWpJ1vM06Hs46t2uA\nc9uH8saNjY2nV3EBOb1Gp9cHQ6sxkUhwtKOHlOHF5S7MH4Fi4C+vqmLtu13ofX2kgNfe3M/m7QeZ\nXx/mjIl+x+RQNDU5ex9fp9c3XIVuwm8qpeZrrVcDnwJeBTYBy5VSPiAIzASagHXAYmCz9XWN1rpL\nKRVRSs0AdgPXA/cP5Y0bGhpy/FFyq7Gx0dE1Or0+GH6NnV09HO3oxZvnHTzSmpqa+LtbLufd3Ud5\nysqh6IkkeaGxwzE5FE1NTdTX19taw2CcXt/pKPTl2n8AHlRKvQ54gWe01ocwQ+LXAi9jXriLYl6A\nq1dKrQFu43hexR3AU8B64E2t9aYCfwYxRpSEi5g8sYxkrLegS8jOGSSH4o+SQzHu5H0krLXeA1xm\n/XoXA9zyrLVeAazIOtYL3DjAuRsxV1IIMWIej4fJk6o43NpGX9zA48lfPGamk+VQPP3yTjZsb+aW\nT81kco3kUIwHsnBRjHuGYTChuoKKsI94tLCj4pPmUPxUcijGC2nCQljCxUVMra0i4E4Qi0YK9r7p\nHIpvL5vLRWdXA/TnUDz42Hre+fBIwWoRhSdNWIgMhmFQVVnGhMpiYpHC7rRcHg7wtewcio4+Hn76\nLX76++10HZMcirFImrAQAwgG/EytrTI3GC1APGamdA7Fgowcig3bm7n/0fW8se2g5FCMMdKEhTgJ\nl8tF3cQqQr5Uwe60Swv6PfzFdYq7v3QJtdVFAPT0xnjiuXd46BdbOHS0sKN0kT/ShIU4haqKMipL\n/cQifQV/7zPqSrl36ewTcyj2tPGdFRskh2KMkCYsxBAUF4WYVF1CosBriuF4DsV9GTkU8URScijG\nCGnCQgyR3+9jyqQq/K44sWjhL5LVpHMoPn0uRUFzPbPkUIx+0oSFGAbDMKiuKqemIkSsgEFAme8/\n9/xJPLBsLnPrJwL051Dc/9h6tujDcuFulJEmLMRpCAUD1uaifQXbvSNTccjH0hvO4xtLLqK6LAhA\nR3eEHz+7jX//r620dRZ+/lqcHmnCQpwml8vF5ElVBD1JW6YnYOAcird3tXK/5FCMGtKEhRihqsoy\nasqDtkxPwPEcinuXzmZGrZn0ms6h+OcnN7PvcJctdYmhkSYsRA6Ym4tW2nJzR1p/DsXCsyWHYhSR\nJixEjmTe3GHX9ITLZXBVwxS+vWwuF2bnUKzYIDkUDiRNWIgcq6ooo7osUNBtlLKVhwPckZ1D0d4r\nORQOJE1YiDwoKgoxZWI5yVivbdMTcDyH4irJoXAsacJC5InH42FKbTVFfgoajZkt6PewRHIoHEua\nsBB5VlleysTKYltuec6UmUPhcZ+YQ/HCut2SQ2GTQm/0KcS4FAj4qakI43fF6Ymk8Pp8ttSRzqG4\neGYNT724gx172ognkvx29ftsereZeWf7GFvbaDqfjISFKJD+W55tXFOcNmAORUsPv369jadekhyK\nQpImLESBhUJBpkyqsO2W57SBcigAVm85nkMh8k+asBA2cLvd5i3P3qStF+3gxByK0pB5k0c6h+KR\nX78tORR5Jk1YCBtVVRzfz87u5WLnTK/gLxZUSg5FgUkTFsJmwYCfaXXVeIgVfBulbB63ITkUBSZN\nWAgHMAyDiTUVlId9tmyjlO2kORSPSw5FrkkTFiLPkskUqzbs4cXGdlZt2DPoj/Ul4SLqJpQSj9o/\nPdGfQ3HbXGZ9ogowP4vkUORWwdcJK6V8wE+BM4AO4C7rW48DSaBJa32Xde4y4HYgBizXWj+nlAoA\nK4EaoBO4VWstvxuEY72yaS/Pr/uQnmMRWtd9CMDCOdNOer7X62VqbTXNh48SS7hxe+xdzl9eEuDO\nL8xiiz7ML1btpKM70p9DMee8iXzxmk8QDtmz7nkssGMkvAzo0lrPA74O/C/gR8A9WusFgEsp9Rml\n1ATr+/OARcD3lVJe4E5gq9Z6PvAkcJ8Nn0GIIdvd3Dno44EYhsGkCZWUhNy2r55Iu0jVcP8yyaHI\nNTua8LnACwBa613AOcDFWus11vdfABYCs4G1Wuu41roT2AXMAq4AXsw499oC1i7EsE2fWDLo48GU\nlYapKQ8Ri9o/TwyD51D8m+RQnBY7mvBbwA0ASqm5QF1WHV1ACRDGnK5I6wZKs46nzxXCsa65dCqL\nL5vBtGo/iy+bwTWXTh3W80OhILXVJcSj9t5llykzh8LrMf/47rByKJ5f96HkUAyDHZNNPwHOUUqt\nBl4HGoFJGd8PA+2Y870lWcfbrOPhrHNPqbGxcWRVF4DTa3R6feDcGis8sKihDGhly5bW03qNZDJJ\na1sXuAO4XPkbPzU1NQ353MlhuOnKcv60rYt9rVHiiSS/W/0Ba7fs5eoLwkwqz/1c8XDqGw3saMKX\nAq9orf9eKdUATAOalVILtNavAZ8CXgU2AcutC3lBYCbQBKwDFgObra9rBniPj2loaMj5B8mlxsZG\nR9fo9PrA+TXmqr7mw0eI5umCXVNTE/X1w4/wuXx2ig1Nzfzq1V309MY42hXnv15vY/7FdXx2/lkE\nA7mp9XTrczI7mvAu4DtKqXsxR7ZfxRzRPmpdeHsXeEZrnVJKPQysBQzMC3dRpdQjwBNKqTVABLjZ\nhs8ghG0m1lTS1t5JR0/UtjS2bOkcivozK/nVK7vYsL2ZFPDam/t5a2crSxaezUWqxu4yHangTdha\nTrYw63AzcNUA564AVmQd6wVuzFd9QowG5WUl+LzHaGk7htcfsLucfsUhH1/5s/OYe/4knnpxBy3t\nvf05FLM+UcWShYryEufU6wRys4YQo1RRUYhJ1SXEIs5bkXDO9Aru++qcj+VQPPDYev7YKDkUmaQJ\nCzGK+f0+ptZWkYz12hqLORCf181nF5zFPUsv7c+h6IsmeHrVTn64UnIo0qQJCzHKuVwuptRW43PF\nScSdF8Y+uSb8sRyKDw9IDkWaNGEhxogJ1RWEgy5iUedtZ39iDkU1IDkUadKEhRhDystKqC4LOCKJ\nbSBmDsUFfO1z51Na7Afoz6H46e+303XMeX+B5Jts9CnEGFNUFMLj8XCwpR2vP2R3OQO6SNUwc3oF\nv3ntfVa/uY8UZg5F0/utfPGas5lbPxHDME75OmOBjISFGIOcfMEuLej38BfZORR9cZ547h0eGkc5\nFNKEhRijnH7BLm2gHApt5VC8sG43iTGeQyFNWIgxbkJ1haMiMQfidrtYNG869311DjOnlQMQTyT5\n7er3Wf74Rj7Y33GKVxi9pAkLMQ6kIzGjEecksQ2kpjzEN5ZcxNJPn0tR0AvAgZYefvjkZn7+B000\nNvZGxXJhTohxIhQKMtnr4cChNty+oGMvfGXmUDzz6i7WN6VzKPax2e/CCB0eUzkUMhIWYhzxer1M\nravGlYyQSDj7JonikI+lN5zHN5ZcRHVZEICeSJIfP7uNR379Nm2dzlyGN1zShIUYZwzDoHZiFSFf\nypE3dmQ7IYfCGryPpRwKacJCjFNVFWVUlwUcP08Mx3MobryyYszlUEgTFmIcKyoKMXlCGfHosVGx\nUWdViXfM5VBIExZinPN6vUytrcaVipJ0+DwxZORQLJvLhWeP/hwKacJCCHOeeEIlPneceCxmdzlD\nUh4OcMfnR38OhTRhIUS/spJiysM+R9/Yke0iVcP9y+Zy1cWTSS+627C9mfsfXc8b2w46fppFmrAQ\n4gQl4SImVBYTGwUX7NKCfg9LsnMoemP9ORSH25ybQyFNWAjxMcGAn7pRdMEuLZ1D8Zn5Z+JxfzyH\nIu7AHAppwkKIAaUv2LlTUUcHAGVzu1186rLpfOu24zkUsbiZQ/E9B+ZQSBMWQpyUYRhMmlBp7dgx\neuaJ4VQ5FDvo7XPGXyzShIUQp1ReVsLEymIS0d5RNT2RzqF4YNlc5tZPBLByKPZz/2Pr2aIP21sg\n0oSFEEMUCPiZUluF14gRj4+OZWxpA+VQdHRHHJFDIU1YCDFkhmEwobqCsiLPqJuegKwcCiuIwu4c\nCmnCQohhKy0x84lj0dGXZJbOobh36WxH5FAUPE9YKeUBngCmA3FgGZAAHgeSQJPW+i7r3GXA7UAM\nWK61fk4pFQBWAjVAJ3Cr1np03acoxBgQCgWp9bg5cNi5G4oOpq6mmLtvuYTVW/bxm9fepy+a6M+h\nWDh7Kp++fAY+rzvvddgxEl4MuLXWlwPfAb4H/Ai4R2u9AHAppT6jlJoAfB2YBywCvq+U8gJ3Alu1\n1vOBJ4H7bPgMQgjA5zu+oehoumCX1p9DcdtcZn3ixByK76zYwLu7j+a/hry/w8ftBDxKKQMoxRzl\nXqy1XmN9/wVgITAbWKu1jmutO4FdwCzgCuDFjHOvLWTxQogTuVwuJk+qwj1KAoAGUl4S4M4vnJhD\n0dLey7/9Ykvecyjs2N6oG5gB7AAqgT8Drsz4fhdQAoSBjqznlWYdT597So2NjSMquhCcXqPT6wPn\n1+j0+mBkNbZ3dtMXd+Px5La1pFIp3v2ojyNdcd7Zu4lzpgTysj2TF7jp8hLe2NHNtj3mbdsbtjfz\n9s5DXH5umJmTc/++djThvwNe1Frfq5SqA/4E+DK+HwbaMed7S7KOt1nHw1nnnlJDQ8PIqs6zxsZG\nR9fo9PrA+TU6vT7ITY0dnV20dUXx+vw5qgpef3s/7x3aR29flLZjLurqKrh8Vl3OXj/bxRfBB/s7\nWPniuxxo6aEvluKVtzvZ1+7mLxfNpKY8d3PgdkxHHOX4SLYd8y+CLUqpBdaxTwFrgE3AFUopn1Kq\nFJgJNAHrMOeVsb6mpzGEEA5QWhKmpqIopzt27G/pHvRxPqRzKD67IL85FHY04YeABqXUauBl4J+A\nu4AHlFKvY/5E8IzW+hDwMLDWOu8erXUUeASoV0qtAW4DHrDhMwghBhEKBqwdO3LTiOuqiwd9nC9u\nt4tF8/KbQ1Hw6QitdQ9w0wDfumqAc1cAK7KO9QI35qU4IUTOeL1epkyq5MCho6RcPlyu0x/zzTu/\nFoCtO/ZywczJ/Y8LJZ1DsaGpmV+9uoue3lh/DsX8i+v47PyzCAZOr53aMScshBgn0isnDre20RtL\n4PF4T/N1DC6fVUepu436+vzNBQ8mnUNRf2Ylz7y6i/VNzf05FG/tbGXJwrO5SNUM+3XljjkhRN7V\nVJVTVuQhGhl9d9hlGzyHYuuwX0+asBCiIEpLwkysCo+qHTsGM3AORcuwX0easBCiYIIBP1MmVYza\nO+yypXMo7ll6aX8OxXBJExZCFJTb7WbypCo8jK4dOwYzuSbM3bdcwp1fuGDYz5UmLIQoOMMwmFgz\nOnfsOBmXy+jPnxjW8/JQixBCDEl5WQk1FUXExsAFu9MlTVgIYatQMMDkiWWjbuukXJEmLISwncfj\nYUrt2JonHippwkIIR0jPExcHXcSi+YuOdBppwkIIR6koK6GqLDAmbuwYCmnCQgjHKS4KMakqTCxy\nzO5S8k6asBDCkQIBP1MmVY6ZGztORpqwEMKxxuKNHdmkCQshHC3zxo54PGZ3OTknTVgIMSqUl5VQ\nEnTldMcOJ5AmLIQYNTJ37Bgr88TShIUQo4rX62VqbRVGMkIikbC7nBGTJiyEGHUMw6BuYhUhb4pY\nbHTf2CFNWAgxalVVllER9o/qJDZpwkKIUa0kXMTEyuKc7excaNKEhRCjXiDgZ/LE0bljhzRhIcSY\nMFpv7JAmLIQYM064sWOUXLCTJiyEGHPKy0qoLguOihs7PIV+Q6XUrcBSIAUEgVnAlcBDQBJo0lrf\nZZ27DLgdiAHLtdbPKaUCwEqgBugEbtVaHyn05xBCOFsoFGSy18PBw+24vAEMw7C7pAEVfCSstX5C\na3211vqTQCPwN8C3gHu01gsAl1LqM0qpCcDXgXnAIuD7SikvcCewVWs9H3gSuK/Qn0EIMTp4vV6m\n1FbhcvCNHbZNRyilLgHO1Vo/BjRorddY33oBWAjMBtZqreNa605gF+ao+QrgxYxzry1s5UKI0cQw\nDGqtGzucGABk55zwN4H7BzjeBZQAYaAj43g3UJp1PH2uEEIMqqqyjNKQx3FbJxV8ThhAKVUKnK21\nXm0dSmZ8Owy0Y873lmQdb7OOh7POPaXGxsaRlFwQTq/R6fWB82t0en3g/BpHWl/PsV66ehN4vP4c\nVTQytjRhYD7wSsbjLUqp+VZT/hTwKrAJWK6U8mFewJsJNAHrgMXAZuvrGoagoaEhd9XnQWNjo6Nr\ndHp94PwanV4fOL/GXNXX2xfh8JFOPL5gDqoaGbumIxTwQcbjfwAeVEq9DniBZ7TWh4CHgbXAy5gX\n7qLAI0C9UmoNcBvwQEErF0KMekFr66RUvI9kMnnqJ+SRLSNhrfW/ZD3eBVw1wHkrgBVZx3qBG/NZ\nnxBi7HO5XEyeVEXrkXZ6Igk8Xq89ddjyrkII4RBVlWVUlvqJRfpseX9pwkKIca+4KETdhFLikWMF\nDwCSJiyEEFg7dtRVF/zGDmnCQghh6b+xw5ciHivMjR3ShIUQIktVRRkVJb6C7NghTVgIIQYQLi5i\nUlWYWORYXt9HmrAQQpyE3+9jam0VyVhv3tYTSxMWQohBuFwuptRW43PF8zJPLE1YCCGGYEJ1BWXF\nnpzPE0sTFkKIISotCVNTUZTTGzukCQshxDCEggHzxo5obm7skCYshBDD5PV6mVpbjSsVJTnCGzuk\nCQshxGkwDIPaCZUER3hjhzRhIYQYgf4bO05zntiuUHchhBgzwsVF+LxeDra0k0zGh7Wts4yEhRAi\nB9I3drR8+GbncJ4nTVgIIXLE5XLR/P7GjlOfmfGcfBUjhBDi1KQJCyGEjaQJCyGEjaQJCyGEjaQJ\nCyGEjaQJCyGEjaQJCyGEjaQJCyGEjaQJCyGEjWzJjlBK/RPw54AX+N/AauBxIAk0aa3vss5bBtwO\nxIDlWuvnlFIBYCVQA3QCt2qtjxT8QwghRA4UfCSslFoAzNNaXwZcBUwFfgTco7VeALiUUp9RSk0A\nvg7MAxYB31dKeYE7ga1a6/nAk8B9hf4MQgiRK3ZMR1wPNCmlfgP8Dvhv4GKt9Rrr+y8AC4HZwFqt\ndVxr3QnsAmYBVwAvZpx7bSGLF0KIXLJjOqIKc/R7A3AGZiPO/MugCygBwkBmEEY3UJp1PH2uEEKM\nSnY04SPAu1rrOLBTKdUHTM74fhhox5zvLck63mYdD2ede0qNjY0jLDv/nF6j0+sD59fo9PrA+TU6\nvT4g1dDQMORMYTua8Frgb4B/VUrVAkXAK0qpBVrr14BPAa8Cm4DlSikfEARmAk3AOmAxsNn6uubj\nb3Gi4fwHEUKIQjJysVvocCmlfgB8EjCAbwK7gccwV0u8CyzTWqeUUl8Fvmadt1xr/RulVBB4ApgE\nRICbtdaHC/4hhBAiB2xpwkIIIUxys4YQQthImrAQQthImrAQQthImrAQQtjIluyIQlFKGZjZFLOA\nPuA2rfUH9lZ1nFLKA/wEmA74MFeA/N7Wok5CKVWDuSzwWq31TrvryZSdRaK1/qnNJZ3A+v/8BOb/\n5zjm6h9H/DdUSs0BfqC1vlopdSYDZLjYLavGC4GHMf87RoAva61bnFJfxrGbgb+24hkGNdZHwp8F\n/NZ/iG9iZlQ4yS1Aq5WD8Sngf9pcz4CsJvLvwDG7a8k2QBbJFHsrGtBiwK21vhz4DvA9m+sBQCl1\nN/Ao4LcOfSzDxbbiLAPU+BBwl9b6k8CzwD/ZVRsMWB9KqYuAvxrqa4z1JtyfM6G13gBcYm85H/NL\njgcQuTDT4pzoX4BHgAN2FzKAgbJInGYn4LF+MisFojbXk/Ye8LmMxw1ZGS5OyGXJrvEmrfU269ce\noLfwJZ3ghPqUUpXAd4FvDPUFxnoTLuHE/Im4Usoxn1lrfUxr3aOUCgO/Au61u6ZsSqmlwGGt9SrM\nm2acpgpoAL6ImbD3lL3lDKgbmAHsAH6M+eO07bTWz2L+WJ+W+f+3C/MvDFtl16i1PgSglLoMuAv4\nV5tKS9fTX5/VWx4D/h7oYYh/XhzTkPIkM2cCwKW1TtpVzECUUlMwb9N+Qmv9tN31DOArwEKl1B+B\nC4H/tOaHneII8JKVtrcT6FNKVdldVJa/A17UWivM6xP/ad2O7zSZfzaGnMtSaEqpmzCv9Sx2WJb4\nxcBZmD81/hw4Ryl1yinQMX1hDngdM63tGaXUXGDbKc4vKCsz+SXMOa4/2l3PQKz5QQCsRvw1h90m\nnp1FEsJszE5ylONTTe2Yf+7c9pVzUm8qpeZrrVdzPMPFUZRSt2Bu9HCV1tpJf0kYWuvNwPkASqlp\nwM+11n9/qieO9Sb8LOYo7nXr8VfsLGYA3wTKgPuUUt8CUsCntNYRe8s6Kcfd427ttnKlUmoj5o9/\n/7fW2ml1PgT8RCm1GnMFxze11nbPZQ7kH4BHrc0T3gWesbmeE1g/7v8bsAd4VimVAl7TWj9gb2XA\nCP5sSHaEEELYaKzPCQshhKNJExZCCBtJExZCCBtJExZCCBtJExZCCBtJExZCCBtJExZjnlJqmlIq\nqZR6JOv4hdbxL5/Gay6z7txCKfXT03kNIUCasBg/jgCLrBCdtJuA07377zIykrOEOF1j/Y45IdK6\ngS3AfOA169hC4GUApdSnMdOvDOADzNuzW5RSHwJPYqa1hYAvAxWY+cVXK6UOWq91g1LqLqAG+J7W\n+tGCfCox6slIWIwnvwT+LwCl1CXA25ixkhMw083+XGt9IbCOE7OdW7TWc6xz7tFav4IZm/ktK10O\nzNzqOZhZJcsL8WHE2CBNWIwXKeD3mME0YE5FPI058j0GbNBaf2R97z+AazKe+5L1tQlzFDyQ3wJo\nrbcDlbkrW4x10oTFuKG17gHeUkpdCVyNNRWB+ecgc67YxYlTdX3W1xQnz4iNn+S4EIOSJizGm18B\nPwA2Z2RLB4E5Sqmp1uPbOXWMY5yTX1NxYvi9cCi5MCfGm99j7n6Q3sUkBTRjNt7fWDGOe4CvZnx/\nIC8Dy5VS7QOcI9GEYsgkylIIIWwk0xFCCGEjacJCCGEjacJCCGEjacJCCGEjacJCCGEjacJCCGEj\nacJCCGGj/wNw1GmPTvAXxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1342ac128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date']= df['timeStamp'].apply(lambda time: time.date()) #toextract date you need to write date()"
   ]
  },
  {
   "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": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rJlVlcK8Re3Csp2LGPkMVsDx3oDnKPW+YCHpVADUod2P5zVCVyl3SCDK4LwGyurHoScDcPJW7veUvYFXLAM1R7lndRNinsZ+r1bkvn4RqTi48IlkA7IJH2jLniI/e8Tw+d+/eRX0Pe3CsR7mb1Knc4wHLc59ILH5wzxmmaDNc3XNfPqWQOfZdyUlMkkaw555asb/MsqhzPzqWWPSDbx/i16vcfVohuGuqgligOf1lcrqJjrDV0a4ez73VZ6jym1O2xW9SknOL3Z6Utsw5YiaVx+wid1qct+dO4bBlAKAj1JxZqjndRETYMtWrZbyadTq0ui3DSzml5y5pBN3uuS9XW4YQcpwQsocQ8hIhZBd7rJ0Q8iAh5BD7v409Tggh3yKEHCaE7CaE7FjMD6AbJuayehOCu+H6czUM03SUQgLN6y+TNwqeezVbxjApAh6WfG3x4J6T1TKSBeC1pNzfTCndTinli3Z8BsDDlNINAB5GYdHstwPYwP7dDOA7C7WzbvAl6xa7R/r8q2XgmKEKWOWQk8nFn8SU002xQEgtnjsP7q2u3GW1jGQhsF8Hrei5N2LLXA/gdvbz7QBusD1+B7V4BkCcL6S9GEwzBZzMLW7FzHw9d2uGqvOx9pCnKf1lcoa9WqYG5c7KJlu9zl0qd8lC8FqplqEAfkkIeZ4QcjN7rIcvfM3+72aPDwA4ZfvbIfbYomBX7ItpzdinstfnuTvbDwCWLTOZyi1qvwrTpMgbtPZqGYPCz5S7Xkd7haUGX1QFkNUyksawV8ukW3AUWGu1zBWU0jOEkG4ADxJCDlbY1m3xzZJowW4SNwPAypUra9yNUqZtAX0mnRfVIQtNI8q92JZpD3qR002kcgZCvsUpWOKq1e9RoSqkpjp3v0dx/G0rYvdJW7EfiGTpYB/BLlvPnVJ6hv0/CuAeAJcCGOF2C/t/lG0+BGCF7c8HAZxxec1bKKU7KaU7u7q65v0BZmwNuGar9CxvhKxuCgW+EModWNxZqtyi8mkKfJpSU28Z4bm3cOMwR5tjGdwlDWAfwS5Lz50QEiKERPjPAH4LwF4A9wG4iW12E4B72c/3Afggq5q5HMAMt28Wg5ki5b5YZHUDUX9tsz3tFLcfACzlDizuLFUe2DyqAq+m1NQV0qspUEhrJ1TnO5NYIikm3+KlkLV4Aj0A7iFWgNIA/JBS+gAh5DcA7iKEfATASQA3su3vB/AOAIcBpAB8eMH32oa9de7iBncTEb8HU6l8Xco9qxfqxznNUO48mHuZcq9WOcK7V3pUpaWDez0LlEiWP4msjocPjOD67fWn/Vq9FLJqcKeUHgWwzeXxCQDXuDxOAXx8QfauBpqm3PMm/B5LBdejCNM5Q1ShcNqb0F+GK1hvHcpdVQi8qtLSjcMc3TtbMAkmWVju3z2MT/94N7YOxLC2K1zX3zomMbVgcG/5GarT6Rw6WRJ1catlTPg0Ff4aVDCHUopUThedGTncllnMWncR3DUFXrUWz51CUxRoKmlp5c5vYoRI5S4pCL7T0+m6/zbPPPeAR0WmBW2Zlg/uM6k8eqI++DRlUYN7Vjcsi8Oj1qzcs7oJkwJBr3OAFPFrUBVSsdb91ieO4gO3Pjvv/XXaMtX3mSv35WLLhH2aVO4SzGWsmDA8nan7b3m1TMSvSeV+LphJ5xELeBANeBbdlvFpCvwepWbPnWfYeRUKR1EI2oIeTFawZfacnsFTR8bnXfFRbMvU0luGe+6trHj55474tJb+HHaeOjKOr/xs/7neDfzZj17EF+7bd653oy7mslYF3ZmZ+pU7X50sGvDI4H4umE7nEQ96EFvs4K6brKywduWeYkO5YlsGsFoQVFLuiYwOkwJDU6l57a/DlqkhuJumddPxaq3tufPPHfZry2YS0wN7z+K2Xx87p4s0U0rx2CujeHD/yDnbh/kwx8qj56Xc2XUQ8WstWS3T8sHdUu7eJgR3w/Lc61DuPLgXJ1QBNku1QnDnJ+WJyXkG96JqmVrq3C3lThyJpFbiyFhCKLWI37NslPtUKg+T1je/opg7f3MS33jo1Xn//dhcFrMZHaen003paLpQJDLzV+7c4ov4Pcuzzn0pQynFTMqyZZqi3D31Kfe0UO6lRUntwcqdIXmQOjnRoHJXawvu3HPXlNb03DN5A+/45hP4wdMnAFiee96gMFu4lQKH909KZOc3SS9vmPj7X7yKW5+Yv/o/NJoQP+85PTOv1zgXzGWZ5z5Tv3LnvWWi0nNvPum8gZxhIh70IOrXMJuZf3A/PJqo+Pc5vX7PPZWzLkZXWyZUuTNkgp2UxyeSNb1XMXmj2JapfHKKOndNEcvUtRIz6Tyyuonj49bxEt0wW/BGVQwXLfx8qpfHXx3DeCKLRFbH0FT9ChYADo3MiZ/3DE3P6zXOBUK5T6frvrHx3jIRv2fBbZknD4/j0VdGq2/YAEsyuD97dAJ/cedLVVXXaXaidoZ9lnJPzS+4U0px43efwhfvK5+0sjx3tT7PPV/elmkPWasxlTvhuC0zX+Wetc1Q9Wlq9Tp3g0JVFPhUpSW9an68RueyAFCYTbwMKmamGlTudz8/BN4B4+DZucobl+HQaAKxgAdrOkNLUrmfnEi52kV8BJzKGZhN13f8eBsOrtwptUaCCzEafP+tz+LD3/sNjo4lKm737NEJbPviL+cV25ZkcP/14XHc8+Lpqn7zY6+MAQBet64D0YAHc1l9XsPO2bSOqVQeD+wdLnuHzuYN4V9z5X5oZK6i2k9XSagaJnU9ISmlQnHU6rkXn3DchvHVU+euErSHvJhoIU+Vw0veeAAUrY6Nc3Ojuus3p+Z9Yy6Gz8JOzUM9Ukrx6CujuIHN0DwwPDuvfTg0msD67jC2DsSwZ2jpBfcPf/85/MMvXyl5fC6jixu9m+/+rYcP4bp/esL1NXXThEKAFe1B5A2Koak03vqNx/G1Byr1TayNzrA11+UzP9lTMWa9MjKHmXQeZ2edtlItcW5JBnd+Eu+uMvx79JVRbOyJYCAeQNCrgc4z6cS/9GTOwCMH3YdKWWHLWMrdNCne/e2ncOvjR6t+jqCn1HPfviIOQoA//rcXShRZVjeFTXJyMlVVKVBK8bZvPo5/fvSweMyRUPVUr5bhnntP1IeR2fr9yXPNXFHTuLDPWoj8XCj3nG7i0z/ejbt2naq+cRV0wxSfbT7KPZM3kcmb2NATwaqOIA6edQ/un793L37ywpDrc5PJHA6PJrChO4zz+6I4M5NpyAJdDM7OZERuwk4io2NjbwQAMOwS3HedmMLe07OuCdO8QaGpCraviAMA7nv5DA6NJnDH0yfm7RJweB7uuWOTGEtky27Hv/ukzZIbmc3ggs/9Ai+enKr4HksyuPPkxd4Kw7+5TB7PHZvEmzZZHSW5Op6PL8m/dIUA9718uuR53bCCrWXLWMp9ZC6DuaxeonJPTabEbLg02xc3W2bn6nZ8870X4TfHJ3HrE84bBP9CN/REkNPNkrt2MWdnM3h1JIH9NlXmqHOvSblb1TLdUT/mMvq8/d1zRXHgC/trW6RkMeDHbiECoL1IIDmP4M7/Phbw4PzeKA4Ol9oyE4ks7njmBO550Tr37Qn1h/aPYMdfP4jJZA7n9USwpjMIADgxvjCjkoUgp5tI5oySG3neMJHOG9jQYwX30y7lkCdZTsut5DhvmPAoBBt7I/BqCm5/6jgAKz7duetkQ/uczOroiVoz60dmygd3fg7ZHYVDIwmk80bV/MnSDO5CuZcP7k8dmYBuUly90VojJCCCe/1D1zPsS79ifSeeP1F6N+Qq2OexlHvGdmCL3+/Td+/GX9292/Gcmy0DAO/c1o8r1nXixy8MOdQ5txg2VVAcdvgweWyucJKUJlTLBznTpDApoCoEvVE/AGB0tvwJtxSZKwqkkRrXjl0M+I1mIWZM29crSGXrP7ftwX1TXwTHJpIl1uOvD4+DUquo4MWTU7jgcw8IS+kUC3pfun4zfu+SFVjVEQJQW6I/qxtNqc3nir34HOc3w7WdIXhUInJ0HN0wxXV8wsVC0w0THk2BR1WwuT+K0bksYgEPLl3djrt2uY9yaiWR1bG+2+p1U+n65kLPHmf4yFo3K5/bSzq47zszW9aSOMtKm/gBCjYQ3Idn0tAUgotWxDGeyJUEBK4IfJqCHqZs97FRRbGamkhmcWh0zrEvxTNU7bzn4kGcmkzjueOT4jEeHFa2WyppqkoPGp7gGrcF91xRQtUwKYwyx9JgF6CmEPSw4F5ttLDUKLFl5tGeeaFIsiBcvE/zwW41zMeWsQf39d1hUFoamB9/dRyAVS74wN6zyBsUx9g2fBT9eztXIOzTsKqDKfcqwX0ymcNFX3oQv3p1rO59rpcpZpEUf9f8+McCHgzEAyXq/Mx0RvRsP+mS28qzfksAsG3QsmYuWd2GzQNREX/mg26YyOom1rNGZpWutUJwL3z3fHu9SlXbkgzuvMokkdXFSVYM98j40nCN2DJnpjPoifox0BYAgBLPWSh3TcV5PdYX8ghL5hbfTBIZHSOzWaRyOtJ5A36PAkVxW5zK4q2bexH2abjnhdOO1wAKwb1SmwKgMMIZcw3uRLQcLqdiedBXFaUwVGz14L4UlPsC2DL2ltaVzu1kVsfzJyZLHufBPRrQ0Bezzm97YKKU4olDY4gHrRwFt2b4TSWTM0CIJWwAyyvuifpwvEjpFt94Dp6dRSpn4MhY+ZvAQpUXTpVR7vyciPg1DLYFS2yME5OFfbMH991D0/iD255FKqvDo1rX7rYVMQDAJavbEQt4kMjqZSf77T09g4//8AV87N+ed32e3/xXtAfhUUlJDb79JsVHpPY4M8quzXJijbMkg3smZ4iTrVx2P2urBgGAAEtazueEOTOdRl/Mj15+8hcFNn4j8WoKzmP+3TNHJgCUXnD8JD85mWIdISt3VQ54VWzuj+LYeOFE4ytKrezgyr18cKeUitzEXFYXnz9rWH3kCSkE93IqlqsXTSHoiTXXlhmby+Kr9x9wTYbVgz24qwoRN/uF8txPTaYqVpp85se78Ye3/wZPHRkveO41lN7phllx9qM9uCcq2DL/9uwJ/N7/eaYkyNqVe3/c+m7tweT4RAqjc1n8/iXW4mm8lLRQW28g4FFBbAvOrOoIifkEZ6bTuOlfnsP2L/4S+84UbNTjzJMvtqZ++OxJ7B6axtNHrBI/N687ldPrmkgnbBmb5/79J49hF7vZhX0eDLYFSoM7u0G1BT2OyqYnD0/giUPjODWVhoetbv+GDV24dE073r6lD7GAFZvcVn4zTYr//v3f4Ge7h3H/nrOuo61ErnDT6Yn6HTfbV87O4YLP/QJfuG8fcrpZxpaxvqN8Kwb3VF7Hed0sCVImaZDVDWgKgaZyRdGILZNBXzyAPhbYiodcvORyTWeIVeYU6sbt70cpFV/m8fGUuDCq0VY0W5W/Rk/ED6+mVFTuZ2YymEjmsG3QUhZcved0Ez52bHzVlLvBlTtBxKch4FGbotxHZzO45CsP4ZbHj+KZo6Wqsx74pC+gttFKvfyv/9yL9/3fZ8omNX++9yweOjCKP//3l8Q2xXkAN/7+l6/gxu8+XfZ5fl5oCqmo3I+Np2CYtGSWtj24d4V9UIjT4+Wvf8mqdseiMvymks4bJTmj1R1Bodz/6ZFDeProBBSF4IfPFpKMx8at+m37TZdSii/+1z58/6nj2D88i5xhlpRVJrI63v7NJ/C5ewsNytI5o6JKLbZlTJPiiz/dj289bFWPRfwaBuIBjCeyjhvpiYkkvJqCnavbHcqdX0NTyRw0ptw7wz7c9Uevw8qOoAjubjPiXxqaxuhcFldvsnKBwy6thvn5EfJp6Iv5Hd/HqyNzMEyK7z91HLf++qg4h9IutoxR5QZYc3AnhKiEkBcJIT9lv68hhDxLCDlECLmTEOJlj/vY74fZ86trfQ9OOmegK+pD1K+V7cOcYV0aOSK41zkBxzQpzs5k0B/3F/xmW3DPGyZuefwodq5qw46VcSgKwYbuQtN/e4lSJm+1+AUsXzOdK70w3GgLFQV39oVG/JrVpqCCcj/I1OQbz7OqhsYSGbHf/GItKHf3k4EnZjSVgBCrHNLNB6xFXdejwH/wzAnxczrfmD9tDyJelmcAFoyPjtEAACAASURBVEa5U0rx8tA0plN5/Oi50ioJSqm4YKeSOaGwa1nT9+DwHA4Mz5YNXjPpPBQCdEd8FT13fp0UK2UegCJ+DzTVyhnZlTsf6YX9GtbZFrPgf2dZi0XBvTMkZryens7g/N4Irtvah/teOiNe7xhX7rYbXDJnIKubGJ7OiKBnb2sAAH/3wEGcmEjhhRNTME2Kv3vgILZ98ZclFWUAcHh0Dt946FVx7fAy6GROB6XAOCsxDPs1DLZbo3K7ej8xkcLK9iBWdwRxcjIlkr+8NHEylYNHKQ2R3FVwC+4P7R+BqhC8/7KVANz7yCdswb03FnDEmwn23n6PgiOjSVflPioSqgun3P8cwAHb718D8HVK6QYAUwA+wh7/CIApSul6AF9n29XENJuxmWaKd6AtWFG52086Xi2TrtNzn0jmkDNM9McCiPo1BL2q4+T/+d6zOD2dxsffvF4MTXlpFeCsYJizqccTE0mkag3uQQ+mU3lxctm//GoNxo6wGW6Xr+0A4FTuniLlXi7QFTx36/N1R/0ltszuoWls/9KDjoBczNGxBC7+8kP4+Z5hfP/JY/gfP3D3HDlnpjPQ2Hsm51EJYieR1UWFjJc1eAMWZgWdoak0plN5eFUF3/3VEXznsSOOC5vPS4j6NegmFd9BIqtX9UVHZq2k3rhLrfND+0cwPJNBLOBB2K9VrJbh9kZx7mE2nRfrBwBAb5FStE+029Adhkcl6Az7CsrdZfS5mlXMnJhIYmwui66ID79/yQrMZXXcv8daLpknbe2jFx64hmfSGGYByh7cR2czuOPpEwj7NBwZS+CX+0fw7ceOIGeYrpP5bnn8KL7x0CG8wmbdcuVefBPknrv9OAGWdbqqPYiV7UFkdVNYUmNz1r7NpPPwaKX5Mq7c7UJmNpPHT14YwgN7z+KyNe3Y1Bdln7VUJHEhEBbKPSOu/fFEDgoBNnRHMJbIlgR306RiPxckuBNCBgH8NoBb2e8EwNUA7mab3A7gBvbz9ex3sOevIYSUzygynj8xhe1fehA/fuG0GApaGe5albt1Yddry/ATvTfmByEEvTE/zs4W3nPv6Rl4NUUoYwDYyIJ7POhxKPeE7cI6Pp5yXWLPjbagF7pJxVTpuYxuzSzVFLSHPJhM5vBPDx/Cl39a2h7h8GgCnWEv1rNErz24c8Vut2XyhlmiOOyeOwD0Rv0YmXOelHwY/vl79+LJw+Oun2PX8SkYJsW3HjmM/+/BV/HAvrOuQYszOpfBmk4rUNSTCE/nDHzxv/YVXVw6BlkC2qsSxAPWDMCFaCbHq5E+/84LEPZp+NoDB/GDp4+L5/k51x+31OEZm1pLVFHv/Ps6U6TwXjk7hz+8Yxfufn4I8aAXIZ/mONfsUErF37spdx6MAKA/FnC0v+Uj3aBXxR+/aR3+4cZt6I74MJO2jq2bLcMT/ScnUiK4X7qmHWs6Q7hz1ykYJhUetj3vMJ6wXvPMTEbs72FbcOfPv2FDJ3ST4q5dp+BRrZFkqihgmybFo8wuffGkNdmRi5fiG1yEee6AU7mPzGbQF/eLQMxvTPw7oRSiWsaOmy3zvV8fxyfvehlHx5N46+Ze9EQsC6z4ewUKwT3oVdEb9SOrm+JmOpHMoj3kQ3fEh9HZjLhR8etjIpkT1+tCJVS/AeDTALj06wAwTSnlR3EIAF+BdgDAKQBgz8+w7R0QQm4mhOwihOwaGxvDp+9+GYDlOXGverAtgNNlGv5kddOh3OfrufNJSHypPn4n5ZyaTGGwLeCoeNk8YJ0MWwdiyORNcZC5+oz6NUu556snVIHCgtncfpnL6oiwUj7Lj8/jZ3uG8UuXXtqHRxNY1xVGR8g6mURwd7FlcsxiuvYff+U4MezVMgAsW8amJgBgkgVpj6rgvpfOuH6O3aeti+zA8Ky4wNzmDXDG5rKibroe5f7y0DS+9+Rx3P18odZ4LpPHCnYBezUFEb8GQoAZMWQ38OD+kXn1Bdk9NAOPSvCeiwfx2F++Gef3RfEUS6hb+259Vp6zsSvjShUzecMU51+xwrPPzo4HPQh5tRK/n3/XE8mcsCT4+93z4hD+8PZdJcG9t0gpFibaaTi/L4rrtw8gzkaS1vOltgwPlKemUphMZtEV9oEQght3DuK5Y5N48vC4yEnNuij3nG6KyVRHxhLi/OMB7KKVVtnho6+MYstADG1Bb8l1vX94Vnx+7pe7BXdVIfB7FHRH/PCoRAR3k+Un4gEvdq5qwxXrO/D1B1/FZDLnqDrj1TJ2ojyhagvuTx4ex6beCP71I5fhfZeuhKYq6I36y9gyzApjyh0ofP/jiRw6w150RXyOklX++e25sGpJ56rBnRByHYBRSql9jO2mxGkNzxUeoPQWSulOSulObzguSqZMkyKrmwh4reCeyOquVQcZ1uuF49MUEFJ/tcysLeEEAL1Rpwc2NJUWQzrO69Z24L4/uQJv2NAJoHBScltmy0AMZ2YymEzkalTu1nvzxFAioyPitx5rD3kxkcjixEQKI7POgEspxZGxJNZ3h6EqBO0hn/ALc7oJr7BlmP+cN3BkNIGxuSxetXX5K1bu67rCyOomnjhUUOiTSWu4eOFgDIfLNDvaMzSDbYMxxAIeXL62HV5VwQsVgvvIbAY9UR+CXrUu+4Rv+4t9Z8VjiYyO9pAXYZ8Gj2qVn8YCHkyn80hkdXzoe8/ho3fsqqkT3/eePIZHbW0o9pyexqbeqDiOr1/XgedPTInkHFfUBeVeOH8qBXf7qKZY4e07MysCSzzgQcinOm6Adzx9HJd99SGcnk47rEse2J45MomHDoxgaCrlCO59MT/SecNRDQMAQVsAjwc9YvJUOl86+owFPAh6Vew5PQuTAl0RSxi9Z8cgVIXgb35+ULyXPdDaZ3On8wZ6oj7kdBOnWHDm+7KlPwZNIaAUuHhlG0I+rSS4P3JwFIQ4g69hUuiG6bBlrJs8gaoQ9Ntq3eey1mI48aAHhBB8/nc2Yy6r419+fcyRK+HWZvHnBwrKPZnV8eKpKbxpYzeu3NAp4lJ/PFBRuVueu1MQjCey6Az70Bn2OVqp8Lg2ahtRL4RyvwLAOwkhxwH8Oyw75hsA4oQQLksHAXA5NwRgBQCw52MAKpZC2HeS+0kBj2XLAMDTRyfw1BGnFWD1Vy+cdIQQhLylJ0E1ZoqCe1/Mj9G5rKhhPTWVEorQ/l4XDsYR8jmtIH7xceVxZibjuGjKEWcLZvPE0FwmL+q024JezGasmvmsbpYMc2fSeTGRyxrKceVOXZU7P758KAsABkuocl/2XTsGsKojiC/9dL9QBxPJHNqCXmzoieDwaKJkNJXTTRw4O4fL1nbg3o9fge9+4GJsGYiWVe453cRUKo+eqB9Br1rX1Hq+WPGuE1NCZc1ldIR9GmIBj/i88YAHU6k8bvnVETx7bBKEAC9XaXpFKcU//OIVR3OovadnsYWN1gDr5p7VTXEM+b6L4G5X7hXKIUdseY1i5b7vzAy2DcbxsTetw3UX9lvKnQ/NE1n8/S9egUmBY2NJh9XAxQq/qfBujhxe687fz21BmVjA61DuxbYMIQQD8QBePmV9fh7cu6N+vPuiARwYnoVCrOvATblzrtrQJfbRvi/RgAdru6wR3cWr2qzzo8iSeuboBDb3R4X/z8nqprDCvKoiriOAWVLsc/PeMPzYnNcTwdrOUMnNX3MJ7j5NRcCjimP03PFJ5A2KK9Y7DYr+eMDVc7c3t+NFHGIUZlPudvjnP2trVdCw504p/SyldJBSuhrAewE8Qil9P4BHAbyHbXYTgHvZz/ex38Gef4RWmYPMn4wHPaJKI+hVxaSiT9z5Iv74X19w/E0mb8CvOXc/4FXrrroo/pJ7Y34YJmXJjDymU3msaA+6/m2oyOfn5XgXrWgT29SaUAWcizLwk7KdWTYc+52b+5W8yqErYlfuhlDu/P9s3hQnkb3pULFy92kqPvv283F4NIGHD1gn+2Qyh/aQF+u6wphJ50t66rw6MoecbmLrQAyrO0OIB724eFUbdp+eEYkuw6Q4PJpAJm+I/eyO+BCs86bMlTulwIP7R6CzHiIRvwdtoUJwjwW9mE7lcIJZaxu6w2X7FR08O4u/uf8AzsxkkMwZOHh2DsfGk8gwlcuFBgBcurYdCrFEB1AYZvM6cntteqVySF71QIq8WdOk2HdmFpv7o/j02zbhdy8etDx3FhRueeJoYfm4mTROT1tqVCGFFrc8qFIKZ3CPO5ViJu+cpASALXzDihtcqmWszxoQdog9EH3tdy/Ew//zjbjvT67Ems4Q5jK6I1mo2uzNK9nIlxcF8Gs36FWxsde6mfLgXpxMnkrl0RsNYHWnM7hn8oY45m+5oAdbB2Liud5YoaZ8muUUuLACgE19Uew745zL4CkzAdG+ONCjB0fhVRXsXNVecoyGpzMlVmAqp0NVCHyaIq5vfj1NJLLoYMpd7INKhHIfmkpBUwi8qlJ1xbRG6tz/CsAnCSGHYXnqt7HHbwPQwR7/JIDPVHsh/uW3h7zihA94NXFBZfJWErC4QsFXdNIFvaojSGTyBu55caiizzqTziPgUUVA4Am+w6MJoYgGi5S7/f2AgnLjF/nmgagIqIEaPHf+BfPFO+Yyupg+31YU3O1qj18UXLl3RXzuCVVPQbnzoPriqYJy12117pxL11gnKg86Eyy48/c6UlTCxoOm/WLasbLNUvTMX/3ZnmFc+4+/wtYv/AL/wTomdjNbph7lzoN71K/hF/sKE0Uifg0XrWjD+SxB1ha0LsDxhOULbxmIYffQjDjfKKX48k/34/DoHH65bwT/5/GjePhAIa/x873DIkjaA2TU78HWgRieZqPJlPDcC+dJB/veKpVD8lHU+q4wztgU3jFWabXZdiyDPhVJdm6fGE+JNgDDMxmcnkoj4tPQGfYJ5W6/VooTqgBwlNmgqZyBYNEkpXjQg7xBkcoZrtUyAITwAoCusF/8rCgE67rC2DIQQ9TvgWFScU1OJHMYbAuI19vQHUHIq4rRZqEXk4YbLx7Eh16/Gt1Rv2PUwklmdYR9Ktay65Uf76xesGX+5ne34jsfuFj8TXfUh9E5K9jyGzAvawSA83sLVXAcN1sGgLD8/uSHL+COp0/g6k3dJfZVf9yPnGFiPOkcsSSzBkJe65j7PSoiPg3jiSzSOQPJnIGOIuXeHfGLY/PK2Tms7QrB51EWtBQSlNLHKKXXsZ+PUkovpZSup5TeSCnNsscz7Pf17PnyPXHF61r/d4S8QrkHPCraQ15R0gY4y5iybsrd4wzujxwcxV/c+TIeLtPGFygssM3Z0m9dUHtOzwgvcEVbGeVeZMvw4WA84BXDylqUe9TvgUKcyp0nVNuDxcG9EARePDmNWMAjkjJdER/GE1mYJnUkVLlXnMjomEzmEPKqODyaEAGA22Kazb+MBzzQFCJ84clkDh3hQnAv9t3PTKdBSKGSAgDOYxcLH2Fwb1hViKgX7474XT3VSnAV89sX9uGpI+PC4w77Nfz1DVvw1XdtFZ9hOpUXFR0XDsQwnsiKG+R4Iodbf30MDx0YFcHjx6wNxKqOIH6+56ywVaK2AAkAl6/rwEunppHK6SKY8KZrAISXWql52OhcFoQAWwdjjskuXD1u7i9YQWGvJqqdUnkD8aAXHSGvFdyn0xhoC1hrGmRKZ8fa970n6sO2wRi+9+RxZHXDKl4oEiBxm6fsVi0DwDGS6Yx4S54HIPJG/AY5wfxkPsLpj/sdo820zSK66rwufOGdmwFYN7aSNh9ZSwBx5c6Pd9Y2qzNU9Ll6o37kDYqpVE6c+3HbsdnUWzjevN+65pJQBazgfmB4Fj/dPYwPvm4VvvW+i0q24TfSM9MZfPdXR/DH//p8Yd9tdlFH2IuJRE5ca51hnyO490R9heA+MofzeiLQFNIa7Qf4LraHvCKJEGR3tpuvWof/8cZ1AJxlTOWUuz2hylXsfS+7V3cApaVisaAHqzqC2DM0g1Ps/crZMkK5s8CQzOpQiDUBgdfC1xLcefKv4LkXarbbQta+8dEDL1HUDROPHBzB1Zu6herqCvuQN6wqgLxOxeiBB52XWAXGVaysk0+n14uqZfg+dYS94hhOMs+9L+pHwKPiyKizZ8gcO2HtVUUr24PQFCJWm5lJ5+FRCXauahcBVij3OkoheSLz+u0DyBtUfL98UQZOnNkyVgWCD1vZLF5eiZIUZWaGOG9ePjWNqF/DWzf34lW2UAJQGtxft7YDeYNi1/Ep8ToRvyZuyvyYV2oeNjqbQUfIhxVtQYwlsmI27W+OTSLoVUWrCwAIciGRNZDO6Qh6VPTFrZr1I2NJrO4IIWJbatLuddvPb0IIPvXWjTg9ncaPnj1pvVbRORoXCf6clVB1U+4suId9WtmKsGhAcxyDiUQOHSEv+uMB+D2KNWs24hN15eW6qLpVCiWyOkI+TXju/HhndQNzGR0hr+oYidq3OTubEQnjmF25226mvIqrnHKPBjwiHr3rogFHcQeH52CGp9P49aFxPLh/BJm8gSTbd05H2IeJZNZWuecVNxfAunFxETE0lcam3gg0VUG+JRqHsX20+8vc5/vkW87DH121FkBRcHdR7pZ3a8vOszvhg/vPlh32z6TzJRfuloGYUO5Bryo88WJEbX22MHki7LOy8xtZ3Xkt1TIAm6WazIsWBtyW4cfkgr4oIj5NDGGfPzGFqVQe157fI16D3+3HElnkWLtSvg8D8QCeYR4xV4S89NIo8tztrzeeyMIwLbXTEfJaw+7uEB57dRR3PH28MPHKdkPieFQFKzuCwj6azVg30otXWTkJhQAdIZ+VCGfHMJM38B+7TlVsFZvOW60nLl3djq6ITywywRfo4MQCHsyy0UpXxIcL+mJQCLCXKWOuuNM53aEM13eH0R7yIqubYqQUKzpHLlndDk0heProhLBLQiyhC1gXf8irVqyWGZ3LoidqKVlKC6OyJw+P47I17Y7AEvax0RfbV6tGOoCjY0kcn0ji/L4oon4PZtN5mCbFbDovglvxvl+5vhPn90Xxi30jrhPtYmyOwOhcFpQCfpdzmAcuexAqJsqVe7pQw90R9uF16zpw5fouEEIcVmIqZ8CjkpKAGvRqyOqFkuO8YSKnmwh7NWxfEce7dwzgzWy6fzZvIpHNi1GDHd43aWQ2I0pknZaV35oVHvKijY2Y3UohgcINUCFOxW+H3wBPT6dxZiYNneWcEsXBPcSUOzsOHSEfwj5NuBbcluGTtTb2Rplyb4GWvxQUhDiTG/YTzqrzVR22TEY3hZfMCRR57vxOmMmbZdX7bJFyB4ALB2IYmkrjhZNTWNEWRLk5WMXKnVdsAKhLuQOF/jK8bp4HqragFwoB1naF0W1bJenB/SPwqgreuLEwuaqbBffR2ayjFBIA1naFhM+6nvXt4epFL6qW4XSGrSGzNXO4cKO5akMXjo4l8bl794nmS/Ybkp21nWHxvvxGunN1m3h9VSHMT7aO4a9eHcNf3r0be0/P4j92ncJtvz5W8prpnImAR4WiEPzujkHhXUdKlHvhe+0M+xDwqugI+0Rehwf3pE25A1aCmt/Qea1xtChYhHwatq2I46kjE0hmCwkyfi6FfCqzSSoF9wy6Iz5RantqKoXhmTSOjidxxfpOx7YFIaGLyXH9cT+bNg9s6osg4tcwl9GRzFllfuf3Wd9z8fltVbv4Mc1sl+KEKT9ufLKTW8UX99yLqzrs8O9jLqOLJSU7w1587E3rcetNO62/DxeCezqnu44SQj5nx1d7KWHAq+Iff2+7yJVxz93tXBTKfSaL6VQeQa8qLEt+XM7vi6I74hP77lYtAxSO6ZrOUFkBFw1oCHlVnJ5Oi2O5/8wsU+6Fv+kI+zCeyGGCefOdEWveQGfYB6+mIB70IKubYqS9qTcCVSGt0fKXUsCjKI4LyH7ACCElLTst5V45oTqZzGF9dxg7Vsbxxf/a57osVbEtAxSSgruHZvDWLb1l91t47tnCScdPqh0r27C6I4iNPe539WLaglbZ3pytrwxgjWBu+YOd+O9XrkZP1C8C2TPHJnDJmjaHd1dQ7hlkbQlVAI6+IbxtMU8qlVXuYR/G53Ki/UE7y+B/+m2b8L0PXwKgcAMt9hHF+3aHcGIiBd0wMZvOI+r3iCUGeRmYvYSVD+HHE1nc+ZtT+PtfHCwZdaXzhlCTn37rRnzqt85DX8xfYp/Zgzs/Nu22Jm1JodwNx4hvfXdYKDe+4lDxOQIAl61px56haUyn8yJBxs/hkE9jStq5708fmcD//s+9oJRieDqD7ohfJEePj6fw5GFrdPX6dc7gzo9tImtT7rGCx39+bxTRgAezmbxI4v7Ohf14z8WD2M5Kc+3EAl7MpHKupY78uJ1lFTVuwasnYt2YKwV3Mdknk8d0KgeTFhKfnK6ID7MZHZm8wT5X6TlUvBAPP0fsAbzQYsNwiKzi9yKkYMvEXb7TL75zM7767q3i+qtULQNAJO/dIISgLx7AgeFZUQSwf3iWJVQL+9cZ9mIymRU3OX6MuiI+0RIFAF46NS1m7nvUBU6oLhYUVuKCe3RA6QIXxS073ZR7cXCfSFpWwi0f3ImOkA9f+dkBFDOdKv2StwzG4NUUvG1zLz5xzYay+13crMwe4LoiPjz2l2/GBf21BXfuD8/Z/FvOtRf0oDtiNTbjyn0mnUd3xO94DRHc57LI6YajvI0neAFgsC0Ir6aIcrCC516k3Jktw6eF2y9Mfsx4Eng2oyPsMhRe1xlGjq14w2+kERbgRdLZp9oCbWGaNZ95+eD+ERwYnhU19xmbD6woBH9y9QY8/dlrSspGeQsCoDADuS3kEYufJGw35VTOwNquEFZ3BHHF+k7xWnw9Afu5yVnZHoRJgePjSfG984s+7NUcHjjntl8fxQ+eOYGHDoxiIpnD1sEY+mMBeDUFJyaSeOrIONpDXrEKF6dQmWWwJKcmEnYhNuEv6rdsKF7eu6ojiH+4cVvJqIPv50w6X8aWYcqdVfC4lUJqqoI3b+wSPY3c4OfwrK10trPoZsDP2fFEFqkyyVseCJNipFWoE+dwBZ7JWwnV4lEcYNmEHSFr5DadKrVjAStY71hZEE2VqmUAVL2+++MBvGSrTNt/ZrY0oRrywqTAgbNziPg1cbx7o37Eg15xw3vx5BTO64lAUaxJWdUSqtXr9JoApdZi0OWUO2AFd75akW5Y1kWxcg94NEfjsIlEFht7I+gM+3DVeZ345T7n9P2cbtVHF6uyqN+DRz/1JvRG/RUX2vBpClSFODx3t5OqFtpZgzBeceOmPLqj1iQlSilSWcMxtON/4/coGJvLIm+bxAQUlHtb0KoDjwc8Igjwlr/FfTS6wj7oJsVR1r7VHjzbxMQrPqs2j8F4acnoum4rgB8dT2A2nRcJsO9/6FKozM8MMU+V16sDwGQyK3Imf/3T/ZhI5vD139+Gd100WLY8rxi7cueWVVvQKybNCM89bwXMle1BfP/DlwIoVPicmEjC71Ecw3cOH3kcHUsKFVmwZSz/3T6JJZM3hDL/OzZJ6or1nVAUgpXtQRwbT+LVkTlcsrqt5Lzjr5+w2TJcuW/qi0JRCCJ+q6KGV5+4BXX7sUnmDMxm8ljnDTueC3istYK5mCqXML31pkvKvr79/WczupiG3xMtL0jSOQNBX+lxLm4tYrdlOFzoZXUDiawuKnKK6Y1ZHU+TWd1xfhQTrtGWuaCCcgeAgbhfFIlsHYhh//AsVIWUJFSBQgsDzqfeuhEz6bzonX9kLIn3sr77mkIabz/QDCzlrjjupMV38MG2IOYyOmbSeWT4Qh1uyj1fWLdxMplDR8gn/n4imXMM8WdcMuacgXigRMkWQwhxVHo0Ety5r8aHZq7BPWLVzU6nrCn1xaVe9gRVzjAdySCukrnat/cPqaTcAeBVlsjpcAnubhOv7Kzt5HXxSYcFFgt6xPb2EVChh0YWsxnLg+Wqj3v3dlumEvYcTkG5ewv7nHEqd/s5x29kI7NZV0sGsG62gDXM5xcrP5fCPs3qrmmbdPbM0QmRDD40mkB/zI/VzJJZ3RHEntMzOD6RwrYVpTZKxMfLE60upkGPKpQ7Dwj8+uG5KTdlyuGfaXQ2W+KpW22f/WIpvVpupG742fyR2UxezItY3+W8kfAa+bG5LJJZHUFP6TnEj22hT76bci9M1EuUsWUASw2fncmIvjLl4AnZcgnVKzd04gOXr8Rla8qPXIBCOSQAXHt+DxJZK4Y5gzu/lvLY3F+Y27CuK4wdK9sc5yWvoNLUFimFBAVT7oUPXKzKB0RXtxSyTN0Vq6mAVwWlECpwOp0XFyn3Y+3WTnHrgflgr/RIugTcWuHBks/6c8v2cytkKpVDVjcdJwinK+zD8EwGhknhVQvHp5dN8+dKKR7wYjqdw0Qii/1sBZ3imt4uFhAPsuBun1AV8WusNt/eD6d0f9pCXkT8Gk5OpjCb0V3tjULuopDY5Mr5L96yAd/9wA5Hh1CrPK/6qcuPV5gl3gDuuVsVJcWlkAFbYIkFPOB59HIK2F7XzqtZ+LkU9Fkd/+xr8j72yhj8HgXvuXgQAPD69Z0iWb+6IyRU/vbB0uDOlSSvlgp4rVLIi1bGce0FPWw/rW34cap0XnPVmjNMV0+9N+oXE6sC3vmHiShL8h4asbqXFk/Ks1d4ufWxAdyUe6HxFodbGSKh6nP/7NzanE7lKyr3SBVbpjPsw5dv2Fq1Go5XFXlUgg9fuVq0sbDnt+yzUbfYJq5x7PMQ+I1cVVrIc/fYlLvbuqP8JJhM5oRy97sod8A6CaZSeVBauCsO2m4OHN7WtJLCqYZDuWfcs/S1UBrcS1+Hv7a9RUMxXRGfmC3KlSVgqbF3busXrYtjTLl/5WcH8K1HrBVrim+oXWxyyt7TM+gMex0nuqIQxFly0jApkjmj7GfvjwVweNTq/ucWcOxVR/wCPsQam61sD+JtW/pYzsU6NpkytdfF8O/Vqm9cAwAAHHpJREFUnvRrC3lhmBRzGV3MKE7lDCRzzgoGVSHi5lAuSLYFvULZceuCB1i+yg5QKHF8/sQUdq5qx29ttoLxlbaKmFW2afRbBksvcL5voveSV4VHVXDPx67Amzd2s/fmyj3t+L3SsbH2vfRY2s8dN8+9VqJ+y/47NDonJsDZ4dfn2Fy27PoHQrmXVMsUtuXKndeDlxtBr2gPYipl5QDcRuycQrVM1W7lFeHtHnqifkT9Hvz7za/Dh16/Gtdt6xPb2EfE9h5GnJDtmGxkwV1TiKhyK8fSCO6UWglVdjK6Xbg8+E0mc2WVe6HXi16o8ODKnZebseBpmlQk1tyy5rXCZ89RSpHIlR8OVoOX3vEA5vY6XE1w9eam3LsjfiTZosbXnN/teO5vf/dCfJTNGYizhNqR8SQu6Ivi//9vF2FFu9Mz50PmZM7AdRf2l7wXt3bcElx2+uN+HDxrlXG5BcqQbb4A99y5auR+pL1aqtY++SobDdprsflxnkzlRC+gVI7P1HS+Jj/nyt38FYUIm4t/9qhttNBTFNxTOR2xgAdvOq8b33n/Dlx3YeEC5/bMuq6Qa1D2aZbFwW0etyDIA9Jpfg5VEBr278HterN747W0rS7Hhp4wXjg5hUOjCWzodp/e3x7yCs+9FuXOiw7cEqq8EqpccP/trX0gxKoQq2TLiISqSz/3euC17tyeCfs0fOGdmx218XFW7uzTlBLbCijkHzvDPnE9aC1TCgm2fqefe7ClXwwP0lO23tXFyr2wGpMhaka5594Z9iLgUcWs05u+9xw+85M9ABqzZYJs9pwV4N0Dbi3w4eqpSWv/3C5M/hgPFq62DFOpl6xuL6mmscMD8+mpNC4cjOG6C/tL6vmjAU3UyvNlwxz7zJS7fZV5N/riAZF4dVXuvoJyL27ZzL/3wbYAzs5mRBK8VjXZFfGJhc8BW+/8VE4M7+cyeeR0s8Tv5dtWOj+4wuUqcvuKOLYOxLCuKyxsG2638JYQikLw9q19jmQdTzS7+e2ciE+zdU0tPdZ8P49PpByrL7lhFzTlbBnxfAPK/drzezA8k8FcRseGntLABRRq3VMus2UBZ40/4J5Q5cUD43NWcC8nNFa0B0U3yloSquU891rhSe++MgleAKxdt1fMPC2Gf/6NvYXjV4vnvkSqZaw7pJ9l6d1ONu6BTqbyostgsXK33+G5cufDPqtWvjC0f+XsnOjl0JjnrmLc1heiuI63VviJdnIyBb9HcfX6+AnLbZlQGVsGAN5RoT7fej+vqBIZcKlyASBWpuqJ+hxLC3Lagh6cns7YKnzcj2O/rR7bTQXbR1zFKzJ1ioR4AJRa3QxrtWUA4Fvvu8jx/fJePVPJQtkpX+ShOLAI5V5BAffaavUBa9r6f/3plQAKCWp+M7a3hCimPx7Ales78TsuIyRO2F+YoewWBNd0hhD1a5hM5sp+p5yYw5Zxr8ziNBLcrzm/BwoBTApXWwawztkRYcuU7kvBtivktnya8xpRFQKPShzrppbj/ZetxK9eHat4rfLRU7lqmVrxaSp++8I+YZ2V462be8seH/757XNmNEVBwqzcj2lpBHdQ4W1FAx7Xk4l7oFPJnLgY3WaoAlZwn0g4bRnAumufmrRWdrK3ZW3Ec4/4PTgylhTqzN4ZsB54IEnnDUeCxU5xUs1Nue9c1YbtK+L47QpBAnB+5oEyXS8B4Nvv31GSBOPEg17sOzMr7I1yF5T9mLhZDlz1JrOGY56CphTmPgzYlkmrtRQSgKP6ALB34MyVTI4qtWUqe+6AbSKWy3cR9WsIeFTRZtZqCeGuBFWF4F//8LJKHwVhn4ZDI1ai2S24a6qCq87rwk93D1cVLLEqnrvdlqm1hYYb7SEvdq5qx3PHJ8sGr76YHwdfGbMW6XH5XkXJsa0qzU2Z+zQV40luy5T//G+5oAf/8qGdeMOGrrLbDLYF8IlrNzjae8yXf/5vO6pu8xXW7M6N9pAXr1/XgbduLuxLLe0HlkZwp4U7ZNSvlT2Z2kJeTKZyonFUsXLnJ+wkmwBDSCFoAtYX9pvjk0jnDeQME9sGY+iO+stmxGuhL+7Hz/emRWtc+6zBevCoCiI+zbHEXjE8MBaUe+l2G3oi+M+PX1H1/ezD8koqzy17b38Nuy1TbihsH5K6J1QLyt2+IlN7yCusInvOpFxVRS04bRlncC8OcvxGUOnmX7BlSj87H/kMC+VuOiqY6iXs08TydeU+/5s3duOnu4ddq5LsaGwhi0RWr2jLcEXcCB+6YjWiAU1UXxXTHw8Ixe12oxElx/YeTi7XiE9TRGuJSqMtQgiu3lQ5aBNC8Ilrz6u4TbPwqAp++NHLHY/V0n5gSQR3oDDNd21XuGyCsz3odSj3Ys99XZe13NyB4VlMpXKIBzwO33EFq5Xn/VDed+lKvPfSUi+5HgbbgsgbFLvZCj/zDe4AEA95RHdFN3yaAk0hYpjvNuGj5vcK1qbcK9HGunjyGazlbkr2Wl+3CoXCDESrJJHPvuuwBYPemB8KAY6NJ2HS+VdwhLwqPCphrR50YRkALrZMDcG9YMu4709v1I8RptyzFZR7LdiPb7kk5xs3doGQypUynFjAg0TW3efmN61AUa/3+fCOrX14x9a+ss/bxUW5XkwhW1PAciXHfo8qJkv1V7GlWp1a2g8sieBOacGf/Pb7d7guwgpYF9upyVRZ5e73qNZqO2dmMJnMlfjE/ITl64fGg/Pzx+3wEstdJyYR8WvzrpYBrJvXqcl02SBJCHH4ro28F68UUBXiSJ7V9RosUPMKpLITR9gNTyHWtPxiCnaalZjmCwvbPVGPqqAvFhCzS+frAxNCrERwModkTkeHrXFVcV/zRm0ZwPrszx2bBKUUOd2Er4FRov34lguCnWEfPnDZKkcv+HLEAh6cnk67Hssga5/QSBlkrdgDcbnFbeyLlZS3Zaxj61FJ2VHCcqGW9gO1LJDtJ4Q8Rwh5mRCyjxDyRfb4GkLIs4SQQ4SQOwkhXva4j/1+mD2/utp7UFBhjXhUpWwSgzd9Ep67Sw/lLQMxvHBiCntPz5T0veDVIzy4l2vlWw98fdX9Z2ZFXfN84TebSkHbPjSvteOk+3tZn7036p930ohbXqeqlN75PSo6w15E/B7Xdg5eTYFXVazujHlDjCQ6itrJ9sf9on1woz4wb/XQbauBL59QLX+ebOmP4fXrOrC9TJVLb8yapcq/M7e+37ViP76VPv9f37ClphEpPwfKvVYv692/2NhbBZQbAQW9Kp46PI53f/tJnJnOlLTeAArHti8WqNg2ZDmwUO0HsgCuppRuA7AdwNsIIZcD+BqAr1NKNwCYAvARtv1HAExRStcD+DrbriKW5179y+A9z3mdu5uq2NwfxWzGanl6+Vrnmoa8kuRVlpQqlyish4G45QWbFI6Su/nAbzaVMv1O9TZ/5c7tkflaMkAhOAzx8s0K+9MXC1RUwLx5WCqni9EQL2O1vwYfJTQSdNqCXozMZqCb1BHci1/zDRu68Mm3nCf6z7sRC3rww49eXnZBl56ItYAKH201kt+xVyMtRNAVs2nLfG988fLFxp5wL3ejCXo1TKXyeOHkNE5Oplyb1PHFe6pVCi0HFqT9ALXga6p52D8K4GoAd7PHbwdwA/v5evY72PPXkCqmHUVp0yo32kMe5AxT9BoprpYBCglAr6pgx0rnRVlqyzSu3ANeVUyS6ZunvcHhyr140Qs73LIJeEpXmqmHiM+qg3Zr9lUrduVevApTMRcOxkSrYTfiAY81+zhvoj8WQE/Uh019TlutL+4X/ngjdkF72ItjrBmTfS5AcSALeFX82TUbGlLbfAUl3uqikdeK2GqvG7lJcArB3f1Y/tk1G/Cp39rY8PtUI+BVRfK63I2GL1TBCbsodz6SX+5+O1Bb+4GapB8hRAXwPID1AP4ZwBEA05RSXm4wBGCA/TwA4BQAUEp1QsgMrAW0x4te82YANwNAsG9dSS9xN3gw4Y3vi6fLA1bLTkKA7SvjJQEg4tPg0xQxxb/SDLV6GGgLYjyRayiZChSqMyqVcXHlPt/JUhxCCH7/khVV628rIb6PmUxV3/7LN2yp+HxH2CeSYWG/hmc+e03JNgPx6gqvFrYNxvCz3cMAnK0JGhkJlYMHHF5R1JhyL9zYF4JYFVuGL5LeDPrjfkwmc2VvNCvbrcZqV2/qxiMHR10TqvxYNzIabRWsGaoL0H6AUmpQSrcDGARwKYDz3TZj/7tF6ZJbDKX0FkrpTkrpTlVVa7JlePA7M2MtxuxWohX2afiDy1fhDy5fVfIc75pIqeXtNaKi7HAboVHPvSZbRiwG0fgF/tV3bcVbLph/HW93xCdql6vtDyGkYtVFR8grZufy9XOLt3cM3xsIcPZOfo7JOotgQfCkP1+EpSHPXXTRXJib0Pm9UQzEA/NudreQ8Iqqct/Bv3zoEjz2qTfhbZutyXlu4oYf64EKs0GXC5pKFrZahlI6TQh5DMDlAOKEEI2p90EAfB27IQArAAwRQjQAMQCTlV+3NkXDPfLhmQz8WvkSrS9dX14ldkd8GJpKL0ilDIcH90aVey0J1UotGpqNohB87E3r8Mm7XsaRsWT1P6iAtdQYn1rvfoHbb56NBPfN/VFR410poboQcOuQ944vN0O1FsLiu1+Y/bzhogHccNFA9Q2bALdSyn22rogPXREf/MyOdFsBipdGvxZsGW2BqmW6CCFx9nMAwLUADgB4FMB72GY3AbiX/Xwf+x3s+UdopdWOwT33GpQ7C35nZzKufnst8JOiLdS4387ha2DOd3Yqp2DLVAjuLPC7eY7ngt/ZZs2EHWxwKGxv7lXuxuUsmZt/kNRUBZewdVz5+eBV3Vs+NEqxLdOQ585tmSYkOZvN2q4QvKpS0ZIELAF1/5+9QbRNtlNQ7q+B4K4qCzKJqQ/A7cx3VwDcRSn9KSFkP4B/J4R8GcCLAG5j298G4AeEkMOwFPt7q72B1RWyduWeyOrzrs3mCbS2BVTu79zWD0ppxYRhLazrCiMe9Lh2z+Ms9NC8UTyqgmc+ew0abJ7nqGkvp97agh74PQoyebPh+uvL13bg0VfGEA964dWURSv54wFnKSr3pcR7L1mJ163tqGnuxsZe9+vD9xpT7tVa/lY9kpTS3QAucnn8KCz/vfjxDIAba9/N2pV71K+hI+TFRDI37z7LYrGKBQzusYAHH3zd6oZfpzfmx0uf+62K2/ALvJEJTAtNo3YUAMds1HKBmxCC/lgAR8eTDQfj9122EgGvirWdIQS96iIGdyvg8LVUPQvguZeb6NPKeDXFtTldPWzsieAil0KK5YiqEFRxZZZIy98a69wJIfjf110AwLmiUj0IW2YByiDPBcVL0y0XOsLVlTtQ6FPTqDUR9Vs3ZEIIQt7y/YwahfvAwpZZCOX+Gghe8+Gm16/GPR+r3ldpOVCLhbgkJIB9hmo1rt/ejx8+e1IshFAv3Yug3JsJ9+MbLYVcatg7YVYM7iyv4VYGO1+CXnXeOZxqCFtGeO6NzE2oXJcuee1QyxyXpREhaG22DGCp9zv/6PJ5NzNqfeW+cKWQSwm7515JRV+2ph2HRxMLOr086FVL+hQtFD6h3FkpZANdIf0eq/VtLYuDS5Y3tcTLJRHca/XcOY10qVvdGcKqjiAudFmnshUIL6FSyIWELzVm0sqf7cadK3DjzhUL+t7vu3Rlw4sylKM4odpIV0hCCC5d3Y6tFdowS14btExwBxpf8aRWon4PfvWXb27Key0GYd/SS6guBHypsfFErinNquw02va5EiWlkA2e5z+6+fLqG0mWPWoN59GSSKgCja8y/lqhP+7Hhu5wTS1dW42OkA+ElPbpb2UWss5dIuG0lHJvdJXx1wpBr4YHP/nGc70bi0JH2IvAVOOLQywlCCHwaorNc5fnuaRxagnuS+ZMk8pd0hH2LctKEJ+mFCYxSeUuWQBqiZdLRrnXk1CVLE/ee8kK7FjpvuhFK+PT1AXpCimRcNQanI6lE9zlSf+a54r1nbhifee53o0Fx75imFTukoXA01K2jFTukmUKr3UnRJ7nkoWhlklMSya4y+GqZLnCa909qrKsksWSc0ct8XLJRNRGloyTSJYy3JbxSQEjWSBaTLnL4C5ZnvDg3khHSInETmuVQso6d8ky5f+1d/exchVlHMe/P6C8aGlLaQvlJdTCRQEjpSkCYpRAVGgkQKRBNNA0kEYsCqgR0MT6hxj0D4yAVpG3ooRAKAJRhJCKQVDQBtpCLdASkFYK1IhAwZK0PP4xs+122Xt3257eO3vu75Ns9uzs7DnPPXvus7Nzzs7sln9x62vcrSrdXIBSzNHm69ytrja33H2MWzUq6ZaRdKCkhyQtl7RM0kW5fKykByWtyPd75XJJulrSSklLJU3tJlifULW6aiR3t9ytKlV1y2wAvhkRh5Emxp4j6XDgMmBhRPQBC/NjgFOAvnybDczrJlifULW6alwts+sOGlbYhp9uejo6JveIWBMRT+Tlt0iTY+8PnAbMz9XmA6fn5dOAWyJ5DBgjaWKn7fiEqtVV4zr3XX2MW0W6OUe5Vd8TJU0izaf6OLBPRKyB9AEATMjV9gdWNb1sdS5rXddsSYskLeo2WLNetKlbxlfLWEUqvRRS0khgAXBxRLw5UNU2Ze+byjUirouIaRExDXxC1eqr+UdMZlXopqejq6NN0ghSYr81Iu7Kxa82ulvy/Wu5fDXQPFXOAcDLnbbhlrvVlVvuVrWqrpYRcAOwPCKuanrqXmBmXp4J3NNUfm6+auZY4I1G981A3HK3utrc5+7kbtXopjHczaiQxwPnAE9JWpzLvgNcCdwh6TzgJWBGfu4+YDqwEngHmNVNsJ6sw+pqU7eMW+5WkUrGc4+IR2jfjw5wUpv6AczpuOXWQNxyt5ry2DJWtd4afsDJ3Wpq0y9UndytIr01/IC7ZaymNo0t424Zq4hb7mYFcMvdqtZbQ/665W415UshrWpuuZsVYPPYMsX8u1mPk9Sx9V7M0ea5Ja2uPLaM7Qg9k9w9t6TVlbtlbEcY0QvJ3Wnd6sxjy9iO0BMtd7farc52H+GWu1Wv07XuRRxtzu1WZxNH78Gs4yfxqb7xQx2K1Uin85TdjC2zwzm3W53tvJOYe+oRQx2G1Uyn5F5Gy93p3cxsq8w58ZABny8iuTu3m5ltnS8fc9CAzxeR3N3nbmZWrTKS+1AHYGZWM93MxHSjpNckPd1UNlbSg5JW5Pu9crkkXS1ppaSlkqZ2E8Seu4/Y9r/AzMzep5uW+83AyS1llwELI6IPWJgfA5wC9OXbbGBeN0FMHL17N9XMzKxLHZN7RDwM/Kel+DRgfl6eD5zeVH5LJI8BYxqTaJuZ2eDZ1j73fRqTXuf7Cbl8f2BVU73VuczMzAZR1SdU250bjbYVpdmSFklatHbt2orDMDMb3rY1ub/a6G7J96/l8tXAgU31DgBebreCiLguIqZFxLTx4/2zbDOzKm1rcr8XmJmXZwL3NJWfm6+aORZ4o9F9Y2Zmg6fj2DKSbgNOAMZJWg3MBa4E7pB0HvASMCNXvw+YDqwE3gFm7YCYzcysg47JPSLO7uepk9rUDWDO9gZlZmbbp4hfqJqZWbWUGttDHIS0FvjnIG92HPDvQd7m1igxvhJjalVyjCXHBmXHV3JsMHTxHRQRba9IKSK5DwVJiyJi2lDH0Z8S4ysxplYlx1hybFB2fCXHBmXG524ZM7MacnI3M6uh4ZzcrxvqADooMb4SY2pVcowlxwZlx1dybFBgfMO2z93MrM6Gc8vdzKy2nNzNzOooInriRhqQ7CFgObAMuCiXjwUeBFbk+71y+UeAvwLvAt9qWdcY4E7gmby+4/rZ5snAs6ThFC5rKr8wlwUwrsD4bs3lzwBrConpBmAJsDSvZ2Rp+63p+WuAdSXFRpo05wVgcb5NKSw+AVcAz+XXf6+g2P7ctN9eBu4ubN+dBDyR43sEOKSSnFnFSgbjBkwEpublPfNBdDjw48aOIs0I9aO8PAE4Oh9wrW/GfOD8vLwrMKbN9nYGngcm5zpLgMPzc0cBk4AX2ZzcS4pvOumfbSJwP3BBATGNaqp3VdP2i9lv+flpwK9Jyb2Y2EjJ/cyC/ydmAbcAO+XHHy0ltpZ6C4BzC9t3zwGH5eWvAjdXkjOrWMlQ3EgjUX6G9Ek4selgf7al3veb3wxgFKkFpA7rPw54oOnx5cDlLXVeJCf3EuPL5ZcAV5QSE+lDZx5waWn7Lf8DPpS3t66w2G6mJbkXFt/fGKDFWcixtyfwOk0NjRLiy9s8pqn8hwOtq9tbT/a5S5pEaj0/Tv+zQvVnMrAWuEnSk5Kul/TBNvW2eVapUuKTNAI4B7i/hJgk3QS8QvqKe03riwuI8ULg3mgzTHUBsQFckSee/4mk3QqL72DgrDwBzx8k9RUUW8MZpLmf32wuLCC+84H78qi755BG3d1uPZfcJY0kfbW6uPVN6tIuwFRgXkQcBbzN5gm+t9hUm7Losfh+DjwMPFlCTBExC9iP1C951hYvHOL9Jmk/0tDV7T50SnhPLyd9KB5N6he+tLD4dgPWR/oJ/q+AGwuKreFs4LYtXlRGfJcA0yPiAOAmUrflduup5J5boguAWyPirlzc36xQ/VkNrI6Ix/PjO4Gpkg6UtDjfvsJWzCpVYnyS5gLjgW+XEhNARGwEbge+0BRrCfvtKOAQYKWkF4EPSFpZSGxExJpI3iUlgI8XtO8a61iQl38LfKyg2JC0N2mf/b6pbMjjkzQeOLLp9bcDn+iwza50HM+9FJJEuuJieUQ0f7I1ZoW6ki1nhWorIl6RtErShyPiWdKZ6n9ExCrSFQiN7e0C9En6EPAv4IvAl3ohPknnA5/Lr71+qGPK++bgiFiZl08lXVlQzH6LiGXAvk311gF9pJNlJbynEyNiTd5fpwNPl7Lv8tN3AyeSWuyfJp0kLCU2SN/KfhcR63P9Uvbd68BoSYdGxHOkfv/lA22za1V03A/GDfgk6WvMUjZf1jQd2BtYSLp0aSEwNtffl/Rp+Sbw37w8Kj83BViU13U3+XKnNtucTjpInwe+21T+9by+DaTWwfWFxbchl63IMb0ylDGRviE+CjwFPE26VHNUae9rS511JcUG/LFp//0GGFlYfGNIreKnSJcMziwltvzcn4CTC80nZ+T9tiTHObmKnOnhB8zMaqin+tzNzKw7Tu5mZjXk5G5mVkNO7mZmNeTkbmZWQ07uNixJ2ph/YLJM0hJJ35A04P+DpEmS+v2tg1lJnNxtuPpfREyJiCNIPxyZDszt8JpJDPBDNrOS+Dp3G5YkrYuIkU2PJwN/B8YBB5GG/W0MAHVhRPxF0mPAYaRRAOcDV5N+yXgCaWyVn0XELwftjzAbgJO7DUutyT2XvU4anOst4L2IWK80uuFtETFN0gmk4V4/n+vPBiZExA+URml8FJgRES8M6h9j1kbPjC1jNggaI/eNAK6VNAXYCBzaT/3PkgbIOjM/Hk0aj8bJ3Yack7sZm7plNpJGAZwLvAocSTovtb6/lwFfi4gHBiVIs63gE6o27OVhV38BXBupn3I0sCYi3iNNnrBzrvoWaTafhgeAC/LQsUg6VO0najAbdG6523C1h6TFpC6YDaQTqI2hX38OLJA0gzTt3tu5fCmwQdIS0rR3PyVdQfNEHkJ2LWk4XrMh5xOqZmY15G4ZM7MacnI3M6shJ3czsxpycjczqyEndzOzGnJyNzOrISd3M7Ma+j8s1dFNz30aIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bydate=df.groupby('Date').count()['twp'].plot() #to plot the twp column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IwnVKnppLZVnfGSQc8FS1oC6Y5zg47Gx9igxTj8fN9ds7GZtK8ozZOumds+Oc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\nAq5S1d+JyFzgVlw9wd+o6pdD39kfuE9V5wT/jwC+A0wDqoErVPX3Wa4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x130419be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'EMS')"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df[df['Reason']=='EMS'].groupby('Date').count()['zip'].plot()\n",
    "plt.title('EMS')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+xYx7qcjmS8B/jgIOV82xrITxLoRmHHczjpnRjGNvxjEDK2vcdoaynuX6TwF8TxCEp9Xj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\nE/P1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1302eb0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'EMS')"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KmogiJg1F474Kmnu9Gwq393llzJ0TWYbOr9tXIIrVxe2pkMq4txlTypahPPcVTYVsUJahtsR6B28o0ii0WnL7cShWF2e2jPvmkjLubUZOhfSsoufeiCyjdfiGIo2iessoWoEzW2YVB7JElHFvM6aQZYqpkPVyzltJ43nuHIxZsQE3eikyBaG5u/s4FKuLypZR1MSRLcM6N8+d82KhlRszA2RUnruiFbi9t4wy7m3GWOVsmUzDjcPsDUW6IaCq8twVLcCZLbOKA1kiyri3maLmjhU37nnDFH3c63nuhslFLr4bvRQZU2nuihbwvN6sQ1Gf1SxiIq8dqN84zCHLuHAiy6giJkUrcKRCunAuKePeZkRAVW75u0LGk4KpQGPtByxZxv0BVSXLKFqB6i2jqAkZGl1jonfLSnWYS8ueewPZMpSu6foKVeW5K1qA8XzciUnROLIsQ557PYmkVcjGvZ6hM8xinnv3eO6rPBCFq+l6WYYx9kXG2BRj7GnpsQHG2A8ZY8fs//vtxxlj7B8ZY8cZY08xxi5t5+DdAM0JOaC6UnKBLMvk62TLcM6h2S0S3LgEJTjn4pwrz12xHORkCDdKfI147l8GcEPJY/8bwI845zsA/Mj+HQB+Cda+qTsA3Args60ZpnsRsoyj5e/KfHYznjvJMm5vPyAP3c3HoVh9aP54NM2VDk9d4845fwDAXMnDNwK43f75dgA3SY9/hVs8DKCPMba2VYN1I6K3zCpny+Qb2ImJkSzjwolMuD19TdE5iKptjcGNU2mpmvsI53wCAOz/19iPrwdwRnrdmP1YGYyxWxlj+xhj+6anp5c4jM6n2BVy5Y17Kid77vWLmHS7/YCbPd5u2uhbsboYJrez3Nw5l1odUGUVHqt4Vjjnt3HO93LO9w4PD7d4GJ2Dcw9V+7EV1tx9Hg35BmUZzeXtB5TnrmgVJrcTIVy6ml2qcT9Hcov9/5T9+BiAjdLrNgAYX/rw3A/NCcohB1Yu8k6yTNTvgVFPljGLNyA3TmTC7f1AFJ2DtZplrq39WKpxvxPAzfbPNwP4jvT4b9hZM1cAWCT55vmKI89d0xyPtRsKqEYCnoaKmFg3yDLS2N2er69YXcQeBxpzZVqtp94LGGNfB3ANgCHG2BiADwL4OIA7GGO3ADgN4Ffsl98N4FUAjgNIAfitNozZVcgbZGsky6yUcc9Z+krE72mo/UBxIrtwJtuQQfd5NFd6W4rOwTCLzfTcmC1T17hzzt9c5alrK7yWA3jncgfVTZiS577S2+yl8wZ8uga/R6tfxMQ5fJoGXWMr2m++1dD59unaihWLKboTk3NRfKj6uSvKKBYxyXnuK6e5B7waPLpWt4iJZBmNubuIicbu1dmKtXlQdCfFbBl3SpXKuLeZihtkr9BESWQLCPk88DTQxpe22dNd3n6AjtOru7PwRNE5kOfuVs1dGfc2YzpSIVfWuC+kcugP+2zPvfZncrsrpNvbD5C37tXrS1EKRS3Ic2esS3vLKJaHnC1De6iuVMByLpnDQNjbkOdOmQFuTfsi6Mbk81hT283HolhdTJcnGSjj3mbkbJmV1tznU3n0h3zwaKwBzd1qP+B2z70oy6xs8FrRfZim1UzPikOt9miaRxn3NkNRdrm3zErlX88mshgM++DR63vuJMtYwaMVGV5bMHlRcwdUIZNi6Ri82H5Aee6KMiptkL0SUkHeMBHLFCzNXdMa3InJrlB1sUGUA6qAOy9KRWdg5bm7V6pUxr3NCFlGQ1GWWQGDs5DKA4DluWusbs63Y4NsFxtEMu4+5bkrlokzW8Z986huEZNieYgiJrsp10pF3udTOQBAf9jXUGESbZANl+b0EkKW8azsloaK7qOYLeNOqVJ57m3GlAKqgGXkV0Jzn01Yxn0gZKVCNrpBtu7S9qZEqSzj5lWIYnUxTIqVtUfey+QN/OcTY22rflXGvc2Ilr+23q6tkOxBnvtAxJZlGsiWsTR3d+eHq4CqolVwzqHb2TLtMO7fe3oSf3THfhwcj7X8vQFl3NsOTQoKpuorFJyZS8qee/3VgmFa7Qd8HoacG9egNjR0nwqoKpZJMVumPVLlc7MpAMC5WKbl7w0ozb3tyNkyAOyCovZ/Lhn3vhB57vVTIXXG4GugD00nIwKqdhGTavurWCqUZMCAtmyzd2beMu7T8Wzr3xzKc287ppQtY/3P6m551wrmkjlEAx74PFbjsEZ7y3h1DblCFxh3XVWotpp7D53DV352arWHsWIU04Pb47mfmVPGvSHSOQM/PHRutYdRhtxbBli53i3zqRwGwj4A1moh38AeqpoGeD0u99xLsmWU5t463v6VffjAdw4ilsmv9lBWBDlbph3y3th8GgAwnVDGvSZ37j+Ld3xlH8bspU6nILpC2sZ9pSpA55I59IfIuGvgvLYXS+0HfHaTMTf2rwaKx6iyZVpPT8BScX9wsOhEFQwTi+nuNPamWaxPabVxzxsmJhZt496Jnjtj7D2MsYOMsacZY19njAUYY1sYY48wxo4xxv6DMeZr1WBrQSeItOZOwSzJlvGsoCwzSJ67Xr/tAe0XSVq1W4OqSpZpHxes7QEAfHd/cVvk/9h3Bld/4v662VhuxOB2Mz0NLW/5O76QFu/ZccadMbYewLsB7OWc7wGgA3gTgL8B8CnO+Q4A8wBuacVA6zFrG/VO8yJoghD6CgVU55M59IWKsgyAmlWqlOdORrFei+BOxShNhVSee8ugG/6Dx2fEdTa+kMZCKi/26+0m5PYDrZb3zsxZXvtw1I+ZDpVlPACCjDEPgBCACQC/COBb9vO3A7hpmZ/REPO2caey+07BClQWf9faVBBRSjJnIGovoxtpWGaaHIwx0U3RrUHVMllGee4tg+aEYXJMLlrpe6mcZdS70bhzXmyD3WqZkjJlLt3U13meO+f8LIC/g7VB9gSARQCPA1jgnBfsl40BWF/p7xljtzLG9jHG9k1PTy91GII526h3mudu2nd/YqWyUbIFA35bYhGeew1vnNoP+Dw6ALg2qFoaUFXtB1pHtmAKh4GCqhnbqGdy3XeiKc+9HUkQZ+ZS8GgMF2/oQzJnIJkt1P+jJlmOLNMP4EYAWwCsAxAG8EsVXlrxrHDOb+Oc7+Wc7x0eHl7qMATznSrLmE5ZJujVxQXRLjjnyORN+L2WofbolPNd/QI07Go8t3vuZY3DlCzTMnIFE8NRPwBg0Xam0rbnnil0n+dumFaSgcZa7yRMLmYw0hPASE8AANoizSxHlnkFgJOc82nOeR7AfwK4EkCfLdMAwAYA49XeoJXMCVmmswKqdPcngl697RcCaaPNeO6U0+v2gKpqP9A+sgUDwxHLuJPnTnIMGfluwjTb135gIZ1Hf9grbpbtkGaWY9xPA7iCMRZijDEA1wI4BOB+AG+wX3MzgO8sb4iNMbdMz/1Ddx7E5R+7F7/++Ydbqq+ZdpUbEfTpDV8Ii+k83vDZn+LkTLKpz8zkLcMcKPHcaxk6ORUScLEsI+2hCqj2A60kK3vuaTLupv1/9xj38YU0Xv8vD2EqnmnbNnsLqRz6gj5xs+wo4845fwRW4PQJAAfs97oNwPsB/BFj7DiAQQBfaME4a5LOGWJyLTWgeu/hc5iKZ/HQ8VlMtrDXQ2lA1e/RxQVRj6Pn4tj33DwOnF1s6jOz9sqg1HOvZbBpJyYyim6VZYoBVVXE1GpyBRND5LmnLY0404UB1YPjMTxxegHzqXzbsmUW0nn0BiXPvcNkGXDOP8g5P59zvodz/lbOeZZzfoJzfhnnfDvn/Fc45+0JBUvMSVLMUj332UQOF63vBQAcPZdoybiA8lTIoK9xzZ3iCM1q9Nkyz72+oaO0L5JlXOu5qw2y63JwfFHMrUo8fGK2Yt56tmAi7NcR9umS527NzWwXGfdUrhjcpI3taznuM4ksnplsrrNjLJ1Hb8grqsipRXcr6YoKVZqoHo0tybincgWk8wZesnUQAHB0Mt6ysZVmywS9WuPG3b5pNXvhVPfca8symsaE5551qeeu+rnX5y2ffwT/+sCzFZ97bjaJN932MO49POV4vGCYMEwOn66jN+gt19y7yLjLsintoVprHv3Tj47hbV96rOH355xjIZVHX9ALXWMI+3Qk2pAt0xVdIUlv3zQQWpJxp7vmtjURrIn6ceRc64x7pWyZRi+EuSSlmzVnaMs0d6225i428WaQPHd3GkUVUK0N5xwL6TzmqniKJGuWVnqLIL1XQ0/QW/TcSZbpolTIpGTcNY0BZu3YzUwyh5kmPO9UzkDB5OgNegEAkYAH8Tb06+kKz50m4pah8JI0d0pDGor4sGs0iqMtNO7UbZEIeBsPqJLn3qxXVOq567YsU615GNk/TQqoulVzN9qkud99YAJv+/JjNeUMN5AtmOAcVT1Fmmuledc0H/wey7jH0t3suRePXWNWokGtVMhEpoCcYTa8Il+wz11fyDLu0YC3LZ571xn3dN4Qxq1RyHMfDPuxc8Qy7q3SaqnbIhHw6sgWzIbef6mae6nn7q3juYvdolix+Me1mntJP/dWGfefHJvGfc9M4c2fe9ihyboNmktVjbvteMRLnieZzufR0BMo99zbXbuxkqRkWaaBbfboXDbaLZNqBMhzjwY8iGeUca/IfCoHjQEbB0IAmg+qziYtz30w4sOukSgyeVOUBy8XahtKBH2WwW0k150892ZlmTLPvU62TLHnvLtSIc/MpTBVktlExpyOvVUpbPO2RPbMZByPnZpvyXuuJLFMHs9MxsRcWrrnrqMnaBkjzrl4fbca90ayZehcUQZRPRbS1nXdG7SCqRG/BzFl3Csza7e37bcjz4tNSjMzkue+bU0YAHBiurnc8moYvCTP3famGzHYtCJptuipPM+9tkTBJVnGTQHVd37tCXzouwcdj5U1DmvRYcyncljba1UTtqsXSDu54VMP4IZP/0QY40QVY0KeeKlxJ4fB59HQa2vu8hzppiIm+VhM+/qttQAkr7tR3bzUc+8JeJFQmntl5pPWxhR0spr23BM5hH06gj5d9ECn99h/ZgG7P/A9TMWXlvtulnjuAa91yhvRKOdTzv4djVItW6ZahaopBVT9LkqFPDmTxLmY09C2q5/7QiqPHSNRAO4z7tmCgXG70RcZ7Xqee+nzWVlztzViWUqgv/vMfcfw2n96sLUHUIG/+/4R/Oq//awt752SrrfZhKUKNCbLNOZ9L5Zp7s3JMrmCics+ei/uPjBR83VdYdznkjn0h33os417s0HV2WQWg3ZxRk/JDeLZ6QRSOQMTC0s07pyXBVSBxjwd8tyzLcqWqdYVsmjcmWuKmOKZPOKZQtmNvKxCtUWa+3wqh3W9AYR9uuuM+wNHZ8TPNKeqee6ZKrKMrLmTEyU7PGTcnzyziEMTsSWdd845/uSb+/HYqbm6rz1yLo6D4/Vzy2cTWbzty49htkKR0G0PPIt/vv942eNyQHU6ka25WQfnXBj3Rj13CqiKbBl/c8Z9IZ3DVDyLU7O11YWOMO4mt+7Ef/Xfh3BsCZkqc8kcBkI+cSdciuc+GLE89p6A9R6UDUD6W2qJy07DRBVZpvb7FQyzrPNeo1BevPDchSxTRXO3H3ZTEdOE7YmW3siLskzrsmVEXnLIh+GoH9OJLPafWcD9R6aq/s29h87hUAPGZyWQN9cQxj1XqGiAyeko9dxLs2UAYEpaNZEDMrGYhmFyES+iz/y/Dz/naOtRMEx8/icnHE5OMmfgW4+P4YGj9bvExtJ5JLKFusHtp84u4r5npirGST529zP4xPeP4FRJe49UzkDYjo1Nx7NgNTT3TN4Uz8ma+3Q8i9t/eqpiK5PFdB5enSFkf0Y04EU6bzR8zdGNud6m9x1h3FO5Aj5z/3F84cGT+Nqjp5v++/lUDgMRHyJ+K22/2ZzRmUQWg2HLc/d5NAS9xQo8mjxLDRiZdrdFQgRU67zfYjovtPCmNfdCqedeu4hJlmXqee7jC+mOSAccX7A2O1hM5xwXkFU0Vkw/bYUsk8oZyBkm+kJWufh0PINP/vAoPnTnwap/83++8zQ+95MTy/7sVvDU2IL4mTa14dwpPxApIcs4n5NlGfI45TYd5LnTTXdWmiP3PD2B//NfT+P0XDFJ4dFTc/jIXSDB1y4AACAASURBVIdx7+Hiln1ktBpxpMjTnYnXnou0AqEt7WSoffE//OiY4/FUzsCWYSv2NpPIQq9RoRrPFm2NbHe+/uhpfPDOgxWr3RdSVusBZs9RGke1tr8Hxxcdczxpfzf1bgYdYdzlE9doxJkwTY75VB4DIZ8wZs0GA2eTOQxFirsByhV4y92MoCxbhmSZOu8nez7NBqvIiyp67rXTAuVsGdHy1+DIGyaS2YJjYt36f/fh4/c809R42sG4LZPlDe44l9TugTKEWiHL0HfRL4x7FidnkjXlv0Sm0JbClKWQyBZETxhZnqgkzVQLqDqyZWxjRBt20N+lc4ZYGcxI0hW9pxwfGbN3IpKz0hJZ5zVXC7o+pxO15VI6DnIGZOha/M6TZx0rgHTOwLreIADgVRettSpUq8wj+RzKqZB0Q90v3VjF6+y+MkQkQE5p+ffx+HNzePU/PognThdXHrSqqldo2BnGXWr53uwFEcvkYZgc/WGfMGbNGHfT5JhPFmUZAOgJeiTP3dnS9O4DE7j8Y/c2nOtsci7u0EDjmjtVp/o9WvMVqgUDPl1z7NsK1EqFtP5n9k7vPt1qkfCyv7kPF37w+/ijO/aL104uZkXq6Goie2KykaV2D2TcW+G50/v3hawufhOLGYzNpxDL5CvePDjnSOYKLStM+fHRaVz51z+qOeeeGlvApX/1w4p9weOZAkZ6LOMuV54msuXXWnXNXcqWseXPc7bnHvZZVdfydyI3wqL3lD19Muq03RyNE3Bq3tUg2bRe/IPec3yx/CaQyBYwGPbB5M4bTypfQNjvwf4PXIe/+eWL7WyZKsZdOk/0WZxz7B+zmv09VcG4L6RzDuPeU7IBiszhCUumnpDGT99NvX1rO8O42+ct6vc0XAhA0GQdCHvh0TXoGmuqiCmWyaNgciHLALbnnqYloj3h7Al614EJnItlcaTB/jNmSeOwQJOe+7q+YNOyTDZvihsdUD8VUpZlAEuvnklkxYSXjzWeyTd9s2kH41KAW46xULsHkmVa4bmTce+3NfdUzoDJrXlbydvKFkyYvLh8Xi7HzsUxvphxeMqlHJ6IYS6Zw9i8ZSwPjcfw3m/uRyZvIFswscbuPijLJZXGXi1bJleSLQMUjXV/2IdM3nB8J3I5Ps0XuSbhjC3RjEmeO50vcqhyBRPv/vrP8ey0U9owzWIQs55xp/ecKPHcTZMjlTOw1ZZfSlchQZ+O3pAXXl2r2c/d4bnb83AylhHjemqsvKPrYjov9jcGLM2d3iuTN/DOrz2B41PWNUfxANmBSdo2qda2mUCHGHdiIOJrulKLjOCAbZwDHq2p7BIyDD2OO2mxAi+VLXrupsnx8LOzANBwi4JqRUz1xki69treQPPZMgVD7MIESEVM9mTgnOOOfWcQy+QxNp8SKVU0Tp9Hc+jqpCvmCiayBbPijWkxlcftPz21YsUs4wtpcTOSJ768NRrQmoBqqSwjUyl4T55Vq7ZOo3M6X0MGonHQZ/7P0Sl88/ExkVGxJmrl6Muee6WbD60oswXTsdKTs2VolUsaen/INu6S5z5TyXNflD13W5aZK5dlaH6dnkvizv3juKck5S+ZK4jVZl3jniNZJlPx8W3DEQDOzJ9UzkBIvn5Y9Tx3quTVNSZs1/4zlkG/bMsADk/EypxNahpGFGOFBTw7ncBdT03giw+dAgCxl4M8z+hzXKK5W2euP+RbguduvX7AvhP6m9zpiCZ4xF/8Mqtp7ken4sLzOTJZuS1wLJN3eCjZgikyUIDGNXdqYzzaG1hSy1+H507tB+zJcHouhfd96ync/dQEvvrIaXz4u4cAFIOQXl0Te9IOR/3COyFvqdJ47j18Dh+88yDe8ZV9K1LQMrGYxuYhy+taTBcNFm2OoouA6vI/i3b3omwZx3Pp8oBeqkrGyVIp7lVQPXhIN7jSG8tZ24iuaVCWkeclvcez0wmH5u736BiK+HB61jbuYZ8ly9gGdCDsc2ru9nuekx4jo352IS1uwPGSgCrljR8pCUrKDiDJP3PJXFmzM+sYrddOxTMOGYOue/LcSWKiqlvKZAFQU3OnczTaE0Ask8fxqTh+cHASHo3hTS/eiLzBy1b5C6m8kLaAYkA1ns2Lm9U9ByaQN8yKxr0oy7jAc6chDoab99znbP23P2ydLH+TnjvJLiFfsUGm3PWOsgfSeQM/PW557aM9gaqe+8fuOozf+OKj4vfFkuBJo0VM88kcgl5deEXNkCkY4nOAoixDyzi6QVmFKMVJQwsM2XNf2xsQpebxkjavMnQz/MmxGXzz8TNNjbdZOOcYX8zggrU9AEpkGdqx3j781gRUi3nJw5GA47lKQVXyCltm3O2Oiw157vTZpDUvkHEv36uz0rUmz7VEtoDHn5vDtX//Yzx5xtKOyVFZ1xcU82kg5EU6Z2B8IY2hiB9rewMlnrs1/nO2557JG5iKZ7G2N4C8wYVhpfMljLt9TKUtuGUHkIzh7331cfzxHU+WHQ+dB5M7by50YxvpCSDk0zG5mBVj5RwISvaAYleV0hppzGt7AzgXy+LV//gg/vPnZ7FnfS+usFuIy+2TM3nDEeAGnLIMHc98Ko8Hjk6L1ZF8Y09mV8BzZ4z1Mca+xRh7hjF2mDH2EsbYAGPsh4yxY/b//fXeh85Zv23cm9nmjjx30sypMVejUHvPsOS59wStCjzT5EhlKchj4JGTs9g0EMJVO4aqtgU+MZPEiemkMCrUlJ8IeBoPqA6EfQh4NZHa2CiW5148HlGhao+JJkoiW3Aszclz9+makCJGewIomNaG22QMKt086TmfrgmPrl3MJXPIFUzsto27Q5Yx0ZKA6v97fAxXf+J+ZAsG5lM5RPwe+Dya8NxrVUPL2nErbi4Nee5ClnE2/hoj426PWzbolW4+cqZKMmvg1Iz1XZ6wdW9aEVIrBsDW3AsmxhfTWNcXwFDE79TcC+S5W0ac4gJXbhsCUPTiEyUBVRqrvHKQH/fqDNPxLBLZAh47NV9WrWwdQ/EY5YwZSvWMBjwY7QngXDyD3/rSo/i7HxwBgBLP3ZpLV/3t/fhxSQ4+jWVdXxCn51LIFkz8+asuwBdu3ot1fUFcf+EIvvTgSfHdkWM1GJY1dwqoFsRKJOL34B/vOy6uWXme0djzbdbc/wHA9zjn5wO4BMBhAP8bwI845zsA/Mj+vSay527YgY5GmU/lEPBqQsv2e7SmAqpkvB2ee8BjBcuyBTGWTN7AdDyLTQMh7BqNYjqerbgMPBfLIGeYmElmRfGL7LlrGrMyYOqMcSGVQ3/Yi4BHh2HypoqKsqWeO1Wo2u9BTbBSOcNxgZNB9Hk0MWnpIo5n8+KxSiuJeCaPoFfHur6Aw0NqB/LYPBrD2YU03v+tp3BoPIZYJi82NQaWprmncwY+/r1n8NxsCuMLGbuAyfoOSW9+wcY+AJZRNU2OT/3wKL72iFWjIWe1JFvQQTIrjHsNz71Elil67pZBleWk/pDX8RqZdN5A1NaAE9misZmMZaCxoqOw1k4V1Jh1o8sVTJydT2NtLxl3ucCpqLlzzkWmzJXbLM+W9Pcyz9320Asmd1Rjkkd/3mAY0/EsHjs5B8PkFc81ZcRY56Jo3Ok8hX0erOnx49mpBP7n6LQoTAv6ymNWY/PpsgKrRLYAn645su1ufME6UfH+nlfuRCJXwBcePAmgmIo6KHnufo8Gr25p9tPxLCJ+D3798k3YL62W5O+eVh1ty5ZhjPUAeDnsPVI55znO+QKAGwHcbr/sdgA31Xsv8tRpy6lmdPfZRE7o7UDzqYPCc5eMOxnjWDrvyJaJZwqI+D2ix0ipNMN5cYk5sZBB0m7KLwdPAHurvXqee8pqhhZosKJVptRz9+rWDYU0zHnJc5cNERPZMsVpMWpfxHEpb7uSLJPIFhAJeDDSExDnoF3QxR/y6egLefHd/eP4j31n8Muf/SnuemoCV+8chk/XwFjxvHHO8Z0nzzYklfz7w8+J5fHEQhrz9ncBWOfmnb+wDW+/agsAa478xXeexj/86Bhu/+kpAM5AZSsyZtIioFrdcy8NqNJxkkHrCXhFvKcn6IXfo1U8F5mcgSH7RpDIFmWC6XgWPo8m0nrX91nzIujVxfuemk3ivMEwhqI+zCaKxWV0PWYLJmLpAsZsT/3yrQNgrOi5x6VVMuBcZTgztigYGsZ0IouHjs/Yx15+rpO5AratsYKmcjohHXvYb83ZZybj4Bxi1Sl77lI+RNk1n8hY854yiEZ7AkICA4DzR3twyYY+PP6clacu2otLNwPGGCJ+DxK25j4c9eO3r94mqmQvWt9borlTEVP7PPetAKYBfIkx9nPG2OcZY2EAI5zzCQCw/19T6Y8ZY7cyxvYxxvYlEtZdmbo6NqO7z6dy4u8AK+DTlOdOmnuJLANYF4zcfiCeKSAa8OCCtZZxpzsrEUsXxESeWEyXNQgiAp76uzHNJ8m4N95ojCjV3BljDqNbzAQqOCoRiwHV4mwe7S0u5+WAaql0FrPPTTPG3TR5xeKSeqTz1jiCPg96gl7Mp/Lw6Ro29Afxy5duwMf+10XQNObo2fHMZBx/8I0n6zZbAqx011H7Ah1fdHruAPDe68/HVTuGEfTqODwRw9ceOY3eoFfIB/INsxW6eyObv1NgN1kSzKXzGw14hMEKeHSrWVWFsaXzhijoS0rG3eRwOAxr+6zzE7Qb7tFrdo1EMRzxI2eYxU20pbl7Lp7Bc7MpBLwa1vUGsbYnIHRlujHR8cbSeVGQJhtVcv62DkeQNzjueXrS8fcyyayBkZ4ABsM+nJQ6vdJrI36P+K6BonQpG3c52600OJrIWg4fSSsXbegtG8NoT0CcR7ExUNgZmI8GvMJzH474MRD24Q9fsRMXruvBlqFwiSxDqZDt09w9AC4F8FnO+QsBJNGABENwzm/jnO/lnO8Nha2INS2fYhV0zGrM2R0hCb9Xa05zz9bz3IuyTCJbQDTgxZpoANuGw/jZiVnHe52T0qms5XzO8X5E0KcjXWd1QcdFKY3NBIlLPXcAGOnxi1S0ouduCFkKkDR3KdNmxJ74VsVlMTiVK1kSWjc+L0Z7LePeSNzk+wcncfUn7m+646bDc7fP7QVro/jBe16Ov3/jJaIiNyoZd7ooa+nWxPhCGpdvHRA/zySyjjlG9Aa9+Plp6wZ/w4WjQj5IOnTrFhj3XAOeexVZhm60Eb9HODABn255ilVkmeEKnjvgnBckywS8ulhdAsCu0agIFpKkkykYwvhNLmZwajaJzYNhaBrD5qGwyAgRPVNMjlzB6q3UE/Bg82Cooud+4Tor5nJ2IY1owIN03iiT4azVto4963sd1aKy5y572kTQKwVUJeM+Fc8604Tt1Tw5hJdUMO7Ujwgoau5DUed8os6Q04msOP/vePlW3PXuq9AnJXgAK5MtMwZgjHP+iP37t2AZ+3OMsbUAYP9fvbuSjUiFXIIsU2bcPXpThjCdK4AxODxdWmItSrJMIlsQ0gNgBYMePTnn0MJlj3V8oei5U1N+IuDVa8osVtOwQpksc3wqgWs+cX/ZBhXEt38+hhs/8yDSeafnDlhGekqKxAPWqkU2PqTGkCwT8GpCjrC6MBa/l1LpK5HJI+r3YE3Uj0zebKiNxMnZJPIGd+Q61+KTPziCP/v2AWHcg15d3Dgv2tDrqAQGaPsya8wUAK/XVC5XMDGdyFryQsSHZ6cTGJtPi3xomb6QF2dtz/iGPaPW50zGHTfMVhj3ennuhsmF5EY3FvLKTW7JCiGfjpBtsIJeDZGAp2xVYdqB82HbOCclzR2AI71WlmVojjIGbF8TEcadvNRM3sTmwWKx0ImZJLbYaaxbhsI4MZ2wsrGk8aTtlXJP0Fu29WUsnYfPo+GX9qzFHb/9Enzt7Zfjd67eBgBlVbzJbAFhnwcXb+jFsalEWWM0CqjSsRCy5i43/gOc0kwim0ck4BFOxsUb+lDKcNSPhVQe2YKB2UQWQa/uiPHROBZSOczEs2Xptn0hL1K54g5zNPZSB6uUJRt3zvkkgDOMsV32Q9cCOATgTgA324/dDOA7jbwfBWaAxmUZzrnV7jfk9NybynPPGQj7PA7DQNkt04msKF4graxHGPdBpHKGowKNPGO/R8PEYkZ4U6WyTNCr1TTulPkwEPYiYF9QmbyJh47P4NRsCs9W2UjkSw+dwv6xRUwspit47gER0CLvNZl1lsjTOaCLWF5uxjMFx8VXOv64JMsAzlVMNWiT5kpZDpW478gUHjkxKy5QS3O3vvtKF5XcJ5u6jda76VirDmB9XwDr+oJ48Jil5+604ywy5K0FvTpesm0QusZwZDLu8NwrSR/NQjfSaqsO+aZb6rkDQMSe3+S5B722514yNrpuhmTjXsVzH4764dGYJcvYRnHzYBgBry680qJxtypBg14dB84u4vRsymHcY5kC5lN5x5hT+QJi6TyiAQ92jkTx3FxKfO+xTAE9AS90jeGyLQO4cvuQsAG0Ev/yQyfxg4OTSOcNRAIeXLyhD4bJcWhiURybbic3UGuGa3YNi88vzXMHgPMGrZ3enMa9gKjfg5fvHMZHbtqDl24fKvt+yFjPJnKODrQy54/24OB4DLFMocy4l2ZmCVmmzUVM7wLwVcbYUwBeAOBjAD4O4JWMsWMAXmn/XhPOreZWpe126/GDQ+eQyBZwycbiUmgpee7yFwkUDbgcgCHpgIzdFVsHwRjws2dnpNdYk/mi9b0YX0yX9W0m6m2STcu+vpDPsS0feZ+VvMFTM0lxo8kbvMxzH+0JWEHhbEFkyySkbCDAWcQEWEvWqN++4ZZszlDJuEf8Hoza2TUkzdy5f7zsWB88NoOpWEYsURvV6M/MpZHMGpIs4xHn9pIKxj0iGfdGPXfSqNf2BrG2NyDGuGu03LiTt3beYAgBr44tQ2EcObd0z33fqTlHOT5RL6DqKE3PFmCYzmZqtNoUmrtXR8TvLZNl6HvqCXrh0zXMJnOO8yU7DLpmxXECUkB154i1uhGee7zouYf9HuxZ34PvHZxEweSiAI2KiE7OJJGwDS5QjHH1BLzYNRIF58DxKSsdk+QaGUplTuas4//b7x/Bv/74Wev4/ZbnDhSrR5NZq60vYwy71/Xg1RevxS0v2yLeT/bi6bp40aZ+RAMePCNJRMmsgbDfg4BXx1uuOM/RaoSgldB0PIuZZM6RKUNcuW1QyMnDJc/3hpxytZBl2pkKyTl/0tbNL+ac38Q5n+ecz3LOr+Wc77D/r9t5nwPwasyR7yljmrysIZJpcnzyB0exdSiM1168TjzedJ67/eXIRPweaKzoiWus6D1RwUF/2IctQ2HHhgHnYhn0Br3YMhR2yDLlnrvuyF0uTXMs9stxyjJUzFEp5euukkCh3H4AKFYoTsWKsYCFVN4xQchgk4cW9nmkjnX5EuNeIsvY8YgRu8x9cjGDp8YW8e6v/xyf/tFR8brFdB43f+lRfO4nJ8R3OtmAcY9l8lhM55GUMnyCPh271/Vg63AY29eUyya0q3wyWxANqupJfnRDX9cXELqy36Nhk70/rwx9r2SgdtmbqydzhvD2GjXunHPccvs+fOa+42XPFfcpNSuu+MiJ0DVWsWEZlbeTFBD0Wvuglq4E6HOCXh2RgEdkjni08lgMAFy+ZQC71/Yg6NPE8QNWpbnGiv1lMnkDAY+Oizf0iZXAVtu4k1xDxp2CuemcgVjG9tztGyvdoOOZAqIlDhPFzJLZgthgh4wwZcSM9Pjx02dncGYuJZwROi///GuX4oWb+sWxyg4fXY/r+4O4bPMAvvn4mEiZnEvmypy3UsgTn45nMZvIYqhC/ObyrYNizlTz3ItVyO3PlmkZnHN4dA0Brw6fRyu7AH/n3x/H3o/c6yhkeOTkHI6ci+Nd124XATRgCXnuFTx3xhh6g15Rui3LPhG/nA/vdSzBJxczGOnxY21fEFPxLGbiWXh15vACACuglclbBS6v+OQDuO0BZ9/vYi8Tnyh6yuRNyXOvXP5PRT0AhJxDkK44uZgV2u1sSZ4+XXjkuUf8HugaQ9inW9ky0vfiaLNrN3KKBjzFm0i82Fzt9p+eEiufR07MwjA5xubTQuqaakCWEYUuuYIjoPrGvRtx3x9fU9FjsmSZPI7ZHp/GGvDcF4ue+zo7I2THSKTi+9NFRwZq+5oITs+lMJ/MCe+1tC96NchLnqpQI5DJFWMolbx3OqbRngBS2WLtAimNZZ67T8eaaMCSHSvc3AM+K5ZxaMJyXEhC8ZfMqU/+6gvwodddKPo6XWSvnnSNYSDsF91DM3YM6GIp2Eie+8aBEHSN4eRMAolsQfTAkT338wZC8Hk0IYfE0pU892JuPmWx0Tyh5150Xj/uPTyFl3/ifhybipc5dbQasc5V8TmKq6zrC+LvfuUS7FgTwTu/+gRmE1kspvPY0B9ELYRxT2SryjK9QS/2rO91vJ7ok2SZXMEUWrs7ukKimH7XU7Kf4FQ8gx8cshr6yx4JXewv2jTgeK9mA6rW8sxT9vi6viCO2p3Z5C8jKk2qkE93tCc9F89ipCeA9X0BcG55Gr1BX1mgL+jVkcmbmIxlMJMo7zBJxpcqVAFLdin23rD+/9ojp3HDpx9AKlfA2HwaF63vFZWIpZ47TdrTcymk84bDWNFNgTrfFWUZ6z0iAY/IlqG/Ky1Tp3MT8Fq55+diGRw5F4dP15A3OL744CkAwE/txmvjixlx8dfqdkiQ58255S15debIx68EZcuQUTh/tKeu5DexYK2+wn4P1tlBw0p6OwCh92+RDBXnVkXlQNjyXhv13CljpNJ2cJlCsb/4QiqPew5M4C2ff0QkIiyIDqIBK/BvzxOaC5U899EeP/JGccekv777MD5obz4S9Op42fYhsYqh4y817sSWoTDufvdVeMUFxaznoYgP0/EcCoaJgskR8OoiLhINeERmnFe3VkVHJq00UhpzKkeau9XtdftwRFwnlixT4rnbczWVNco6MVLfqL+8cQ8+8Jrd4Bx4+uximXGnc1iaYEFsHgyjP+zDLS/bglTOwEP2XN5YYVUnQ/ZjKpZ1bOlZykvsoq5KAVUA+MT3j+DtX9knHndFV0jOi1WUPQGv4wL81/8perXyhUI6LXmKhN+jIWeYDZd9p3IFR447sbE/JJZBcjvgqDSpQj6Pw4ueimUw0hPA9jXWxfD4c/NlkgxgXTypXEFc0KW7xMwJzd0rZJknS9K4vvX4GP7s2wfwzGQcx84lMJPIYqTHLyZa6YVIxp0MnZzb+7vXbMP/ec1uvPUl5zn+NhKgDXy9okKVLsp0FeMOACNRK3h79Fwc56+NYseaiNBLf0bGfaHouVcLvj52ag5ffsiq7JO16Ol4tmw1VIlowINswRTywu51PVisE1AdX0iLqlySZXZVMe4UUCVZZp39d6dmk4j4PQhXCFpWg3KwZSnjY3cfxv4zC8gbXMQy5lM5PHZqHg8en8Fz9nHR9bKuL2gHyYu/03kAZM1dE/OBJLHv7h/HQ3bvpKBXx2svKUqdO2wtvZpxB6xzKzsxw1GrSrW4K5iGzYMh9AQ82DoUdrx2y1AYT5+1DDJdz4lsAcmcgZ6gNfZdo1EcmoghlStgIZUXjxNkqJO5Ap46uwifdOOP2HGjoYgfv/rijQCsLKJooNy4r+0NIujVHeP781ddgL+6aQ+usNNj6Wb+E7tadWN/bePu91gOz4mZBPIGd7QekLnlZVvwkZv2iO+GoBXiM5NxR4VsvX2OO8O4g4vmVqU7gf9UClgmHV5yxmH8CPq9XppQ8T0re+4bB4pLrVqeO3nRnHOx5LpwXQ88GkMqZ1TU44ajfiyk88LDKG1HOp/MIeSzUsz8tgdBTf/JG/zOk2fFBH7q7CI4B0Z6A9jYX8w/lgn6rB10npm0ltrrpaVkT9CLW162RQTMaBVFHg8VAyWyxUh+VjLulK1BN75do1Hse24ehyfi2DkSxUDYh/lUDtPxLI6ci6Mn4MF0PIuCyeHVWVVZ5muPnMZH7z6MvGE60iWn49myVLJKkMd6YsbypAcj9buOji9mRJrfBWujeMUFI3jl7pGKr71y2yCuv3AEu9day2kypnmDI+T3VMxIqcZJu7x+NplFJm/gltsfw20PnMC3Hh8DAGHcF1J5Ybwpb5uckHV9QaTyhsgIovHQeaCKx6BXF7ndU7EsYpm8YzOLoE/D3vP6hQOww3ZWSjX3WlALAiH12AbzHVdtxRttA0vsWd8rbjLD0eK4gOKcuv7CEUzHs3jZ39yPuWRONOUi6BpeSOVxeDyGl+8sZr7IfaPCfo+4titd96+9ZB3e9OJNjsf6wz689YrzhMEn4/7AMdu4D9SWZQArSPqMvfHGUBXPfU00gLdccV7Z49GAFyWLf/QEPG0tYmoZnBelgB6p3S5gabd08pyee9bhfRJiN6YGpZlUtlxzB5xLrUpNfgBr0sgtgXOGib6gFQSlpWxp6wHAysnmHPjvp6yNiydjGUfxxZxU7k5G+sxcGqM9AQyEfUjmDMQyBRFE/Lm9BddINIAN/ZU9dwCizBqAQyeMlKxc5IAqHXM8U0AskxfGXQ6o0s2YjMirLlqLuWQOM4ksdpFxT+aEd3b9haPib3esiYoaglKm41nkDY5TM0nRfwSwtMtK31kpZBhOTCcxHPGjJ2D1QKmVhjqxmBbVlyGfB5+/eS+2VshxB6xe4P/21r0io2lUaqYVtguFSmUZznnFGwx57pm8if9+akJ40SLAa68i5lM5sVok52AhnRcFXZwXYycbhHG3Uzbt7zPg1R1ZTaVdF4NeDzSN4c2XbcLOkYj4zkvTa2sxFPE5jbv9t++6dgd+/XKnAXvNxWvFz/RZtDInbf2GPWvx16+/CMlsAR987W7c+IL1jvcgA35wfBE5w8R10g05UiK/0Eqskizzyt0j+MBrd9c8tr6QDwNhH87Fsoj6PXUDqnRcx2yZt5GbgYyuMWwfjuAdV20RK9a+q/4ZogAAH9NJREFUkM8dLX+BYkRelmWyBWtPxq1D1sUlSyDnYpmKlWXk6TYaVE3myrNlAOdSizQyXXMGR4NejzDuCyU57ZSeWemLp7Q9yrQxTO6o0pyXCrPkz7ti6wDCtsGIpfPYMhyGV2d40q6SHO0NiIlT6rkDlkGncdKFD6DMC5ZTIQH7O8lYu81TmpZDlsk4ZZlrdg2LC2rnqGXc51I5cYwvOq/YKHS3XWVYKR2SjNSRc3GcmUuJC30qlnEUmVSDxnNyJonhqN9ReVyJZNZa8q/ra+7iIwJeXTgCIV9lWeb7B8/hso/eW1aVSxIdANGHZF1vQEh2lIst1xscsI07tZUO2eeczqXw3CukQtL3OGnHRmTo3L772u343h+8XDQa89WJccgMRaxiNpIY/RU0bGLnSFQY3DWlxl26ft582SY8/eHr8Vsv3VL2HjSHqQZk81BYnLPS65scr1Knphk22znvGwZCZTG1SgxH/TA58MJNfbh0U91GuWV87w9fjj971QV48RZLGuoLed2yWUdxE+e+kFcYIFqakaZZqrmP9pQvb+TskkaolC0DOO+uZGgjfmexk+W5Wy2KF0ty2i9abxnw3gqa+0DYJzxnMoKyNDOfyotqXTloeOW2IaHzx9LWbi5re4M4YRuGNZLmXsm4v/f688WxbJBuXqWejU8qYqL/p2JZcA7Jcy8a91iJLBPw6rjuQstz2jkSQX/Ih8V0XnihcsHRhbWMux1cPDQew5n5FM4ftV6bzBkNee5k1LIFE8NRv6NnUCUoq2Z7FU+9Ecighv2VPffDEzFk8iaeeK4YQzHttgU77JXYk2cW0B/yYn1/UJyz/rDP3u0nL7KWnh5fhGHvATwQ9gljRTEMkpeifqdxD9pZaUMRy/s8OhlH2KeL/vjkUDDGoGlMBI5rGehSyCGi9r6V5qPMay+xvHcy7iTTlOri1YLo5HhRa+KRHr9w0Mo899HqnnujbLEdzo11MmUIupm+97pdDd0MStE1a39j6qTZF/K5JaDKhc5L+qwpebO0LCYvyDA5pu3MlFJkz52CUtX6i+cKJvIGr/gly8aPvLHSiRb06TC5ZTyE5y7KkHvt3ysHT8h7pyDNz0/P4+9/cMS6WFM54S3JvGTbICJ+HclsQXhrFPzTNYbBsB8v3jyA916/Cy/dPlj297vX9eCO374C77thF7atCYvHSw0leWhkHKNSqToZ97RDc3d67gDwh9fuxAdes1tISZxbxrMn4MHmoeK5pUwdMu55w8Qnvv8MZhPFlsp37BtDJm/iaqmCMNiA5i5nVDg89yq6O8kTlQqWGoW+D8tz18vSVskTP3C2aNwnYhlkCyb2brY8uiOTMWwcCKEv5BPnIOi1YiaxdEEU+6RyVkuKWdu4k4xGFb8kL4WFcbezZezve000gCnbc985GsUL7NVmabCaVqO1AqqlUL46BcLrBcDfesVm/PErd+Ki9b3wezRxDKVZMbUI+3WRaTbSE8DGgRA8dhWqjPDcKwRUG4UcznqZMsSbLtuID7/uQlxZoYK1Gd704o147/W7cMFo1B2yDEdRlukPWbuRL6bzYneUbXYAI5UzcHgihoPjizA5KssytueeLZg4cHYRtz1wAm/8t585lr1EcRem8okX8OoYjvrh1Znw+KIlE432WUzljKLnHioGFa/bPYIrKxhZoNg9jjYs+PS9x/BP9x3Hs9OJspYKxMaBEMJ+D2YSVjCyJ+gVnuKaqB+6xuyWtNurBhy3r4ni967Z7rihld7cSmUZOm6vzvDizdbNSF4ZlWbLAMCmwRDe9rItYIyJ1cLRyTiGo35HZSnJMrRyOXB2Ef98/7OiNzpglbFH/B5HsVqowWwZwtLcrd+ree5HzsUR8Gp1sx9qITx3n46w34PpRBZ3H5gQ2Vt0nE+NLeLouThOzSRFy9qrd1qphCa3ZEH5Bh/06nbnQKtMn2SME9MJ0ZI4LMkyYZ+OrUMRXL1zGJdtsW4acvsBwG4mF8vgyGQcu0aiuOkF63Hd7pGK3vIvX7pBzNVGGGrSc+8NefGua3fAo2sI+z2S5t6McbfG3Ru0Ei1+ac8oXn/p+jJPefuaCF65ewSXb6l8bTaCSH9t0HPfviaKm6/cvOTPI/pCPrzzF7aLrMBaLP3W1UJkWYYyU+ZSOfEFy5777/z740ISqBlQLRhCs52MZXDbAyfw16+/yPHaSr3cZTb2B5ErmGJiRkuMIGmclift7ADp1TXc9ht7qx7zy7YPwaszXLVjCGGfLgzk5GIG8UzB0QzN57GyF2istFTvDXpFoU2lG10t6Jj1Cp5NUZaxjtsqr9fw2be8CHvW91q7Q5Vky5TGI2ToWE7OJIV3uq4vCI1ZN46hiE9kw1DO9qOnrMLm3Wt7cGgihut2jziylhqSZaTvy6m5V85gOXrOyu4pbRTVDPR9hPwebBoIYS6Zw+999Qnc+fsvxcUb+kSR1P4zC3jzbQ8jGvBgfX8QGweCjt4mpYUxAZ9VVRqzNfcXntePQxMxzNgrnIGw07hHAh4EfTpuf9tl4j3OGwjBqzMhOY72BvDg8RnkDY4L1/fi8q2DuHxrZYP392+8pKnzQCs8+l4r5Y1XI+jVMZfMQWPAQIWCn2rQnCa7cN2Fo7hOCt4TXl3D52pcm41w0fpe+HQNF28sb3uxEngaiH90hnFHUZYhj3UuaRl3n2419rFSCwuYimWFJDBSSXP3FjV3Mu6DdrZGKWIXpiqBla3DEczZaYlAuSxDj6fzhhRQbWwy7lnfi6c/fD38Hh3r+oJC733W1gzlHvWHPny98D7C/uKNoCfgFbnYI9HK6VXVIENA/TVkhOduXyw3vXA9btgzKs5taVdLKuWupiXSd1owuUh1O09azm7oD4ndeajvDQUVX75zGIcmYnjtC9Yh5NPBGOw9LhvPlgHQkOZ+ZDLuSKFbCvR9hH063vbSzdg1EsXvfvUJsX0kFUnRGGaTOZyaTeH3rtmGgFe3Cq+yBWwYCDl6vwS9OqJ+K9kgkS3gvIEQNGalbpIzQKmO52JZbBsOl41t63AEh/7yBvH9rokGRN7161+4vuz1y2Eg7ANjEL3a63nuMnRdXbltqEwvrwVlzJTWvrSDjQMhHPjwdU1lELUSj17fAekM4y4VMZGXR8Z9TY/f6mjns+7mstbbiOeuawybBkMVddZ6nvv7btiFBXsjCKDcuMv9LBbtjQXCDRid4lit167ts4KihsmFkZeX5PJd2rmRt0f09ZDT8BohLOWwlyJSIaXnAo4soXLjXqkghJA9bgosffB1u0Wm0caBkCgZp4pJeu7XLtuEl20fwku3D4IxhrDP0v8b8dx9Hs1uR2EHVG1j/59PjOGxU3P4zK9dKl47n8xhKp4Vza+Wiuy5M8aETJOxHYB03sCrLlqL//fEGH5h1zDmkjnsH1sURUODER/i2QI29gcdlbsBux/MoYkYOLdWbQNhv+h22S957kDl7xVwBiQpZvW712xbVnCx2ueMRAOi0CrQhBGkBAEKsjYKHUOlWFw7WC3DDgBezTWeOxwBVcC62ORc9ojfI/Q7wA4gVigGEAFV23MfDPvQF/Q6NuwlivunVv6S1kQDWBMt7ixUqrmT95jOGViws1eWEgn/nZdvxesuWYf3fWs/jp+zjPtAlRWAfNH2Br3CEDc7oX26Bo/GhLQkc/WOYfzBtTtE9kQpAa9zs5HTc6ma6YNylS4t18nDBSz5654DEzBMXrbb0JoePzYNOjN7EtlCQwFVwK5STeQwHPHD59EQ9OrYP7aI/WOL+NSvmsLYUeVutVYDjXLxhj68+9oduNpeAcgrSZJkfvH8NVjfH8QbLt2AZK6A+56Zwvl2EHcw4sep2RQ2DoQccQ3S3MngRwIeDEf9Io1xsMS4v2Gvs1CoEq/YvQYTi9srFs60go0DQZH10owsQzUf11eQVGpRKst0My7y3Lnw3GkJP2t77mRgQn6PaOR17flrEAl4KjZzkgOqtKtJNOAV3oBMsqSxUDXoAi2NrgvPPWdgMZWvmPbYCBRB//g9h0WhgyzLyDi2A7R3Prpy26BIkWoUxhjCdol8Kb0hL97zyp1V/1aWZTjnODoZx/+6tPqy3u8p9g8v7ZsBWJ57weSYWEw7GmP12L1qZGjF0YjnDlg3ZMosAqwbIq3+FtP5ssDfeYPlckYzeHUNfySdOzJqmbyBCTuYur4/iFdLhTvyTZQys9b3BR291K1sGa/oBBjxW8b9sN3cqz/kQ9TvwZXbBvGKC0bw1gYM9ppoAH983a66r1sqG/tDeOyUJa+V9jqqxftvOB9j86mGJU6C5kYlubbbqNdXCegQ4w4U70TU/H/eNu7X7LIyCMJ+j5jIt1y1pWrkXr6YaLPZnqCzpQFRK1tGJuzTMRD2YUvJhU+eeypXcBiQpTIYLnpilbZ1A8o994BXx9feccWSPi/s05uSkQg5oDqxmEE8W6jr8Q6EfdWNu52dcmau2AMfKG+gBMhNsBo17h4MRfwiSLppIIRMwZJIFlLF7o3UQmG532EpwnMvGEgu2m0BakhoO0aszpIBr+7ImAr4NIf0FQ14HH2/B8I+aBpb8lxoBxukuEoznvvvXrNtSZ9HkmWzyQVuxNuA594ZqZC8mAoJWBP11GwKyZxRrDLz6aJRTqU0QcLhudubzVIKWaVNnYH6nrtH1/DQ+38Rb3jRBsfjohNdzsBCOlex1UAzyNp0pYZjgDM+UEvnboT+sK/muayGrLnTzahebjitREo3IgCKBWNn5lNYSOVE8U0l407fVSONwwBrrsjxiK/cchk+aWd+yNvWxSrk6rcCuahufCEDr86q9hYBgPe8Yif+650vtcduzQHGLBlNrtaM+L2O81PNGVhNKE2Qxt9u6Mb/vJBlVkJzZ4zpAPYBOMs5fw1jbAuAbwAYAPAEgLdyzmvuSszhDBoOhH14xN58ulI1WW3jbr1POm9gRpJl8oa1P6ScZTE2l7Ir9eov4yplZ9CelJTnvpzKRqBY1Rf26VWDNXIgtJF0qFp88o0vaNgDlgl4dRGgpsKfnWvqeO62oapksCktcmwuhflkHrtGo5hNZkVmjUxpQU49PvS6C2FIDZYCXl30DHdudJxH0Ks3tNxtBr8syyymMdobqJlq6dE10FdPsgR1KZRvPCTLENWcgdWECnxKuyy2i5CQZZ4Hxn2FNPc/AHAYAAmHfwPgU5zzbzDG/hXALQA+W+sN5ApVwDLuB+wmU1SqLssRtSYyGfdzsYydeucXF2w8k3cY6RMzSZxnbxawFIQsY/ckaVYjLIX01mp6OyD3e1n+V7fUSkxLNsvjcw+cwKMn5zDaE6gbb+i3+5tX8jC9uoa1vUGcmU9jIZXDBWt78Bev3i3K8WVKS+nrQcUmMjR/5OBtLF0oayPbCvweDYxZXTQXUvmqgfJK+Dwawr7iBtRyQY9s3HsCnpbflFpBrVYY7eBVe9YiVzCV5m6zrNnMGNsA4NUAPgrgj5h1e/5FAL9mv+R2AB9CPeMO5zKDDMDGgaD4We6LUWuyeOwsENrcgRr2AFbZuazHnZJ2YV8KPo8Gr86QsPcXXa5eSyXbtZbY5LH2tFgbbga/V8PZhTQ+evdhAGgoN/xl24eQKdkkROa8wRBOzCStvjohb9UMDiHLLGHFQdDKTw7exrP5smyoVsCYVSSWzhtI54yGVxyE7DDIN/SIpLlX2/xhtRntCcCrs7JdwdrF5qEw/vAV1RMBuglPAw7pcs/6pwG8DwCtewcBLHDOKXo5BqBiGgVj7FbG2D7G2D7T5I5lBl18coMp8twbWX76PZroaWFp7tbfynuzGibHc7MpbKlQ7NEMoZKK0eVAF2kt2YnOw2oad9LbL9nYh6BXxyXS9mnVeP2lG/Avv/6iqs/vHIniyGQM6bzR0MplKXISEfLp8OrMEbyNpQstWQ1VgnbeSlZpUlcLa8+CYjtsIuzXhedeqQ9RJ6BrVp7/Snnuzye8DdwwlzybGWOvATDFOX+cMXYNPVzhpRW723DObwNwGwAE1u3g8jKDAosXry8aDfJ4GpE+/F5dpE0OR/0icPazZ2fxm198FHe9+yoA1oYepRkwzRLy6aIZ1HJ1T5JlannupLm3OqujGXIF6yv9+1+5GANhf0uCkLtGoyKvu9Z5jDSZClkJxqxOh/IG0fHM8mW1alDqaDpnVKwrqMVw1C/kIzrPVnFW0bgPhDvTcwesrekq7fuqWB7tLmJ6KYDXMcZeBSAAS3P/NIA+xpjH9t43ABiv+04l2TKVPHcyao14KQGPJrrpWbKMZYx+fHQasUwBP3t2VmRQLEeWASx5gJpBtcpzr2XcKFummYZKreYjN+3Bb165WWwn2ArkVMpGVi6NFjFVoz/kFa0OAGtVt2mZN/pqBLw6MgXbc2/Si/2LV+8WWWL0nUeluIvPo2Eg3JmeOwB84LW7kc41treConHaGlDlnP8pgD8FANtz/xPO+a8zxr4J4A2wMmZuBvCduu8FZ7bML5w/jJtfch4uPU827tZQG0nd0+0Dv2BtDyJ+D1L2RXHY3hxj/9gC0nnLmCxXlgn7PDgxbQV/G23/WQ2hudc4Rrnz3Wox2htout1BPeSy/1o3t2svGMHZhTTWLjMjoi9ktZb+3tOT2DUaRTyTb3kaJOH3WHUBqZxRtY9RNbZLQWUqoqP/GWP4k+t24gUbm9/8YaXYtswMMkVlGslzb8dsfj+AbzDGPgLg5wC+0MgfeSXPfW1vEB++cY/j+XATmjsFU9/zih1gjAmPR+xgc3YRXt3KRKiUd90MFNgL+fRlT+TR3gD2rO/BpedVv1h9Hg1XbB1w7GbUDUQDXqzvC+LsQrrmDXzjQAh//ura26A1Qn/IiyOTcfz+157Ar+zdaGvu7blhkiyTanCTkWp4dQ0he/s+4taXL63gR+FuViTPHQA45/8D4H/sn08AuKzW6ysOpE5qD1VSNuK5b+gPYmw+LTY2DnitDBraucTaDcfA9pHosvNvaVx71vcuOaWS8Ht0/Pe7rqr7um/c+pJlfU6nsnMkUte4t4r+kFUoBwAnZxLIGWbbPPeAV0M8U4Bh8qazZUqJBjxNdUpUdCeNyDIdkxxbb5nRjOd+17uvwoEPXScMN2PFDTeGIn7kDY6j5xK4+SXLb5hEF2sjGSOK2uwa7YHGVqYgR87Lp03D25WBFLD7kwPLCwQD1k1pNSU5RWfQSMVvx7gA9fI2I01o7v+/vXuPkbMq4zj+fba77LbddkthS5eyUJa2SCnQ1kVEiDSigquGi62oCRICEhWUm5EiiZgoBDHRKAiKKBRtCIZCJd4aUiACIlqxLdTSUgJItVwUTGljIW0f/zhnytthd2d257Jn3v19ksnMnHkvz74788yZ8573nP7e/BPamnlt+1v0HTWVOx57gRlT2t8xg/pwFD6s2ZO/MjznnXgovYfsW5euc9n3UaE3Sq26QrY1Vy+5X3vmUaq5S+NM1gGlg50xpZ1P9h7EiTOHNwdhoT31uEP3o8mMj8yZWnEzCmSTu2ruleqc0MoHY1NarfXX66p2be5NeyZYqbRZZv7B+TrXIsNTzkVMyST3Us0ybS1juH7h0Kb6yiq0p3ZPHstHjz5y2NspNmdaB3O7J3FwhT1lpL4KfdpnHdDOxjiGfi2GH4C9r6attOYuAuUNP5BMm3s5Z38rUaiVVTL5cX8W9Xaz/MIT6jIwklRPYY7SbNNcLYYfgL1n7Km05i4CDXZCtZxgKzFpXAsTWpuTHD1P6u/IAzt44PKTODMzyUgtu0IWqOYu1dBQk3XUelS7z72/h1PmTFUNW/bo6Wxn125nTJOxa7fXtCtkwfghXsQk0p+RuohpWMo5QVCJwzrbdbWcvMOYJmPqxDZe2rqjZrXqvSYXV7OMVEHdLmKqhhTHo5bRoaujje1v7azZr7rskLfDmdZQpFhj1dxr3OYuMpCezvF7uirWwt41dyV3qZyZlezKnU5yr3FvGZGBXNU3m+1v1T65NzdZXeYSldGhVFN2Msm9nJ8ZIrXQMa6l5DSBlSicUB27T33mEpXRoVRFIZlqRKWTPYukqjXW3MfrZKpUUamm7GQyaq17y4iMlLEtlc8eJVKsVIU4meSu3jKSV4U296FO1CEymJYSFeJkMqp6y0heFdrcx7WoWUaqp2Y1dzPrNrMHzWy9ma0zs4tj+WQzu9/Mnon3ZQ1jV86EryKNqK1ZNXepvlq2ue8ELnf3I4D3Ahea2WxgMbDS3WcCK+PzigMVaVRtanOXGqhZbxl33+LuT8THbwDrgWnAacCSuNgS4PRytqfkLnm1p1lGvWWkiurSW8bMpgPzgMeBA9x9C4QvAGBKOdtQs4zklWruUgulLvysOKOaWTuwDLjE3bcOYb0LzGyVma0C1dwlv1qbmzBTzV2qq9SFnxUldzNrIST2pe5+Tyx+2cy64utdwCv9revut7h7r7v3hkBVc5d8MjO+edocFr678jl7RQoWHD54o4i5+7A2bOE66iXAa+5+Sab8O8B/3P06M1sMTHb3rw62rdaumb598wZdpSoiMgRm9tdCBblYJb8TTwDOBp40s9Wx7GvAdcAvzew84B/AonI2Vo3JqkVEJBh2cnf3R4CBMvLJQ9mWgQZUEhGpoiTaQZTYRUSqK4nkPnGsehGIiFRTEsm9e99xIx2CiEiuJJHcRUSkupTcRURySMldRCSHlNxFRHJIyV1EJIeU3EVEckjJXUQkh5TcRURyaNijQlY1CLNXgRfqvNv9gX/XeZ9DkWp8qcZVkHJ8KccGaceXcmwwcvEd4u6d/b2QRHIfCWa2aqChMlOQanypxlWQcnwpxwZpx5dybJBmfGqWERHJISV3EZEcGs3J/ZaRDqCEVONLNa6ClONLOTZIO76UY4ME4xu1be4iInk2mmvuIiK5peQuIpJH7t4QN6AbeBBYD6wDLo7lk4H7gWfi/b6x/F3AY8CbwFeKtjUJuBt4Om7v+AH2eSqwAdgELM6UXxTLHNg/wfiWxvKngLuAhxKJ66fAGmBt3E57Ssct8/oNwLaUYgNuB54DVsfb3MTiM+AaYGNc/+sJxfZw5rj9C1ie2LE7GXgixvcIMKMqObMaG6nHDegC5sfHE+KbaDZwfeFAAYuBb8fHU4Bj4xuu+J+xBDg/Pt4HmNTP/sYAzwI9cZk1wOz42jxgOvA8byf3lOLrI847DtwLXJtIXBMzy3037jeZ4xZf7wV+TkjuycRGSO4LE/5MnAvcATTF53NSia1ouWXAZxM7dhuBI+LjLwK3VyVnVmMjI3EDfgV8iPBN2JV5s28oWu4b2X8GMJFQA7IS2z8eWJF5fiVwZdEyzxOTe4rxxfJLgWtSiovwpXMzcEVKxy1+AB+M+9uWWGy3U5TcE4vvzwxS40zkfTcBeJ1MJSOF+OI+j8uUXzvYtsq9NWSbu5lNJ9SeHwcOcPctAPF+SonVe4BXgdvM7G9mdquZje9nuWnAi5nnm2NZw8RnZi3A2cDvU4nLzG4DXiL8zL2hKN6Rju8i4L7CfhOLDeAaM1trZt8zs9bE4jsMOMvMVpnZ78xsZkKxFZwBrHT3rdnCBOI7H/itmW0mfF6vK7HPsjRccjezdsJPq0uK/0llagbmAze7+zxgO+Hn1zt21U+ZN1h8NwF/cPeHU4nL3c8FDiS0TZ61Z6URjs/MDgQWUfSFk0Js8f5KwhfisYR24SsSi68V2OHhEvyfAD9LKLaCTwN37rVSGvFdCvS5+0HAbYQmy4o1VHKPNdFlwFJ3vycWv2xmXfH1LuCVEpvZDGx298fj87uB+WbWbWar4+3zcbnuzHoHEU7GNER8ZnY10AlcllJcAO6+i3Ci9xNx/ynENw+YAWwys+eBcWa2KZHYcPctHrxJSADvSejYFbaxLD6+Fzg6odgws/0Ix+w3mbIRj8/MOoFjMuvfBbyvxD7L0lyNjdSDmRmht8V6d89+s90HnEP4KXMOoe1sQO7+kpm9aGaHu/sGwpnqv7v7i4QeCIX9NQMzzexQ4J/Ap4DPNEJ8ZnY+cEpc11OIKx6fw9x9U3z8ceDpVI6bu68DpmaW2wbMJJwsS+F/2uXuW+LxOh14KpVjF19eDnyAUGM/iXCSMJXYIPwq+7W774jLp3LsXgc6zGyWu28ktPuvH2yfZatGw309bsCJhES1lre7NfUB+wErCV2XVgKT4/JTCd+WW4H/xscT42tzgVVxW8uJ3Z362Wcf4U36LHBVpvzLcXs7CbWDWxOLb2csWx33O+JxEX4lPgo8SeiiuZRwMiqZ41a0zLaUYgMeyBy7XxC6kaYU3yRCrfhJQpfBc1KJLb72EHBqovnkjHjc1sQ4e6qRMzX8gIhIDjVUm7uIiJRHyV1EJIeU3EVEckjJXUQkh5TcRURySMldRiUz2xUvMFlnZmvM7DIzG/TzYGbTzWzAax1EUqLkLqPV/9x9rrsfSbhwpA+4usQ60xnkQjaRlKifu4xKZrbN3dszz3uAvwD7A4cQhv0tDAB1kbv/0cz+BBxBGAVwCfADwpWMCwhjq/zQ3X9ctz9CZBBK7jIqFSf3WPY6YXCuN4Dd7r7DwuiGd7p7r5ktIAz3+rG4/AXAFHf/loVRGh8FFrn7c3X9Y0T60TBjy4jUQWHkvhbgRjObC+wCZg2w/IcJA2QtjM87COPRKLnLiFNyF2FPs8wuwiiAVwMvA8cQzkvtGGg14EvuvqIuQYoMgU6oyqgXh139EXCjh3bKDmCLu+8mTJ4wJi76BmE2n4IVwBfi0LGY2Szrf6IGkbpTzV1Gq7FmtprQBLOTcAK1MPTrTcAyM1tEmHZveyxfC+w0szWEae++T+hB80QcQvZVwnC8IiNOJ1RFRHJIzTIiIjmk5C4ikkNK7iIiOaTkLiKSQ0ruIiI5pOQuIpJDSu4iIjn0f4xLQRokn23BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='Fire'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('EMS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zcnmd67d00RYNrlg5lVIbdcVx64tRIQn1YrWDcqkelMeDbR9Uc04M+bxx8rE4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\nGEZJYgrKMAzDKElMQRmGYRglyf8D0QfhuyNHPrIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130442d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'EMS')"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Cc5f7lbgc5kc+1mzirnZiav1oN6VSIWkRE3Hm0vLiHheRe71XIy7WczeE5x6JpQAAl6wLAwBON5u4U+ROEG1F64u7EPWMweta9Gqx2TKGSIWU6Y8Xre0E0HzpkO20QjVXSqH1RyEEsVhaXtyl5w7UN/VNF71aPHdpy7zzqvW46aLVeN91Z6HL72o6Wya3jWDrCyJF7gRRXT33piauiXsinYXfXZ9TWmy2jCGyZbr8LnzurWbl5L6gm2yZOiJFncSdOJOhyL1KFmvLJDMG3M78j7k/5Gk6WyYrbJmMwZsuB79WFrslIkG0Ey0v7vF0fuReL/KzZaoXv2QmC7cj/2Pu9DkbUm88lTGq/kzSxuIKozUjKbJlCKINxD2VK+taz3TIxXruyYwBt8Oe91jI68Rcov7i/gf/+QTO/stfVnVsZpG2UzMiO992WJBFEIul5cU935apb+TustvUz9XAOUcqYxRF7iGPE3PxdN3tj+eHIgCAQ+PzFY9drO3UjNCEKkG0gbjn2zL19dy9Lrv4ubr3kXMAhZ57yOuAwYFoqr65+Wu7vACAHz4zXPFYfYeoVo94c4uYWvs8CGIptL6469kydY7c/Urcq4u4lbgX2jIeJwBgrs6+e9Btvs9Pnj1VcXu/jFavvtWLh1HkThBtIO55tkydPXcZuVcrGtImKrRlglLc6+y7S5GbXEhiRqyQLX1sO9ky7ZPWSRCLpb3EvY6ReyrD4XbY4bCx6m2ZtIzci20ZAJhP1HePT72dlbJm2mtClSJ3gmh5cU+ks/A67ernepExDDjtDE67bRGe+8rYMvpkbkVx1z33Fi8eRqmQxHLxge89h7+7d99KN2NRtLy4x1IZdPldAOpffsBpt8FpZzV47ta2TMjbGFsmleXqvSpNNus+u+6/tyKy8022+Aik2dh5bBovnppd6WY0lJfG5rBbZJ21Gm0g7lmEfVLA6jihmuFw2m1wOWxVZ2HkJlQLUyFNW2YuXl9bJpXJqveKV4zc28OWyRocchBSS2lmojJ/dc+L+MdfHljpZjSUVMbAVLT8fFWz0vq1ZdJZFbnXNRXSMOB2OkxbptoJ1bR1tkywQbZMOsvRISL3eIW0y3axZRa72IyozORCMm9HsTOBdJZjLtGa4t7ykXs8lUXQ44DdxhqyiKk2z13YMgV57i6HDR6nDfPJOkfuWUN1JJUnVNsjW0YXdPLclw/D4JiOpjDTYlHswfF53Pr1nYse1SczBuYTmbrv9FYPWl7cY6ksPE47PA5bfSN3YcvU5rlb2zJAbpVqvcgaHFkj57lXsmXSWUNNTLey564Leit3Us1GJJ6GwYHpCim1zcaTR6fw8EsTODEVW9Tfy2tousU6NaAKcWeMrWWMPcwY288Y28sY+5B4vIsx9gBj7JD4v1M8zhhj/8YYO8wY28MYu6SeJxBPZ+Fz2eF22uteOMwhsmVq99ztRc/Vu76MvCg7RNplNdkyuTz+NrFlKHJfNqYWzCqmibRR0eJrJmS6caV1HqWQ19DUQhuKO4AMgP/FOT8HwJUA7mCMbQfwcQAPcs63AHhQ/A4ArwGwRfy7HcAXl73VGrFUBj6XAx6Hrb7ZMoZpy7gcNdgyaetsGcCcVK3nhKrsgEKeKrNlDK4i91aOePWKnfW8Hs40JjVxW6xQrgQygIosss36QsBWo6K4c85HOefPip/nAewHMAjgJgB3icPuAnCz+PkmAN/kJk8CCDPGBpa95TB9wETatBM89Y7clS2zmDx3C3Gvd+Qu3rtaWyajrcBtZXFPtdFirGZiKpoTt1ayKGQANR2t/V4zDK4SDdo1clcwxjYAuBjAUwD6OeejgNkBAOgThw0CGNL+bFg8VvhatzPGdjHGdk1MTNTecuRqyXhddrjq7bkrW4ZVXc+9rC1TZ89dilxQpkJWypbJcviEuLdybRkp6F6nnbJllhFd0Fspcp8XAdRi2qxfP3rn1ipULe6MsQCAHwP4M875XLlDLR4rUgvO+Z2c8x2c8x29vb3VNiMPWXrA5zIj97qWH1CLmGrx3EvbMkGPo67lB2QH5HGIjq/CZ5M2DHiELdPKoijF3e92kOe+jOTbMvXfi2C5kPfYYmyZPHFv18idMeaEKezf4Zz/RDw8Lu0W8f9p8fgwgLXan68BMLI8zc1HfnFBjwMep62uhcMyWQ6XwwZXLbZMidoyQM6WqVdN91TWFHOnwwav045Ehcg92y6euxJ3e0uI+57hCH6+Z3Slm1GRqYUkHCLHvZXSIWXkvhhbRl/PMtmO4s4YYwC+CmA/5/wz2lM/BXCL+PkWAPdoj79LZM1cCWBW2jfLjRxqhX0uuB32upf8ddhqry3jcthgfoT5hDxOpLO8blaSzHhx2U1xr+y552yZVhZ3ed5+l6Mltgv8xm+P45M/27ti77+QzOCdX30KB8bKDcbNyHVdlw9Ai3nuyxW5t6ktcw2AdwK4njH2vPj3WgD/AOBGxtghADeK3wHgPgBHARwG8GUA71v+ZpvILyzsddY1cufcnFhx2m1wOmw11ZaxitoBcx9VAHjHV57E0PTicnDLIS9Ml4PB67JX7ETSeROqzS+KpZAdU6BFbJl4KtuQLRdLcWh8Ho8dmsRbvvhE2eOmoyn0hdzo8DrPGM9dn1trS1uGc/4bzjnjnF/AOb9I/LuPcz7FOb+Bc75F/D8tjuec8zs452dxzs/nnO+qtVH37x3D73z20Yrbw82IoVanz2Vmy9Qpcpdi53KYi5iqr+devH+q5HUXDODDr9qKPcOz+O7Ok8vWVokUOafdBrfDVjFyzxpcee6tHLnn2TJZo+5bGS6VeDqLRNooO1/0s90jePddT9fl/Q3x+cwnM5iYLx2dTkaT6A640eV3taTnvpg2S2vT7bCpPP9WoilXqO46MYOD4wv4/S/+tmxUK3vjTp8LboetbqmQOaFkNXvupSL3oMeJD71qC84b7MAzJ2aWra0S2QG57DYRuVdeoeqy22qqV9+M6BOq5u9NLu5iLqTc5Pr/7B/H/+w/DaPCblqLQR/t/uDp0kHG1EIK3X4XOn3OlvHcM1lDJV0sKltGRO6rw15MRlNNHygU0pTiLh3quUQGz54sLXyRWBo2JidU7XVbtCIFw2GrvbaMVY67zqXrO7F7KGL5mpxzHD69UHuDkbNl1IRqFStUc3MKrXUR68hrICDEvdkzf+T3Uk7cj01GAQDR1PJnV+n3TKkgI501MBtPo9vvRqfP1TKeu55wMRtPV9xqshB57awKeZDKGFiocy2o5aYpxV2/4GbL5ILPxFII+1yw2VhdFzHpQumuIZ++nC0juWRdJ5IZA3tHiie0PvXz/XjVZx7FwQr2FAA8dmgib5ST1iJ3T5UTqg67DY4abKdmRHZMMnJv9nORkWWpNQ+ccxybEOKeXNz1/eD+cZyKxC2fk3bQph4/jk1GkcoYuHvXUN4oQfrN3QEXOv2ulvHcpbiv7/aB8/JaYoUMuAbCHgDm5/DrgxM4MRVd3obWiSYV96yacJwt45VFYmlVy12K7mKHTi+NzZecUZeLelx2hk6/S/iklW80mS1TjkvWhwEUR033PH8KX/nNMQDA8cnKF9P7vv0svvjoEfV7bkJVZMtULPlrZgO57LaWLhymbJkWyfyJV4jcJxdSqnroYiJHzjne++1n8a8PHLR8XgZSZw8EMTQTx892j+CjP9qD3cO5DSrG5hIAgIEOD7r8rRO5y4nq9V1+ALVbMzIwGOgQ4h5N4o7vPot/ud/6s2w2mlPc0wYCHge8TnvZ3jYST6HTZ9ZyX+oCnD+884mSX5puy3SL2vHVFPBPpktny0gGOrwYDHuL7Kf7946r1aXj4uYqRSyVKZoQk22WkXu50YYhNriQhdGqXYHbjBR67s0eucsgoVTGzDGtY48uQtwXkhmksgZ2Hp+2fF6J+6oQsgbHfS+Mivbk3mts1oz6VwlxT2YMxOpgES038jNdK1I4a02HlFoy0OEFAJyYimE+kcELDdyNinOO0/Pl7/9SNKW4JzJZuB12dHid5W2ZaFpF+Lm9Qmu/mReSGURiaTw3ZO05yojJ57KjO+AGgKpmz5OZ0hOqOht6fBgtGDafisRx/mAHHDamIqdSTM6nitokRc0paseXG2mkjVxmjcPe2hOq8ryluDd78bDchGopcc/NuSxG3OX9c2IqhrHZ4usoJ+5BAMCjB81SIDHtvUZnZeTuVRvjtEJqoG7LALUvZCqM3PcJ6/TYZLTu6atPHp3C6bkEHtg3jmv+4aFFCXxTirvMMpHi/vTxaXz98WNFx0WE5w7kNqFOLsJ3Py3E86WxecuUNLk6rTfoRneg+ou7Gs8dALr97qKRwEgkjrWdPvQF3RibLd+RTCwk8toJmPunAtUtYpK2k7RlGj0JuX90btkyEXKeuyxfvDLifnwyWtEK45yr76VUhdBjk7l5lMVs7hLRbE2r6F3eL2evCgHI7cilW0Bjswm4HDZ0+pzoEdd/s1VJHInEiyxcJe4icq/VlpFBziop7qO5ebF67iWbNTje9bWd+I9HjuDFkTmksxzjFTTAiuYU90y+uH/3qZP49H37i27UmVgucveICHkxkdppYWeksxwHx4qzU6Td0RNwo8cvIvdqbJkqsmUAc6JqUrNUkpksTs8nsTrsRX+HB2Nz1pNhufaVjtz1VMhSAipvaLvIlhmajuHuXUOWxy43P98zitd87jH84sWxJb1O1uB4x1eexMMHzCoYfpdMhWycuB8cn8cD+8aRNThe92+P4Ru/PV72+FTWUPu9lovc5ehvKZE7AOw8NlX0vLxf+jvcav4KyE30AmbkPtDhAWMM3fL6LwhuOOf4/s6TdVmQVw23fG0n/vn+/P1d5We6TkTutY421CjQ5UDI48hLeqinuI/PJZDKGDgysYCTYvJ2MSOFJhV3Ycv4THEfn0sgneU4qg1RE+ks4umsityl576YjJnTmrBa+WkySukNutGlIvcqbJkyee46PQE3oqmsivRkL7067MGqkMdyOK0zIdqiv4bKzXeYmUQGLz0fkcnm2zK7h2fx0R/tKTuZvVz85NlhAMDCEouoRWIpPH54CjuPT8PGkNt4pIHi/jc/3YtP/GQP4uksoqksTlYQukQq17a5Eud/bDKK7avNqHox4i4j98GwF78+OFmUK5/UgoCNPX71uJ52OTabwKqQGb3KkWvhpOrnHzqMj//kBXz7yRM1t7GQ/aNzJbN7SjE8Ey9qkxwN9Yc88DrtNY820lpSQk/ArTrKTp8TL5wqX66hEt/beRLfKvFZDc+Y5350IooT4hpaTAXZJhV3A26nGbnPxdNKfA+M5lIC5UUbXgbPXUbmbocNL5yKWD7vddrhdzvgd9nNFWtVRe7V2TJyqCvrV8gLezDsRX/Ig/G5JD75s734ix/utvx7PeqXF3CqIBUSyBcTHRm5O+wsLzqZaMDQe+cx0yqwLXHjZT3bxFxJbF4PjbJlJuaTePLoFOYTGeVXl1vxCeTX2LeKzLIGx/GpGM4f7AAALGipkM+cmMbVf/+gErQH9o3jxs88WnS+kbj5/C1Xr8fJ6Rh+fSi/vHYyk1X1jzb1BOB22GC3sbyOZHQurnxnGblParVWnj05g8+IbJwjEwuq7Z9/8NCivOL3fHMX/uz7z1V9fCyVQTydLSo/Mp9Iw+eyw2m3oTforlnc1byV3aY6tYDbgcs3dmHvEiP37+88iS89csTyuVMRU9BHZuM4Ita5tE/kXuC5S098v1bcSF+dCuQi98WU/T09n4DLbsOODZ140aJHnlxIojdoXtSMMfQEqrtQytWW0VE3jBg2jghxXx32YqDDg4VkBnc/PYRdJRaZ6CIsO5101gBjptUiqz2WKs+gonybDZt6c9Gb1ejkoQPjqn1LZXwuoXzkSnn4ldAvfqfYMQtoXOT+ixdHYXCzQ5dtqdQ55om7hec+EokjlTFwzkAINpYfud+/dxwjswlV8GvnsSkcOr1QdF3KIOitl69Db9CNuwqsopQ26f/BGzbjy+/aAb/LrnLqDcP0e1eJjBGvyw6fy55ncTxxxLR7Xra5B0dETv7ekVn8ywMH8asa7bbZWBrDM3E8fXwmL1OoHLIthdf3fCKjMs56g+6KnW0hKa3siLxH+0JurM8hYeYAACAASURBVOvy4VQkvqR5oplYGqciccvVvsPT5v3FeW5EJ6+P0/MJ3LunuiK7zSnuWrZMNJVVJ6hH7rmKkMJzV7bMIiL3OVO813f7LYVrYj6pomvAHJpWk+srRyCV6AnmZ+DINqzq8KjJnGgqW/I9J+eTkIUnZRSfEuUEGGPwiDaUmuDLap77j//0anz/9ivN1yrwKCcXkrjtrl34bImc6Vp59KVcFFmpJLFOLJXBJ37yQp5tlBe5283SzEB9IvcXT80W2X/37s4VPpVzIJOVInftnK08dyluG3v88LsdeZOccl2EHMKPRMwAqNiaSMPtsCHkceJtl6/DIwcn8qJpfXS5vtuPa7f2wu92qI5kOpZCKmuoyB0wr3+9498zHMGGbh8uXhfGyekYUtqivEoLh148NZvXaenVKX/8zHDZv02ks3hheFadc2HkPhVNqm0mewKuRUfuLi1yXxXyoC/oQTJjFE1wpzIGPv7jPdg7Ujmql6JuZQNbWVIyYPj648fx/u8+h5/urizwTSruuchd4rQzvDRWbMvIyN2tJlQX57n3Bt3o8pmr7wp9ST1yB4Buv6vi5AznXERF1WTL5GfgjMzG0RNww+O0oz+Uu6nmEmnlj+tMLCSxoduMuKW1k8oYSuBk5F4qOpYZJg6xSGtzXyDvtSQP7h8H58Bvj0wtS3bLC6dmVZmAWiL33UOz+N7Ok3j8yKR6TPckZbE0YPnF/ejEAt7whd/gJ8+eynv8yMQC7MJakiIyMZ8s+znJc3Y5bJae+3Exmbapx4+AJu7JTBZ7hCgMC09WCkKhuOsL/S5eFwbnyJv0tJoX8rnsakJVzves0sW9ILvrxVNzOG+wA5t6/cgaHCenoyptsFzBrt8cmsTrP/8b/Oevj6rHXhKrsbcPhHD3rqGy8wz/9+f78Hv/8bjypfXIPZM1sPPYNC5eZy4SLBW5f/GRI7j53x+3nAjWPXeZAt0f8qAvZP58ei7/9Q6MzeH7Tw/h1q8/jdHZ0qPbdDbXMZQS97O0ETSQu75lp/mX//1ixZFI84q7M1/cd6zvwthcQvV4hZ77UiL30/MJ9AXNineGxTLlifl8ce/yu8tOqJ6eT+ARkS9c7YQqkPMxT0USGBRLnldp4l5qCfXkQhLb+oPi55wtI60Jjys32Twxn1Q+tySj5bkDZofJWHHk/sC+cdG+OE5MLT0r4thkFGf1BeCqomqljoya9VGWHrk7HQxBEbEtdmPkUty/z+zgCkd48XRW5YBPaKOncpugy/PoD7ktJ8yOTkThd9nRG3QjoEXTe0fmVKeVi9xLiHs8hbA3F3UCyEuttcro0juSXI67Lu654GY6mlJrMs7qNYOCw6ejKm0wUkLc5xNpfOzHewDkd8z7R+cR9jnxtzedi4mFJD59337Lvx+bTeDup4eRMTheEKtp9cj9+aEI5hIZvHKbuftnT8CNmVg6L3vqxFQUn33gIJ4fiuBNX/yt+t4OjM3hju8+i2gqA5uwNuXIvS/kVlpQOJ8gR1ozsRQ++qM9lu0u/ExeGC4W9+GZOM4eCCldCPucqvPfNzKHbf1BzMbTeMoi+0mnacQ9mcniH35xAMcno0ims/AIW0Zy9VndAICjsoiSuPhk5Ceth1LZMr98cRQ/KzGUOT2fRF9Iy2HXbpB01sBMLK0+aEAM8aIpnJiKFkUWL43N442ffxy3ft0s0VqNuHtddvhddrUYaSQSx+qw6XGu6vDAxqAuqMJcXc45JuaTWNvlRcDtyJtQlWLtceQi939/+DDe+dWn8oooyTx3GXnabQxdvvxhbCyVwWOHJnHtVnNLxK89fgx/d+8+y5EEYN68Jyt0AMcmo9jU46+qPIKO7AhGtSyiQs+9L+iGx2lbVCc0NB0rOYElOzj9s+GcI5HOqhGY7rWX893lOfcFPSVtmY29fjDG8myZZ4Uls6nHj+GZuEqdBYpTdCOxtLqPpLjrUaXVpL/P5VArUOUCOj3I6A641KhORp7nD3aobJsjEwvYPyptGevO9aEDp9VoQ58XOTA2h7NXBbFjQxfe8/JN+M5TJ/H8UHGSw5cfO6r+bo8QSD1yf+SlCdhtDNds7gGQu3/0Efc//fIlOOwMX3nXDkzMJ3HP8+Zo7FcvjuPne0YxNB1T95D03KUtAxRPmB+diIIx4IPXb8FjhybxXImih/IeNhM48sXdMDhOReJYE/ZiU48fYZ8Tazq9ZmLJXAKTC0lcvdnUwkr7VzSNuP/9fQfwpUeP4EfPDKvIPaSJ+7mDZjrYlEr7k6tGTXGXF6hVnnvW4Pire/biCw8dLnoumckiEkujL+hRkZce/UxpC5gk3QEXUhkDN37m1/jCw/mv+cmf7c2zhuTiqkp0B9zqhplaSKrOxOO04+u3Xo7/87pzRNvyRSCaMuuBywVWUypy5ypy92qR+4GxOSQzRl5JA5kt47TnMlZ6Avmjk2dOzCCZMXDbyzZiVciDbz5xAl/9zTFlHehwznHHd5/D6z7/WMl6KIl0FqcicWwU4l5LCqsURV2k9OjPZbfBZmNY3+XH8RrE3TA4/uqeF3HtPz+MT/+8OGKcmE+qMhG6uMt8dRkc6Dd9uaFzXIvc55OZIjvw2GQUG3vMaLgwcl/d4cFF68IYmonlLXCZLrDSZuNpdIjRbdjnhNthy/vurVZRmx2J2bZpcT11+vU5JzemRQlcGTWfO9iBoMeJ/pAbDx04rWydUpG7HHGsCnnUqMswOF4am1cLqj50wxb4XHZ832K/g2dOzGCLsA9lzrkudo8cPI1L1oVVx6ZGx9r39tsjk3jDBavxqu392D4Qwr1iu0NZGGxqIaXuIRm594c8uci9wJY5NhnFmk4vbn3ZRoR9Tku9AXJ++2UbunAqEs/r2CcXkkhlDAx2evHmS9fgj65Yj5DH3JJzr+gwL17XaZ5vBcuxKcQ9kc6qBR82G0PG4GpCFQBsDNjSZ9oOUnhjqSw8TpuKNstF7k8fn8bp+aRlWpa++tRK3PUFTBLZi6eyBg6M5mfXjM0mcM3mHvSJC6CayB1AnjBHk1m1fB4AXrG1Vw15C4fdevu6/a48z12KtVezrA6fNi9ceXMBuTx3hy3X1u6AK8+Wkcdv7gvgDy9bi7Vd5shC91Q553j04ATu3jWEXx+cwHwio/LYC5GdwoYeP7yuylUrdeLKltEjdzMzwuu0q2hrfbevpgp+u4cj+OYTJ+CwMRV56nz5saPg3BTjCe2zkVZgLuupysg9nYvcOc8FLAtJ83MbnompaNjvzmWwzMbT6A64sbbTh7G5RF4HW9j5z8bTCIv7iDGGVR0ejGmilBKpkDp+t11F7tPRJEIeh/pMzfN0IZ3lmEtkcHB8AYNhr7pXt/QF1WTvui4fIiUmVEdn4+jwOvMsqZHZOGKpLLYKi9HvduA15w3g3j2jRbVsYqkMNvX64XLYEBUdibz3DYNj38gcLtvQpY6XgizvF8PgmI2nlX/++gsH8PxQBEPTMfV5TkdTat7qkvWd+Ivf3YZXbutFyOOA22Er+m5lZxxwO/CWS9fg4ZdOW45s5T2zTZR80EcTw2I0s6bTiz+4bC3+/He3meIez6h5jIvXmvMIleYXm0Lc9dVwslfTJ1R7AjmfSw47o8mMsmQAzXO3OGFpx8zE0kUfiEyz7Csh7voCJonMbnHZbSr1SxKJmxNYN27vB1A511m9pkivTGUMpLIGAu78iF+2TbdlYqkM/lbsv3lWb8B8DWHtpLI5W0bOS7w0Nq/OZ3hGKw+sTahKugsi95FIHDYG9Afd+PCNW/HFd1wKIP+z2jsyh1u+thMf+/EL2Njjx/mDHbjrt8ctJxVlpctNPX6zJHENtkwibRG5J9IIeZxY2+VVndqGHj9OTMeq3uTiqPgur9zUjaOTUdXuoxML+Ptf7Medvz6Kt1+xDlef1ZOXCSPbU2vkLv9OCoz0Ve/67XF85O7dMDhwgchx122ZhYR57a/p9IJzqBRZj9OmInc5v6LbMoAZeY7PVo7cc9kyaTWZKOnR6iudisRVRw8Af3fzeXjvK8/CH+xYgys2dpWM3Ecj5qpXuVE8kIvyu7XMtLfsWIOFZAa/LEipjCazCLidGAzn3ltGsvOJDAyeu2cAoDeQL+7yGLkI8vXnrwZgprRKK286movcnXYb7rhuM3wuBxhj6Au5lXYAojSzsBkBM/PI4NYr2eU9rAK2WHEQNRj2qcdCXrMe/b6ROazt8qrPvyUid7nVl8PGlFjo4t4bNDNHdE85lsoqSwaA6mELfSjD4Pjli2PqAi682XK1qnVxL745e7UL/KpN3fj0752PW1+2AUMzsbyIIRIzK1V+8IYtuHBtGK8+b1VVn0GPiJTlTaVH7kAuK0gX939/+DAePTiBT/3eebhwbRirw14MzZhipucv94c8WNvlxfe1nXb0yF3673mRe0FG0KlIHKtCHjgKOgx9wlIuYHn/dZvxH++4BO+8aj2OTEQta9XLuZMNPX54nbVNqMpg4PR8Uk2QyZzmmy4axHViEm1Dtx+pjIFdJ2bw0IHxiq97bDIKu43h5Vt6MJ/IYEpYD2/8wuP4z0eP4rXnr8In33iuSquT4q/EXVw/p0VqqtPOytsy4jz6hYcrUzufPDqFrf0BPPW/b8CrRJAQ1MR9LpFGwOPAmk5TAJ46ak6snTMQUvfPfzx8GK/6zKNiFXdO3FeFPHmF6MxsmfxAQs9zn44mVYkPSZdWGfXUTDxPiDb2+PGxV5+Nf3rzhegJujEbz9/B6C9+uBv37hnByGwCg2Evgh6HsmUWCubRAOCKjV0Iuh1Fvns0lYHfbS8Sd865SjrQOzUpiDLantH2XwbMEgVn9fpx/95xJcjTsVTeiEWnL+jJW9k+sZDEQjKjRlq5yetityAn7uax0/p9JsW9M3de0pYx5yNCqsNpCc9dfvf6RI3baVe1yKXFkW9dZOBz5S5Km43B5bAVRe6nInFMRVN45TZzInC8wCeTvWa33wW3w+xA9N52eCaWN6EJmKlRb79iHbYPhMA5VE8/nzSjAXO46cE9d1yjeudKdPvdmI4m1QVeKO5elx1epz1v0cOjByewY0MX3nHFegBm+lgslcXxqSjSWuQOANec1aPO3Wln+Rt7iGwZPXLvDZo+sJ6Zslq7kXIjiVxkJl/zjus245yBkDp33aaYT6TxiZ+8gF++OIY+kQVSzTaAOrIj4Nxc6fe13xzDXDyNkNeJO67bjA/csAUAsEHUFHnvt5/BbXftqlgP5NhUFGs7vdgibIFjk1EsJDNYSGbw0Vdvw3+841I47eZS9GTG0OwAYctoEa3f5UBvoDj97smjU/j7X+zPKxomM1EisRTSWQPPnJjBVZu689JgZTTNOcdCMoOgiNwB4LmhCHoCbgx0eJS4D83EcyLny0Wwpi2TUIJrlS3jdzsQT2eRNTimFlLo8udH7rJdJ6diGJ9P5AmRTtjrRDrLVWd8ZGIBP3xmGP/17CmMzsYxEPYIy8FsZ2GSBGBaSWG/s6jefSxpBne6uJvnY6jzDmvn7XWZ97b8PtQiSH+uA7h8Y3feQkHOUXI/hr6gO0/c5YYqUtzlZ2RVrnsmmoLHaVP303SB3oR9zrzPIOR1ivs6hi19AVEDirWGLSMjd30LLxl1bujxY6vwprq1jQJiqWyRAHoctqLeTG5TJ2fNJwp8d/l6Uqy6/K48Ad03Oo9NvQFl++hI8ZIRa6Rg1WwtdHidMHguvcrvchQdY26UIIewKewdmcM1Z/Wo52UNkn2jc3mpkABwtTh/j9OG8wY7Cjx3MaFaELkDuWHlSCSRJ+5e0fnqn9WJqRh6g241gSsjJ3mzZbIGPvC95/C9nSexZ3hW3QiVqlYWols4f3vvPnz2fw5iLpFByJP/ma3vkbn/KXAO/N29+8rmnR+biGJjj18NrY9NRpVV0OPXs6WEty5ubtl2eQ0Z3MwV7w2683zZ6WgK7//uc/jPR4/iyMQC4qLef49mOe4dmUMslcXlG7vz2uZ3O5AxOJJiu7egx4HVYS+u2tRtTsCFPXn3jz6iCnvzI/dUxlCdsqUtI669WCqD6WhKXQuSDT0+2G0Mjx6cAOfAmnAJcZejO/H9y0yj54YiiMTSGOiwjtwL7+ug25k3Ya5bl7JjkXNvyYyhSi7okTuAvBIEERXd587tio1dKKR05J5vyxwpEnfzOx23GLmZBQ9dOacglj9CXlPQWcrrOmtwbOk3NcftqLytaLGCrACcm2Ie9DhUrqgcKv7oT6/KpSMF3Co6jKbyPXfAjPYLe7NDp81FEVcLESyK3KMpuB02NQro9LvyIvd9I7O4zOJLB6CW6sv6D4W597UQ8prnIicJ/e7iziTsc6qI44kjU+AceNmWnAhs6Q/AYWPYJ/Kgddvqqk3mcWf1BrCuy5e3OYiaUC3w3AFTwAZCHozOxvHa8wfU84wxc7Nk7cI8MR1T5VVle4GcuN/34hgeeWkCn3zjuQh6HNggboTFeu6AOV+QzmYwNB3DOSIIkAyEPHA5bDAMjvddtxn/9uAh/GrvuKVVJj3TKzd1YzBs+vbHJqM4R2Ru6N+pFOPJhSQ29PhVe4JuhyqZ7Hc70Bt05036fvq+/So18Fd7x5FIZeF12fPmeuQ8wmUbO/PaJ6/1hWTG9Nw9DthtDN959xW494VR9AfdePzwJCJir1B9RKWLnFyMNDabUBtvFKVCimsvmsxiJpbKy5QBzHtzY49f1X4vFKPc+5p/F4mlMBj2KnGXHdDqsAeGYY5g0lnD0pYBzHtDT02NaZly8ntZFfLgVCSOZDqrRe4F4q6NpHKBmB655+5zGzM76ZKRe8iDuYQ5svU47Xji6BR6Am41kugOuGG3sbz5Dc45GGOqVLlP1KnSI/dTM/G8EiAAVLYTAGzuNa9xt8PWOpG7RxTmKozc/W6H+oC7NeGNJjNF0a25KUVx5N4TcGNTjx8OGysaJk1HU+jyu8DE+n19dDATTWFkNoHtAyHLdvtcDqzu8Cj/OFcSofbIXS6Tlh5d4QUOIG+Ls8ePTMLvsuOCNWH1vNthx+a+gLnIRUuFBMyo5fKNXbhsQxfWdHoxGkkoUVeFw2y6uOeKmU0uJJHOcrWwStLpc+WJyMmpmCqvCuRERXZ68kJ/0yWDeNMla3CJSOnyVtgpqpB4OpuXvQSYohcqiNRsNoaL14bxlh1r8MHrN2NrfwCfvm+/uik4N4tbnZyKYXwuiXg6i429fjjsNqzr8uHYRFRFgfp3KtPiRmYTODEVVZG7x2VXwuh1Fkfuu4ciuG5bHy5Y04EH9o0jns7C67Qj7HWCMTNyf/r4DDb2+FUutURGs1MLKWQMjoDbqc7xjReuxhWbutHld4Fz8zqcjafxss09eMcV63DJ+lxHUWgXpCwid3ntyWqshZE7AGztDygRLWnL+HLf/+SCmUZ6uZbBIiN3wJwzUbZMwQgs5Mm3ZaQdpnvuq8W1mcwY6norjNx7grm1G7lALHduq8NerO3yYlXIo/7WZbcuaCfn4H7w9BDiqSweOzSBV2ztVQXw7DaG3oBbfc6fuf8lvOZzjyESSwnNcYoSyjmrmXOO4Zm4mkvRz19yVp8p/G4Ll6KQphB3zk1h9rscqr61VU0WWdPFMDiiyay6kSQeR7F3e+j0Ajb3+WGzMfQW+GRATtwlui0j0+Gk3WHFpt6AsmVKRQzVIFdUjojIrXBoCphiKiOO505GcMn6zqJh47mrO7BvdM5McSt47vvvuRJ//YbtWNvpQ8bgamJNRkX6TSUv3tNzSbXYZHXB8LvTl/usEuksxuYSar9KwBzSBtwOdSPNJ9JgrNhyqjkVMpVFT8CFgQ4PrtyUE4ugp/gz+957rsSnbj4fDrsN/+d123FyOoYfPG3Wqp9cSOFfHjiIHz0zpEaM0pLZ2BPAscmo6rz0CE92LH/7s314zeceU6LkcdjVufnddrVWQE5Yx9NZBDwO3HhOP54fiuD4VAxepx0Ou5k8MB1NYmg6ZjlPI7OnZGRfKIAA0CXaNRNNYSaWwsYePz71e+fnBQoycpffqVVxOznikyWLuyzF3YwgGcttQ1eILu4HRufBOXDrNRvU86s7vKpDnounVW69r8ACDXnzbRlZddPncuDcwQ5cu7UXrxCL6xJa5F5ky2iR+0yJDuDWqzfiDy5bq+7HUpH7Jes7MdDhwV//dC/e/KXfIhJLq3k9SX9HbvL6hVOzODA2j/d++1lMLuQ2GdI3HJ+JpRFPZ4vmEeRnNBj25tb1OCvbMk0h7rnIPfelWtVk6fa7kRX5qbFUceTudtryTphzjsOnF1SOfF/IUxS5TxWIuxwdcM5VlkepyB0wIwb5mlLoFuO5S1um2sh9Np7Oy+CRbF8dwsR8EiORRNGFabMxMMZUpHVKW7rusLG8aFHVzxCvZZ5rgbj7c7aMtMvWd+dHHR1ep4p+55MZBFyOovK++grV0/MJ/MUPd6vfXzw1i4/9aE9evnA8bdoZ97z/Gnz5XTsgX06PcPRzlu937dZeDHR41IpG2akdmYgqcd+gUtl8ODEdVZ2pHuHJ62VyIYlYKqu8d1kxETCFpzfohsFzNkRCROrXnW1m8+w6Pq3mcsygIq02xihERuryWiucXwCALl9uhexsPG0ZZAyIRThPHDXrA1mnQpptGhLpsl2B4utZlrvoC7pLCqAsexCJp9RuYWcPhNT59Xe4lYjOJdIqxbPw+jCzRYoj94DbgYDbgW/+yeXYJuyzZMZQxdIK58l6Am7MJTJIZrKYjaUQEtaWzp+8bCM+cuNWdf+V8tw39wXw249fjw/esAV7R+ZgY2ZVTJ3+oFstdJqOptDhdeKJo1M4OR1TwUKX5kbI9OTCkZC8rqXfDrSULWM2Vo9WrRb/6OUBoqnKkfvp+STmExlVCKtP+7Al09Fk3rCzU20AnMXekVmsCnmK8nx1OoXgcs7VJI3VjVeJXORu3gR6JpB6L58Lc4kM0lnDzPO3eJ/zxCgjns7mrTjVkYttpDCPRBJY1eHJu9DdDtMLHptLqLolhRedOZLI7dEJIM+WAaBq8gNQXnEhHjGhyjnHU0en8cNnhrF7OIKswfGxH+/BD3YN4fBEbqOWeCorsqg8CHqcqtOxitwLGQx71fnIob5cLh9wOzAgbIuBDg8SaUOdV34RO1teJC8Xe3mcuWvY77YX5VbLdp+9Kgiv2EBFTj53+10YjpgZLqssxF0KtYymS3X+AHB8MgbOre1Bm43hVef04ZEDpxFNZc35LmdhKqT52rLD7rJ4HZlRVBhlWrU5Ekur+6436Ma5q0PoDbrhdtjVvSJtGau5ppDXTAOVHXxURe56MJhbxFiY2y/pVXMlKXNS02JEIpHXUuHoV4cxhg9evxmXrAvjyk3dRa/XH/JgXCRITEVTuG5bL266yMynl5+paQOLmlIzuQVMhecPAJt7C8W9JSZUucpjl1jZMnI4LLehsorc9Yk5mSkjlyn3h9zYVbCP5Ew0/0uWN8jOY9P41d5xvKZCnrpcrSc32Q55HCoXvBbkRT5Wzpbx526WhWTG8pjz13SoyaBSUYd+0wHmEL0wKgdyGQFOG0PA7SiKjDu1KppSdPQJVfle8n1klkchUuCSGUPduGOzCfz4mWE1ejo4vqCWpcfT2bybd6OosVLouVuxOuzFHrFkXnY6xyajcDtsOHd1SEWN0pt+aWweAW3eRyILUQG5VE+vNvqUkTtgRtKccyQyBjzChjl/TQd2HptWq4e7/C48IkogW0Xu8rXkCMNK3GWnIO3EcInP48bt/fjeziE88pK5JaHVIiYAGBJ1xa1smQ3dPrjsNgwW+MM6HqcdHqcNs/E0DIObn4/Ljo+++mw1QlWRezyNhZT1NS2PWUhmEPa5LNeC5PZzMEqOWvQsp4i2ctcKeZ06K6wwd9ht+J4okV3Iqg4PIrE0EuksZqJmSukHb9iM0UhCJWl0+l0qz11msK0J53+mPQE3rtncrRZGAiJbphU8d4ObUbee3WFly8iLTEYUhdGtpyA9SNXDFrPPfUFP3irVZCaLhWQmL3J/2eYedHiduO2up5E1OP7sVVvLtl3mAE9HU3kbdteKvIAn5pNw2JjlyEWK61TUnOC0usF9LofyQ0sOlwtS1EYiccsITOZEn5iOFdktgHlhGtyMusbFhieFQhD2OdX7zCeKM5wArSRxKquG3KOzCfz42WFsFnm9B7Vyz/F0VlW6BHJWUDWR++qwFyOzCRgGV7ZMMmNg9/Cs2vFInjtgFrKyigI/cMMW/NGV6wDkInOPM3cNy1RI+Xw6y5E1uOrILhJLyHO2jFtdu1aRuznpn1tFazUC6vQ54XPZVT1xPYdb5+qzeuBz2VUN+kq2TLeFLeOw2/CXb9iOW65ab/kekrDXnCeaEGWzGWPY2h9UBehkVDqfMLOAghbXhwx8ZIXNmJpQLR7pS8+9XORurtwtf6/K+9FdRaDmdtgt9UquzxmajiGayqI74ELY58Ldf3oVXr7FPP9uv0vUhzJrLQXdDvWZSJx2G77z7itxxaZcZpxpQbeELcPhdtryltyXs2VKDU09BQWoTk7H4HLY1ApA+fczIlc8l+Oes11Wh7340h9dCofNhvdcu7HIZihqk5YPrm/YXSsuhw0epw0GNy9amb2jI8VrNFLalwegaliXity9TjtcdhsiMTNtbmw2obINdPqD5hZ/J6Ziql68jjzX6VgKM9EUOkUGgE6HN2fdzCczCFj44mozkXRWTZaNzZplhS9cE8bGHj9eGp/HwwdOY89wBIlUNm/STbbNynMvZDBs5nlPRVNF5XjPX6OJu4jcJxdSliL5xgtX4/cvWSOOyW3T6Nc8d71YlcqoEe2+UGQ5SbHv0t7DaoLSabehy+dSdU+szpUxhsGwF/vFpjalxMvjtOOitWG16rNohao7Z8t4nLa8oEvnnVeux44N1mnCkk6/ufL69Fx+2WyJ7rlHS4xG1aSr6IxlDR6/1sHrMFSZ+gAAGK1JREFUkXukhLjrKayRCvdqJc+9GuToT5Y/tpqL69Is0uGZGAY7vZb3fiHV2DIVW84Y+xpj7DRj7EXtsS7G2AOMsUPi/07xOGOM/Rtj7DBjbA9j7JKKrYTMlrFX9Ny7RJ1xWZzfV5jn7shPhTwhVhzKoba8IWQVtsIFTJKrzurGzv//Bvz572yr2HZp6UwvpMyLapGRO5C70P0WfjuQu8hlqVurGwHICUep7fAYYwj7nJiNpzAxn0TG4Ja2TH+HB5MLZgaHZeSulUSYjhavZARynjvnHAuJtGVkJm/MeDqLBXHjHpuKYWwugXVdPmztD+DFU7N4/3efxef+55CaUJVcfVYPtvUHLTugQuR5jkTiRWV99chdTigDuYnBQmRnOzGfhMdp7nolr0m/y7yefS47JuaTKuiQo5SLRAfsFR2b/tnp5XV1eoNudX2X6tgHO72qIylnO6zSMjkKLVDZcRrc2m+vhY09PhybjGJiIakiWZ2g2wHGzLo6pazGkKdA3KXnXiJyn4un8xYnSXq02j8zFSN34blXWfjPCjlHJWu2W9lbqpTDQkqkQZaew9CpZhFTNS3/BoBXFzz2cQAPcs63AHhQ/A4ArwGwRfy7HcAXq2moAV4s7hYrQh1i6bdcNFQogoXpQSemYliv3fB6BPDebz+DP/+hWVDfatgZ9rmq6kFl5D4dS4m6MouL3IHc8LOUaKvIXabDlTjuPCFSBzQroxDphZdKcwTMOQrOzTx4mUWiIzu2SCyFKYuVjPJ9UlkD8XQ2b09LHd2WiYl0OFmzfH23D1v7gxidTSCaymIymlL54ZLtq0P41YevzVvsUYo8cY+n4bAxdHjN5d565+B22NX5lEptlRkskwsp1R4Vubvz9+5U4u4yb7nVHR5s7Q+o61N/L2+Jzl2PfEtdI7q9Vi5rS+9ACicNHXYb3nnlegTcDjVxuljO6g3g5HQMY7MJy8jdJuZzzFTIEraMN9+WiVqkTBZ67laRu5zAHZtLYD6RKZuyLG2vpUTu8ruQIyQrndELApp1eqoV98q2TEWTknP+a8bYhoKHbwLwSvHzXQAeAfAx8fg3ubnG+0nGWJgxNsA5H0UZODdLBwQqRO6AeVPsF6JVOFz0OG1IpnMLVE5Ox3Cl5lPp3t3zQxEVAS8mdVGiry6MxMpP0lRCRe4lxV1k1FSwZc5eFcTLt/Tgjus2l3yvsLBLVCaMlbhrqZFlbZloGjPRFNZaTK6FtYVMC8kSnrtWb15GZXK14rpuX170NDmfRCJtWJaDqAYp7qdE5B7yOrF9IAS7ljIp6Q95MBVNlRZ3cT2lsga6neZ1IK9JKfIyt1rZMsICYYzhFx+6VmUoyeuoVM44kBN3t8NWMqKU0SJjKDvBrPv6VskLf3fzefirN2yHvYoApxxy672FZMYydRfILVIqacsURO6xlFlXypaX3WWeQ1TUAir1nfUG3WpdSvkJ1fJ57tXgcdqxKuTBiyOlI3f5nR4cX8B8MlO0gKkUbmflRUyLzZbpl4LNOR9ljPWJxwcBDGnHDYvHyoq7zHOXE6RmYRzrD3Wgw4vdYphTmDbldthV4bCpaAqxVDbPTtAjd70UqVXEWS1yCfHEfBJzifSiJ1T19pUSbRn1js3JjBprgXPYbfjWbVeUfa8OnxND0zEl7lYZGroAbLCwZbo1T7lwvYAkrDoA8/uwmgjU93iNFtTtXt/lU52v12lXed6lottKhDxmbvRIxIzeQh4HvvD2i8FQLGKrOjzYNzpXsvP3Oe1gzAxOVOQuV6hKcQ+6cej0gsri0ieC9dTTnLhbWzLytYCc8FghO+mQx1mUw513blrkXmqf36VErRJ9QZZV5A6Y1/VconQGmL7QCTDz3AsDO9lByUWKVpE7YGaeHBw3xb1cKmRIpUIurXNb2+XF2HHzmrWyuNZ1+eB12nH/XrOkcanVvoUsly1TC1afhGWlJsbY7YyxXYyxXYbB4XHmIvdyG1wMaBN/VpG7zEqQ+cl54q5lpMhISg7LFwtjDF1+Fw6OmyvweiyGXtUixdsqxx0AAi7Tn5QTqtVkh5Qi7HViNm7aMiGPw1IwpO8sl9IXtcftQMjjwImpKOYTGUtxlzemtH+s3kdG4bFUNq+2f8DtQJffhY09fnzn3Vfg9ms3qVIJ3kVG7owxrA57cCoSw1w8jaDHibDPZWnpyM6t1PVhszEEtBWDgB6559sy8QLPvRD52Vllykhk5Fvue5eebaVV0nmR+xKi00psqkLcQ16nyiiyOjepC3Ihk1U+vOygZOdfLnKfjqZgY/lzLKXecymROwCsFanB9hI6Y7cxnLs6hKdFivZy2jKLbfk4Y2wAAMT/p8XjwwDWasetAWC5cSnn/E7O+Q7O+Q4O88vxVyHuq7Vha+EXnPPdsjg5bWYVrNOWw8sLR6ZS3n7tJnz2Dy8qGo7XSpffhafEptPlShVUQnY+pSJ36U+WK1FQLdJzH5qOlcxV7vGbxY/Wd/tKzj8MdvrUPpCWkbs3P33VylPVbZmFZEZFnGu7cu97zeaevKh2seIOmNaMactkitLOdGR0W862kyMRmfEjvzu5wK434MZsPK384nLi7nHasLHMpLAUx1LXB5Db5KHSCLKayH05CLgdqkJiKXFf0+nFgTEzo8QqmcBuYwi6HSoRIprMFq1xkWVw1QreEh2ybMPbr1iX1/EUIoOQpY5epFXZ6XOW1JnzBjtU2ZXqJ1SXIVumBD8FcIv4+RYA92iPv0tkzVwJYLaS3y7RV/eVu9jKRe6yU3jN5x7DX/73XjCW/2F5RJlamUp54Zow3nDh6mqaV5Yuv7mnqo0B2wdKRwOVkEJTTrRDHmfFjIlqCPtciKezODA2r+qpFGKzMQx0eMrWpB8Me/GSmAMpZ8vIBRplbRkxoSpHW4ULovTX9yzSlgHMG25o2pxQLZc+qcS9RL44kPsO5DnI6Ez+L60rOXIpNVfgcdrxiw9di3eWyRvP2TKlv/e+oBtOO6s49yOrFgLWnvtyIq+fUuK+rT+Yu6ZLfB9mfRmZ5269ktXjsFecRzt3dQf6Q258uML6leVIhQRM2wWwvjckcgThcRavEymF22lX+2CUoqI6MMa+B3PytIcxNgzgrwH8A4C7GWO3ATgJ4C3i8PsAvBbAYQAxALdW1VKIVEhxw5a72PQJp6JFTOLGOTEVg93GsKWvuA57yONU4r6YAl9WSM9+c19g0V6wbBtQXtz1G9uq5nu1yHMfnU2oGtRW/PvbLyl7wa3p9Kpt+sqJuxS3souYROS+fXUIRyeiRemXerZBYXGpWljf7cNsPI1UxlCVKa3Y0h8AY7CcKJbkInezPS/f0oMv/dGlqh6RFHlZ+7vciKPc9wBA1f4p16nbbAzbVgUt50h07DaGvqAbo7OJutoygCnusiSuFVu1jJzC7SUl0pcHTM/dqvNyO20qiFhdwt5686Vr8KaLByuO1mWgtdSOT9oy5UZ/F4j1FWs6S4+QC6nmO6smW+ZtJZ66weJYDuCOiu9qgVssy3Y7bOVtGRG5uxy2ol7Vo30R37rtcpy7ujiKDnkdGBIXwFK8dh05MXNeGQ+vGuQkTqkL3DzGbHNhtkCt6Lnb5UTlwrXhks8B+R6h1cS0XHJ+aNyM7suVH4ins4ilMlgV8uDv33R+USGmbi0XfCmdqMz8iaezZW2Zi9d14qn/fUNR+V2dwsjdYbfl1YuXry/tAo9r8WKhbJkKcy3ffc+VZWuiSFZ1eDA6W1xgbrm59ZoNuGBNR8koeKtWh79UYKNXhowmM0XlpwFzxJ81UnDaWcmOBEBV981g2Iu/ecN2vPrc6rbJLIXcX9YqDVKyqTcAn8tetd8OLJO4NwqPI+dZlrNl+oJmgSsrb07+ndPOcMm6TsshcMjjRCpj+vHLHblfsERxDyrhrhy5L8WSAfLPfWNv5cU/pdBn960id8YYNvcHsVvk+lqJu1qAIsoP+N0OvO3ydUXH6dUJF5sKCZg7CUnKZZ4AKCvs5t+Xj/BkZyw3iVnKXEHI44DXaa8YlFSzUhfI2U719NwBU7zK+durOzwIiD1iS13XIY9TVU2MWezlAOS+g/6QZ8nzaIwx/PE1G5f0GoCZTux22Mp2NnYbw0du3Jq3JqcS1XxnzSPuKpXMUbZXstsY+kWNiuLXMP/unIFQyZtfn2hZStqijlxdqC9fXwwyyisn3Msl7rpAlPLcq0FGG4yV/jy39gWUuMuFPzqMMXiddswlMqIgXImhudsBp50hneVLEklzotZMYVxMBU+dwsi9EHm9ye0Tl9IpMcbwpXdeqjZWXir9StxXtgoJYwxb+gN47mSk5HW9fXUI/7N/HL85NKkCgELkGoLVZdYKNBqbjeHOd+2oeI+9++WbanrdauyipqgtAxSIe4WGD4S9lheB/HIvXFPaSpDi6CgR/S+GG7f344M3bCn7vtVQaRETkBOLpWTKALnIvdPnXFInJyP3sLd0XvU2bdhdajLQ57KrXYtKnZtMOwWWZsu4HXYlANVUkiyH7KxKBhPifE/PmQXhljpB94qtvVUvdKnEZRu6sK0/uKQOZ7mQ9eFLfffve+VZ2NTrx0d/tBuz8bRlurDUjQELy2YlecXWXuW9LxfVdMhNJO5mU952+VrcfNFg2WPfdvk6/MFla4selzd8OZ9YDlnDvuIiV4ulN+jGR27cuqhSvzrbB0J4/QUDuGxD6Um+5bNlTJGsNIlXiW6Rwldu0nWLtmtPqRz+3qBbVTwstTjLfL9c7v1SkNZMtRZGKaT/Xao9ssOeT2aW3Obl5nUXDOBXH7627GKnRrFjQxeCbkfJiUeP045//cOLVO18q05SBnflVvm2C9XMkzSdLfOuqzZUPPbNl66xfPyCNWH8zRu24/UXDFg+D+Ssj+WyZJYTv9uBL7y9fK21aqL7qt7LZYfDxrCxp7QXWg2yEmG3RdEwiYzKAiWqXQKmvfPY4UkA5ecc5MTUUoVyfbcfjx+eWnLkLvP2PSVGmy6HzdxpqqBMMZHP718yiN85t7/siOyCNWH85H1X4yN3P49L1xcHQDJyt6pw2m60lOe+HL6f3VZ5EkRF7suUKdNoZOS+lNWpgCnKH331Nly6vnzJ1mr4wPVbSoobYBYgC3oclguYJKvDXqQylfP35QhhKVknQK6cwlI/x8JUSCs6vM6iYmdEPoyxqkZR5w124P4Pv8LyuTMpcm+tbJkGXfgyUluuTJlGk8uFX/rndfu1Zy35NQDg5ovL22iMMWzrD+btYF+IXpWylHUDmLaM3caqSvUrx/Vn9+PJo9NLtqUC7sriHvI6MDZXOronlgfluZcp4dAutFTk7qlzOpZETnBZ1XtuBWSkuVRbptH8+e9uUxUfrdCH0uXO7R1XrsPZA8Elz5ds7gvga3982ZJeA6gucpcdMkXu9UVGs1blq9uNarJlmkYhGhXV6BOqrYj03MtZHM2IXnrZCn0BRzlxP6s3ULYcQqMJVkiFBHKjxWbISmlnfC5zHcBS9lRoFVrKlrHanKMeqAnVFvXcK23o0aro0dZypag2gi19QVy2obNshUH5nS0lfZOozJ9csxGv3Na7bFlwzUxr2TKNjtyXUMN9JRkIezEY9uLsVYuvPtmM9AVNLz1r8JbquDp8TvzwT68ue4yM3MmWqS/run0V9zxuF1oqcl/qBFm1rOv24Q0Xri6qW9IqBNwOPP7x61e6GcuOw27DqpAHpyLxthNBGVCQLUMsFy3judsYa9hQyu2w4/Nvu7gh70XUxuqwB5FYasl1QZoNaQWSuBPLRTXBcFPkZp0BFhlRBWs7fUteVNSMULYMsdw47DY4KgRBzRG5W+7OR5xp/NmrtuKtojRuOyGLtHmXuPCKIHQq+e5NIe4UuRNA+06IqVTIBq3lIM4MKmUYNkUoYSN1J9oYZctQKiSxjFSK3JtC3Nf3tF+0RhCS3JZtJO7E8nHL1RvKPt8U4t6oNEiCWAnWdvrwges348Zz+le6KUQb8aevKF8bqik8d4JoZ2w2hv/1O9tWuhnEGQaFzARBEG0IiTtBEEQbQuJOEATRhpC4EwRBtCEk7gRBEG0IiTtBEEQbQuJOEATRhpC4EwRBtCGMc77SbQBjbALAiQa/bQ+AyQa/Zy00Y/uasU2FNHMbm7ltQHO3r5nbBqxc+9ZzznutnmgKcV8JGGO7OOc7VrodpWjG9jVjmwpp5jY2c9uA5m5fM7cNaM72kS1DEATRhpC4EwRBtCFnsrjfudINqEAztq8Z21RIM7exmdsGNHf7mrltQBO274z13AmCINqZMzlyJwiCaFtI3AmCINoRznlL/AOwFsDDAPYD2AvgQ+LxLgAPADgk/u8Uj58N4AkASQB/XvBaYQA/AnBAvN5VJd7z1QBeAnAYwMe1x98vHuMAepqwfd8Rjx8AMNokbfoqgN0A9ojXCTTb56Y9/3kAC83UNgDfAHAMwPPi30VN1j4G4FMADoq//6smattj2uc2AuC/m+yzuwHAs6J9vwGweVk0czlepBH/AAwAuET8HBQX0XYA/yQ/KAAfB/CP4uc+AJeJC67wy7gLwLvFzy4AYYv3swM4AmCTOGY3gO3iuYsBbABwHDlxb6b2vRbmzTYA4JcA3tsEbQppx31Ge/+m+dzE8zsAfAumuDdN22CK+5ub+J64FcA3AdjE7+c1S9sKjvsxgHc12Wd3EMA54uf3AfjGsmjmcrzISvwDcA+AG2H2hAPaxf5SwXF/o38ZAEIwIyBW4fWvAvAr7fdPAPhEwTHHIcS9GdsnHv8wgE81S5tgdjpfBPCxZvvcxA34sHi/hSZr2zdQIO5N1r6dKBNxNsm1FwQwAy3QaIb2/b/27i3EqiqO4/j3R4pUolFpakqmaUSQF7pABEkPFUNBUmIFIYFEgUX1UEQPvlT0FpVZkSRBIUKWSDcfjAiMJMl7UhkFTozmg+GFJpjx38N/nTpO48xEMrPOmd8HDnPOOmvv/Z91Zv5n77XXXrts84am8hcGWtdQHy3Z5y5pJrn3vA24JCK6AMrPyYMsPgs4AqyVtEPSGknn91PvUuBg0+vOUtYy8UkaCzwAfFZDTJLWAofIQ9xX+y5cQYwrgE2N7VYWG8DzknZLeknSuMrimw0slbRd0qeS5lQUW8NiYEtEHGsurCC+5cAnkjrJ/9cXB9nmkLRccpc0njy0erzvhzREY4CFwOsRsQA4SR5+/WtT/ZRFi8W3GvgS2FFDTBHxIDCN7JdcetqCI9xukqYBS+j/S6eGz/QZ8kvxOrJf+OnK4hsHdEdegv8W8HZFsTXcB6w7baE64nsC6IiI6cBastvyf2up5F72RDcA70XEB6X4sKSp5f2pwG+DrKYT6IyIbeX1+8BCSTMk7SyPh0u9GU3LTSdPxrREfJJWApOAp2qJCSAieoH1wN1NsdbQbguAK4ADkn4BzpN0oJLYiIiuSH+SCeD6itqusY4N5fmHwDUVxYaki8g2+7ipbMTjkzQJmNe0/HrgxkG2OSRjzsZKhoMkkSMu9kdE8zfbJmAZeSizjOw7O6OIOCTpoKQrI+J78kz1dxFxkByB0NjeGGCOpMuBX4F7gftbIT5Jy4HbyrJrRjqm0jazI+JAeX4nObKgmnaLiH3AlKZ6J4A55MmyGj7TqRHRVdrrLmBvLW1X3t4I3ELusd9MniSsJTbIo7KPIqK71K+l7Y4CEyXNjYgfyH7//QNtc8jORsf9cDyAm8jDmN38M6ypA7gI2EIOXdoCXFjqTyG/LY8Bv5fnE8p784HtZV0bKcOd+tlmB/lH+hPwbFP5Y2V9PeTewZrK4uspZT+WmA6NZEzkEeJWYA+wlxyqOaG2z7VPnRM1xQZ83tR+7wLjK4vvAnKveA85ZHBZLbGV974Abq80nywu7barxDnrbORMTz9gZtaGWqrP3czMhsbJ3cysDTm5m5m1ISd3M7M25ORuZtaGnNxtVJLUWy4w2Sdpl6QnJQ34/yBppqQzXutgVhMndxut/oiI+RFxNXnhSAewcpBlZjLAhWxmNfE4dxuVJJ2IiPFNr2cB3wAXA5eR0/42JoBaERFfSfoauIqcBfAd4BXySsZF5Nwqr0XEm8P2S5gNwMndRqW+yb2UHSUn5zoOnIqIbuXshusi4lpJi8jpXu8o9R8CJkfEc8pZGrcCSyLi52H9Zcz60TJzy5gNg8bMfWOBVZLmA73A3DPUv5WcIOue8noiOR+Nk7uNOCd3M/7uluklZwFcCRwG5pHnpbrPtBjwaERsHpYgzf4Dn1C1Ua9Mu/oGsCqyn3Ii0BURp8ibJ5xTqh4n7+bTsBl4pEwdi6S56v9GDWbDznvuNlqdK2kn2QXTQ55AbUz9uhrYIGkJedu9k6V8N9AjaRd527uXyRE035YpZI+Q0/GajTifUDUza0PuljEza0NO7mZmbcjJ3cysDTm5m5m1ISd3M7M25ORuZtaGnNzNzNrQX7950XdF3KVLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='Traffic'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('EMS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XgPE2x3eh0dSRtB8C4FygH7oq1F1GiM80TXzu2wdcAzOdlFBrWAocSmD6Fura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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13030f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": 241,
   "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>DayofWeek</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",
       "DayofWeek                                                    ...             \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",
       "DayofWeek                                            \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": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new=df.groupby(by=['DayofWeek','hour']).count()['Reason'] #collates the info in these two columns\n",
    "new.head()\n",
    "#now unstack this so that hour becomes a column label instead of a nested row label\n",
    "new=df.groupby(by=['DayofWeek','hour']).count()['Reason'].unstack()\n",
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ca8c7f0a58>"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(new,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1253fa198>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lSZI6wcrjhPYC9ga+BTw3MwP4X8DyQu1JkiR1w1iv/2MWFUkeM3NNZq4G9sjM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\nzu4YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b4bc940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a clustermap using this DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1ca8c8afcc0>"
      ]
     },
     "execution_count": 237,
     "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(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1304fb668>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SVJBhS5IkqSDDliRJUkGGLUmSpIIMW5IkSQUZtiRJkgoybEmSJBVk2JIkSSpo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\nkGFLkiSpoP8PGwocjpNs7BAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1304fb320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": 243,
   "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>DayofWeek</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",
       "DayofWeek                                                      \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": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2=df.groupby(by=['DayofWeek','month']).count()['Reason'].unstack() #collates the info in these two columns\n",
    "new2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ca8c9fba58>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(new2,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1304fbd30>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D6RxKSZIkNWKHUpIkqSCHvCVJktTMAIe8x0YD3FxTkiRJ5TiHUpIkSY1YUEqS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a1a2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1ca8d80a940>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.clustermap(new2,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x12a1a61d0>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AgN3k0939yUUuKLYAgN3kmqp6R5IPJ1lJku4+b+SCYgsA2E1+f9EL+m5EAGA3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\nYgsAYCCxBQAw0P8Hv0ppOs88huMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a1a63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Continue exploring the Data however you see fit!**\n",
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}