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

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

# data[].attributes.attachment_link.readable_text
{
 "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": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the csv file as a dataframe called df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 zipcodes for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 townships (twp) for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Take a look at the 'title' column, how many unique title codes are there? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['title'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** In the titles column there are \"Reasons/Departments\" specified before the title code. These are EMS, Fire, and Traffic. Use .apply() with a custom lambda expression to create a new column called \"Reason\" that contains this string value.** \n",
    "\n",
    "**For example, if the title column value is EMS: BACK PAINS/INJURY , the Reason column value would be EMS. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Reason'] = df['title'].apply(lambda title: title.split(\":\")[0])"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Reason', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Reason', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "** Now let us begin to focus on time information. What is the data type of the objects in the timeStamp column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   lat        99492 non-null  float64\n",
      " 1   lng        99492 non-null  float64\n",
      " 2   desc       99492 non-null  object \n",
      " 3   zip        86637 non-null  float64\n",
      " 4   title      99492 non-null  object \n",
      " 5   timeStamp  99492 non-null  object \n",
      " 6   twp        99449 non-null  object \n",
      " 7   addr       98973 non-null  object \n",
      " 8   e          99492 non-null  int64  \n",
      " 9   Reason     99492 non-null  object \n",
      "dtypes: float64(3), int64(1), object(6)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2015-12-10 17:40:00'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timeStamp'].iloc[0]"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])\n",
    "type(df['timeStamp'].iloc[0])"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)\n",
    "df['Month'] = df['timeStamp'].apply(lambda time: time.month)\n",
    "df['Day of Week'] = df['timeStamp'].apply(lambda time: time.dayofweek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month  Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12            3  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12            3  \n",
       "2                    HAWS AVE  1   Fire    17     12            3  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12            3  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12            3  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day of Week'] = df['Day of Week'].map(dmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12         Thu  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12         Thu  \n",
       "2                    HAWS AVE  1   Fire    17     12         Thu  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12         Thu  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12         Thu  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f715668fd60>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Day of Week', data=df, hue='Reason')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f71565e6100>"
      ]
     },
     "execution_count": 21,
     "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')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>11527</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13203</td>\n",
       "      <td>13096</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>9930</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11465</td>\n",
       "      <td>11396</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>9755</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11092</td>\n",
       "      <td>11059</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>9895</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11323</td>\n",
       "      <td>11283</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>9946</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11420</td>\n",
       "      <td>11378</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "Month                                                                      \n",
       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "\n",
       "       Reason   Hour  Day of Week  \n",
       "Month                              \n",
       "1       13205  13205        13205  \n",
       "2       11467  11467        11467  \n",
       "3       11101  11101        11101  \n",
       "4       11326  11326        11326  \n",
       "5       11423  11423        11423  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth = df.groupby('Month').count()\n",
    "byMonth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a simple plot off of the dataframe indicating the count of calls per month. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "byMonth['lat'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now see if you can use seaborn's lmplot() to create a linear fit on the number of calls per month. Keep in mind you may need to reset the index to a column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f71561f3100>"
      ]
     },
     "execution_count": 24,
     "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)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f715617dca0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAA62klEQVR4nO3deXxU9b3/8ddnlsxM9oUEEhbZdxQIUlstl2pVtFbULqIF7W2vYNWr1l6r1lZbra22/bVqrQq2XiuoYF0qt+6tWtpaQMK+RIGwBYEA2cgyySzf3x/nRCMC2XPOJJ/n45FHJt/MTD5Dkjcnn/P9fo8YY1BKKdX9PE4XoJRSvZUGsFJKOUQDWCmlHKIBrJRSDtEAVkoph/icLqC7zZgxw7z22mtOl6GU6l3kWIO97gj40KFDTpeglFJALwxgpZRyCw1gpZRyiAawUko5RANYKaUcogGslFIO0QBWSimHaAArpZRDNICVUsohGsBKKeUQDWCllHKIBrBSSjlEA1gppRyiAdyCxmicWFyvm6eU6nwawC2IxOJ8WFlPNBZ3uhSlVA+jAdwKVgiHaYjGnC5FKdWDaAC3UjQeZ3+VhrBSqvNoALdBLG7YVxkmHNEQVkp1nAZwG8WNYX9VmPpGDWGlVMdoALdD3Bj2V4epaYg6XYpSKoFpALeTMYay6jCVdY1Ol6KUSlAawB1UXtvIoZoGp8tQSiUgDeBOUF0f4UB1GGN0wYZSqvU0gDtJbUOU/RrCSqk28DldgJu9U1zGw+9sZ1d5LfnpIWadOpCpQ7OPe//6xhj7q8P0Sw8iIt1YqVIqEekR8HG8U1zGHUs3cbAmTHrQx+HaBh54aysrS8pP+LimENYjYaVUSzSAj2P+shL8XiHk9yEIIb8Xn0dY/N6eFh+rIayUag0N4OPYU1FHyO/9xFjQ72F/dX2rHq8hrJRqiQbwcQzMSqb+qCXH4UicfumhVj+HhrBS6kQ0gI9j3rShRGKG+kgUg6E+EiMaN8w6dWCbnkdDWCl1PBrAxzF9dB53XTiO3NQgR8JRclIC3HDmiBPOgjgeDWGl1LHoNLQTmD46j1OHZHOgOtzh56pvjPFhlTVFzevRKWpKKT0C7lYNkRgfVtbTGNWrayilNIC7XSQWZ1+VhrBSqgsDWEQeF5EyEdnYbOxuEVkvImtF5A0RKbDHRUQeFJFt9ucnN3vMlSKy1X67stl4oYhssB/zoCTQ0rNY3HCgOqzXmVOql+vKI+AngBlHjf3SGHOyMWYi8BfgDnv8PGCE/TYXeARARLKBO4HPAFOBO0Uky37MI8BVzR539NdyNetIOKxXXFaqF+uyADbGLAPKjxqrbvZhCtCUPjOBJ41lOZApIvnAucCbxphyY0wF8CYww/5cujFmubGmFjwJXNRVr6WrRGJx9leHiWsIK9UrdfssCBG5B7gCqAK+YA/3B5qv8S21x040XnqM8eN9zblYR9YMGjSoYy+gkzVErClq+Rm6gY9SvU23n4QzxtxujBkIPAVc101fc4ExZooxZkpubm53fMk2CUd0nrBSvZGTsyCeAr5i394LNF9iNsAeO9H4gGOMJyxdrKFU79OtASwiI5p9OBMotm8vBa6wZ0OcBlQZY/YBrwPniEiWffLtHOB1+3PVInKaPfvhCuCl7nslXUNDWKnepct6wCLyDDAd6CMipVizGc4XkVFAHNgFXG3f/RXgfGAbUAf8J4AxplxE7gbes+93lzGm6cTeNVgzLULAq/ZbwmsK4bw0XTGnVE8nve1oa8qUKWbVqlWtvn9tQ7RTliK3ld/rIS89QMDnbfnOSim3O+bRlK6Ec6lILM6+yjC1DVGnS1FKdRENYBeLG2vFXFVdxOlSlFJdQAM4ARyubeBQTYPTZSilOpkGcIKoro9wQGdIKNWjaAAnkNqGKPuqdOmyUj2FBnCCCUdi7K2spyEaa/nOSilX0wBuwdo9la77sz8Si/NhZZjqsJ6cUyqRaQCfwD+3HmL2H1Zwx0ubqK53V9gZYzh0pIGyI9qSUCpRaQCfwN8/KMMY+Nf2w8xdWMTmD6tbflA3qwlHtSWhVILSAD6B2780lnsuHk/Q56HsSAM3LFnLs6v2aEtCKdUpNIBbcNHE/jw8ezKDc5KJxQ2P/r2E2/+8kSq3tiR0g3elEoYGcCsMzknh4W9M5rzx/QBYXlLOvIVFbNxb5XBln1bTYLUkwhFtSSjldhrArRT0e7n53FHcet7oj1oSNy5Zy+KVu4m7siVRz6GaBte1S5RSH9MAbqNzxvbl0dmFDOmTQtzAgn/s4AcvbnTlfg3V9RE+rNKrLyvlVhrA7TAoJ5nfXT6J8ydYLYmVO8q5auEqNpS6ryXREInxYWVYZ0ko5UIawO0U9Hv5n3NGcfv5own5vRyqaeS7z67l6RXua0lE4zpLQik30gDuoLPG9OXR2ZMZlmu1JH7/zx384IUNVNY1Ol3aJ+jCDaXcRwO4EwzMTuahyybx5ZPzAVi5s4KrFhaxrrTS2cKOoWnhRmNU+8JKOU0DuJME/F6+e/ZIfvilMSQneTlc08j3nl3HouW7XNeSiMTi7K2s54i2JJRylAZwJztzdB6Pzp7M8NxU4gYe/9dObnl+A+W17mtJHDzSwMEjOlVNKadoAHeBAVnJPHT5JGaeUgBA0a4K5i4sYs3uCocr+7Qj4Qj7qsLEtC+sVLfTAO4iST4PN3xxBHdcMJaUJC/ltY3c/Nx6nvz3TteFXTgSY29FPXWNegFQpbqTBnAXmz4ql0fnFDKyr9WSeOLdXXz/+fWua0lE43H2V4U5eKRBZ0ko1U00gLtB/8wQD86axMWT+gOwZnclVz25itW73NmS0O0tleoeGsDdJMnn4b/PHM6PLxxLSsBLRV2Em59bzxP/cl9LQre3VKp7aAB3s2kjcpk/u5BR/dIwwJPLd3Hzc+s47LLLzuvCDaW6ngawAwoyQzw4ayKXTLZaEmv3VDF3YRGrdpY7XNmn6RU3lOo6GsAO8Xs9XPeF4dx14ThSAz4q6iLc8vwG/vDPHdqSUKqX0AB22Bkj+rBgTiFj8q2WxFMrdvO9P63j4BFtSSjV02kAu0C/jCD3XzqRrxUOAGB9qdWSWLlDWxJK9WQawC7h93r4zvRh/PSicaQFfVTVR7j1hQ089o8SbUko1UNJb9sHYMqUKWbVqlWtvn9tQ5QD1eEurOjTDlSHufsvm9m87wgA4wvS+eGXxpCXHuzWOlojNeijT0oAj0ecLsUV3ikuY/6yEvZU1DEwK5l504YyfXSe02Up5x3zF0SPgF2ob7rVkrh0itWS2PhhNXMXFrG85LDDlX2atiQ+9k5xGXcs3UTZkTCZIT9lR8LcsXQT7xSXOV2acikNYJfyeT3M+49h/Ozi8aQHfVSHo/zgxY3M//t2113jTVsSlvnLSvB7heQkHyLWe79XmL+sxOnSlEtpALvcaUNzWDCnkPEF6QAsWVXKjUvWdXtbpCUfzZKo7r2zJPZU1BHyez8xFvJ7Ka2oc6gi5XYawAkgLz3Ir79+CpdNHQjA5n3VzFtYxLvbDzlc2afVNPTelsTArGTqI5983fWRGAOykh2qSLmdBnCC8Hk9XPX5odx7yQQyQn6qw1F++OdNPPLOdiLaknCFedOGEokZ6hqjGGO9j8QM86YNdbo01QbGGBqiMWoaolTUNlJWHebDynr2lNd1+pYBXRbAIvK4iJSJyMZmY78UkWIRWS8iL4pIZrPP3SYi20TkfRE5t9n4DHtsm4jc2mx8iIissMeXiEhSV70WN5k6JJsFcwqZ0D8DgD8VlXLjkrXs15aE46aPzuOuC8eRlxakqj5CXlqQuy4cp7MgXCoWN4QjMarDEQ7XNLC/Ksye8jp2HKplb0U9ZdVhKuoaqWmIEo7EiMTidPaPcpdNQxORaUAN8KQxZrw9dg7wljEmKiL3ARhjbhGRscAzwFSgAPgrMNJ+qg+As4FS4D3gMmPMZhF5FnjBGLNYRB4F1hljHmmprkSYhtYasbjhiXd38vSK3RggNeDjlhmjOH14H6dL+xS/10NeeoCAz9vynZXqZJFY3HqLGhqbbsfi7Zpfnxb0k5sWaE8Z3TsNzRizDCg/auwNY0zTZReWAwPs2zOBxcaYBmPMDmAbVhhPBbYZY0qMMY3AYmCmiAhwJvCc/fg/Ahd11WtxI69H+PYZQ7j3KxPIDPmpaYjyo5c28dDb27QloXqdY7UNSiuso9k95XXsrwpzuLaBI+EI4UjMNYubnOwBfwt41b7dH9jT7HOl9tjxxnOAymZh3jR+TCIyV0RWiciqgwcPdlL57nDq4GwWXFHIxIFWS+KF1Xu5fvFa9lXVO1zZJ/XGloTqfG1pGzRG466/4KwjASwitwNR4Knu+HrGmAXGmCnGmCm5ubnd8SW7VZ/UAL/86ilccdpJCPD+/iPMXVjEsq3u+8+mN8+SUK0XjcWpb4xRVR/hUE0D+6rq2XW4ll2Ha/mwsp5DRxqoqo/YJzrd9RdfW/i6+wuKyDeBC4CzzMf/Pe0FBja72wB7jOOMHwYyRcRnHwU3v3+v5PUI3zx9MBMGZPCzV7ZQURfhx0s3c/Gk/sybNpQkn3smvDS1JLJTksgI+Z0uRzmoeX+2IRYjEjNEonHiLj9y7Szd+lspIjOA7wMXGmOaz05fCswSkYCIDAFGACuxTrqNsGc8JAGzgKV2cL8NfNV+/JXAS931Otys8KQsHrtiCpMGZQLw4pq9XL94DXsr3deSOFzTwIeV9Ql9BKNaZoyhMRqntiFKZd3x+7M14SgNkVivCV/o2lkQzwDTgT7AAeBO4DYggHUEC7DcGHO1ff/bsfrCUeBGY8yr9vj5wP2AF3jcGHOPPT4U66RcNrAGmG2MaXGSXk+ZBdGSWNzw1IpdPPnvXcQNpCR5+d45o5g+yn0tGBEhI+QnM+TXTX0SmDFNswyswI3E4jRG40TjxvW92Nbq7FkQuhtaCxI1gJus2V3BPa8UU17bCMDMUwr4zvRhrmpJNPGIkJnsJyPkx5rootwoHv94OpcVtOajVkJPpwHcQb0tgAHKaxv5+avFFO2qAGB4Xip3XjCW/lkhhys7toDfS25qwJX/SbhZLG4deTZ+FIwfh6JHBBHrvfUGHs/Ht8X+PIAx1tFs3EDcGOJx63Y0HicaM72qRXA0DeAO6o0BDNYv0tMrdvPEuzuJG0hO8vK9s0fyBZeu0hIRMkPW0bC2JT4pFreOOBtjcSLNAtctc1t7ss4O4G6fBaGc4RFh9mknMaF/Bj99ZQuHaxq5++UtrC2t5Nrpw113tGmMoaKukar6CBkhP2lBHz5v59TYVZumG2OI2EeITUeJgnVkmeT1fPQfSbSpT2ofoTbNiz5WfBoDBvtoNG56VD9V6RFwi3rKEXBzlXVWS+K9nVZLYlhuCndcMJaB2e7etSuU5CU14CMlydfuo+KmTdP9XiHk91IfsaY+tXbPhqYTTU1HnNG4IdrKHqjXI8TtP+9VYtIWRAdpAFvixrB45R4e/9cO4sbat/ams0dy1hh3tiSaExFSAl5Cfi9+rwe/14O3lYF82YLlHKiuJznp4z/+6hqj5KYF+eO3piICXpGPwjIaj9urr+LUR2IJsbpKdR1tQahO4RHh8s8MYkL/DO5+eTOHahq555UtrN1TyXVfGEbA796Nc4wx1ISj1ISjH40F/F5Sk3wEkzx47RNNBqtf2rR8tS4So+RQDelBH43Rj49WvR75aIWVUt1JA7iXmzAgg8fmTOHe14pZsaOclzfsY8u+au64YCyDctzdkmiuIRKjIRKD2hPfLz89xOHahk9cuSIcidMv3Z0zQlTP5q4zL8oRGcl+7rl4PHM/PwSPQMmhWq5+qog3Nh9wurRON+vUgUTjhvpIDIP1Pho3zDp1YMsPVqqTaQArwGpJzJo6iPsvnUheWoBwJM69rxbzi9feJxzpORvnTB2azQ1njiAnJcCRcJSclAA3nDmCqUOznS5N9UJ6Eq4FPfUk3IlU1Ue477VilpdY2zkPzknmji+PZXBOisOVKeWshNmQXSWujJCfey4az9X/MRSvR9h5uI5rFq3m9U37nS5NqR5FA1gdk4jw9SkDeaCpJRGNc99r73Pfa8WfuvKvUqp9NIDVCY0tSGfBnEI+NywHgNc3HeCaRavZcaiF6QZKqRZpAKsWpYf83D1zHN+ZPsyaM1texzVPreaVDft0UYJSHaABrFpFRPha4QAenDWRfulBGqJxfvXGB/z81WLqG7UloVR7aACrNhmTn878OZM5Y3gfAP66pYyrFxVRcrDG4cqUSjw6Da0FXTUNbWVJOYvf28O+6nry00PMOnVgQs1FNcbw4pq9PPr3EqJxQ5LPw3VfGM6XJvTTzdRVj6XT0HqAlSXlPPDWVg7XNpAe9HG4toEH3trKSnvebSIQES6ZPIDfXjaJ/IwgjdE4v37zA372SjF1jdGWn0AppQHshMXv7cHnsbZDFKz3Po+w+L09TpfWZqP6pTF/diHTRlgtib8Vl3H1otVsL2t/S2JlSTk3LVnHZY8t56Yl6xLqPyal2kIDuAU+r5AW9JMa9JES8BH0e0nytX77w2PZV11P0P/Jf/qg38P+6sTcjSs16OPOL4/l+jOH4/cKpRX1XPP0apau+7DNsyR6wl8HSrWW7obWgoDPS27asbdmNMbakPvjjbnjH30cicU/2grxaD1xRy4R4aJJ/RlbkM5df9nMh5Vh7v/rVtbtqeSms0eSEmjdj1rzvw6AjzZNX/zenoTqkSvVGnoE3AEigt/rIei3rtSQmZxEn9QAfdODDMhK5qScFIb0SWFgdjIFmSHy0oNkpyTxrdMHEzOGcDSGgR61I9fIvlZLYvrIXADefv8gVy9azdYDR1r1+J7214FSJ6IB3MWOFdIXTurPzy6aQP/MZMKRGP0zQ/zky+P40in5ZCUnkRrwEfB78STobIKUgI8fXTCGG784Ar9X2FtZz3XPrOGltXtbbEnkp4cIRz55aZ9E/+tAqePRaWgu19TWaIzFP3HtMevS4RAzzlyksbXT6LaV1XDXXzZTWmEdwU4b2Yf/OWcUqcdpSTT1gH0eIej3EI5Yr1+3jFRuoNeE66BEC+DWaOo5N0TjhCMxwpFYl16ivK0hWdcY5ddvbuWt4jIA8jOC3PnlsYzsm3bc51/83h72V9fTLwHnSKueSwO4g3piAB/NGMORhihVdZEWr9TbHjctWfepk4j1kRg5KQF+fekpx63p5Q37eejtbTRG4/i9wtX/MYyLJhbowg2VMHQhhmqRiJAe9DMwO5m+6UH83s79NrfnRJmIcMHJ+fzu8kkMzAoRiRl++9Y2fvx/mz9xcU2lehMN4B4uJeBjYHYyuWkBUgK+Tjmx15ETZcNyU3l0diFfHJMHwD+2HmLeoiKK91d3uC6lEo0GcC+RFvTTNz3ISTnJ5KUH8Xna/63v6IUtQ0lebjtvNDefM5KAz8O+qjDXP7OW54pKdXtL1atoAPcyIkJqwMeArBAZIX+7nqMzLmwpIpw3IZ+HvzGZk7KTicYND7+znR+9tInq+ki76lIq0ehJuF6uvjHGwSMNROOdf7Ku1TVEYjz4t628vukAAHlpAe64YCxjC9Idq0mpY9GTcKpThZK89M8KHXdebrfU4Pdyy4zR3DJjFEGfh7IjDdywZC1/WrVHWxKqR9MAVng9Ql56kILMEEk+534kzh3Xj4dnT+aknGRiccMjfy/h9j9vpEpbEqqH0gBWHwn6vQzISqZPWqBDu711xOCcFB75xmTOG98PgOUl5cxbWMTGvVWO1KNUV9IecA/1TnEZ85eVsKeijoFZycybNpTpo/Na/fhY3FBe28iRsHNHn29sPsD9b35AOBrHI/Bfnx/K16cMSNg9MlTi0x6watE7xWXcsXQTZUfCZIb8lB0Jc8fSTbxjLwVuDa9HyE2zdnZzKvDOGduXR2cXMqRPCnEDC5aVcPuLG6mq05aE6hk0gHug+ctK8HuF5CQfItZ7v1eYv6ykzc+VEvDRPytEwH/sPZG72qCcZB6+fBJfmpAPwIod5Vy1cBUbSrUloRKfBnAPtKei7hP7NIA106C0oq5dz+f3eijICJKVnOTIvg0Bv5fvnTOS288fTcjv5VBNI999di1Pr9hNvJe10FTP0mUBLCKPi0iZiGxsNvY1EdkkInERmXLU/W8TkW0i8r6InNtsfIY9tk1Ebm02PkREVtjjS0QkqateS6IZmJVMfST2ibH6SIwBWcntfk4RISsliYLMzt9borXOGtOXR2dPZliu1ZL4/T93cNsLG6isa3SkHqU6qit/k54AZhw1thG4BFjWfFBExgKzgHH2Yx4WEa+IeIHfAecBY4HL7PsC3Af8xhgzHKgAvt1FryPhzJs2lEjMUNcYxRjrfSRmmDdtaIefO+DzMiArRFqwfavoOmpgdjIPXTaJL59stSTe21nBVQuLWLen0pF6lOqILgtgY8wyoPyosS3GmPePcfeZwGJjTIMxZgewDZhqv20zxpQYYxqBxcBMsf4OPhN4zn78H4GLuuaVJJ7po/O468Jx5KUFqaqPkJcW5K4Lx7VpFsSJiFgn6PLSg45MVwv4vXz37JH88EtjSE7ycrimke/9aR0Ll+/q0n2QlepsbrkoZ39gebOPS+0xgD1HjX8GyAEqjTHRY9z/U0RkLjAXYNCgQZ1UsrtNH53XaYF7PKkB30cr18JHtTy6w5mj8xjZN5W7/m8L2w7W8L//2sn6PZXcdv4YslO0I6Xcr1echDPGLDDGTDHGTMnNzXW6nB7F5/VQkBkiK9mZwBuQlcxDl09i5ikFABTtrmTuwiLW7K5wpB6l2sItAbwXaL6X4QB77Hjjh4FMEfEdNa4ckpWSRH5GqEPbXLZXks/DDV8cwR0XjCUlyUt5bSM3P7eeJ/+9U1sSytXcEsBLgVkiEhCRIcAIYCXwHjDCnvGQhHWibqmxlu+9DXzVfvyVwEsO1K2aadrYJ5TkzJzh6aNyeXROISP7phI38MS7u/j+8+spr9VZEsqdunIa2jPAv4FRIlIqIt8WkYtFpBT4LPCyiLwOYIzZBDwLbAZeA641xsTsHu91wOvAFuBZ+74AtwA3icg2rJ7wH7rqtajW83qE/IyQYz3Y/pkhHpw1iYsnWacE1uyu5KonV7F6l7YklPvoXhCqy4QjMcqqndtreNnWg/zy9fepbYghwJzTTmLOZ09ybKMhlfh0LwiVMIJ+Z1sS00bksmBOIaP6pWGAJ5fv4ubn1nGopsGRepQ6mgaw6lJNLYlMh2ZJ5GeEeHDWRL4y2WpJrN1Txdwni3hvZ3kLj1Sq62kAq26RnZJEXnrQkb0k/F4P135hOHddOI7UgI/K+gi3Pr+BP/xzh86SUI7SAFbdJjXgIz+jY1dk7ogzRvRhwZxCxuRbLYmnVuzmpmfXcfCItiSUMzSAVbcK+r0UZAYd296yX0aQ+y+dyNcKBwCwYW8VcxcWsXKHtiRU99MAVt3OZ29v6dSFQP1eD9+ZPoyfXjSOtKCPqvoIt76wgcf+UaItCdWtNICVI0SsC4E6uWfD54ZZLYmx+ekAPLNyD99dspay6rBjNaneRQNYOSozOYl+Gc5d9qhvepD7Lz2FWadaK943fljN3IVFLC857Eg9qnfRAFaOS07yUZAZcmyjd5/Xw9xpQ/nZxeNJD/qoDkf5wYsbmf/37URjziwiUb2DBrByhSSftauaU4s2AE4bmsNjV0xhfIHVkliyqpQbl6zjgLYkVBfRAFau4fUI/dKDpIecudoGQG5agN9cOpHLplotic37rJbEu9sPOVaT6rlaDGARCYrITSLygog8LyLfFZFgdxSneh8RoU9qgD5pAUcWbYD1H8FVnx/KvZdMICPk50g4yg//vIlH3tlORFsSqhO15gj4Saxrtf0WeAjr2mwLu7IopdKDfvo5dMmjJlOHZLNgTiET+mcA8KeiUm5cspb9VdqSUJ2jxd3QRGSzMWZsS2OJQndDSyyRWJz9VWFHjzxjccMT7+7k6RW7MVgr+m6ZMYrTh/dxrCblDCd2Q1stIqd99CwinwE0wVS38Hs99M8MkZzk3OULvR7h22cM4b6vTCAz5KemIcqPXtrEQ29v05aE6pDWBHAh8K6I7BSRnVibrJ8qIhtEZH2XVqcU4PEI/TKCju2o1mTK4Gweu6KQiQOtlsQLq/dy/eK17Kuqd7Qulbha04I46USfN8bs6tSKupi2IBJbTUOUQ0caiDt4IYFY3LDw37tYuHwXBkgJeLn53FFMG6EXfO3pnGhB/BcwEjhkjNl19Ft7KlGqvVIDPvIzg44t2gCrJfHN0wfzi6+eTFayn9qGGD9eupnfvrWNxqi2JFTrteanuAS4DFglIitF5P+JyMwurkup4wr4vBQ43BcGKDwpi8eumMKkQZkAvLhmL9cvXsPeSm1JqNZp9TXhRKQf8HXgf4AsY0xaVxbWVbQF0bNU1DZSUefsVY9jccNTK3bx5L93ETeQkuTle+eMYvoobUn0NN3eghCR34vIu8AjgA/rUvBZ7alAqc6WlWJt5uPkfGGvR7jis4P55VdPJjslidrGGHf9ZTMP/HWrtiTUCbWmBZEDeIFKoByrFxztyqKUaoumzXyc2uS9yaRBWSyYU0ih3ZJ4ad2HXPfMGkor6hytS7lXiwFsjLnYGPMZ4BdAJvC2iJR2dWFKtYXf3uQ9LejcPhJgXfvu3q+czLdOH4xHYFtZDVcvWs3bxWWO1qXcqcWzGCJyAfB5YBpWAL8F/KNry1Kq7USE3LQAfq9QXutcX9jrEWafdhIT+mfw01e2cLimkbtf3sLaPZVcM32Y40fqyj1a04K4BFgNfMUYM8YY85/AqK4tS6n2y0xOoq9DV2Bu7pSBmTw2p5BTB1unTP5v/T6ue2YNe8q1JaEsrQngicaYJcaYD5uNnddVBSnVGVICPgoynbsCc5PM5CR+fskE/uuMIXgEth+s5epFq/nbFm1JqBMEsIh8R0Q2AKNEZH2ztx2ALkFWrhfweemfFSLo8J/8HhEu/8wgfvP1ifRJTaI+EuOeV7bw/974gIZIzNHalLOOOw9YRDKwppv9HLi12aeOGGMS9hreOg+49zHGcLi2ker6iNOlUFUX4d7Xilmxw/oVGtonhTsuGMugnGSHK1Ot0dnzgFu9EKOn0ADuvY6EIxyqacTpn/m4MTy7qpTf/6OEuIGg38ONXxzJOWP7OlqXapkTe0Eo1SOkBf0UOLyPBFgtiVmnDuT+SyeSlxYgHIlz76vF/OK19wlrS6JX0QBWvYpb9pEAGN8/g/lzCjltaDYAr23azzVPrWbn4VqHK1PdRQNY9Tpel+wvDJAR8vPTi8Yzb9pQvB5h5+E6rlm0mtc37Xe6NNUNNIBVr5WdkkSeC+YLe0S49NSBPNDUkojGue+197nvtWLqtSXRo2kAq14tNeAjP8P5+cIAYwvSWTCnkM8NywHg9U0HuGbRanYc0pZET+X8T51SDgv6vRRkBl2xRDg95OfumeO4ZvowfB5hV3kd1zy1mlc27HN89obqfBrASgE+ezOf1KDzJ+dEhK8WDuCBWRPplx6kIRrnV298wM9fLaa+UVsSPYkGsFI2ESEvLUhOasDxvjDAmHyrJXHG8D4A/HVLGVcvKmL7wRqHK1OdpcsCWEQeF5EyEdnYbCxbRN4Uka32+yx7XETkQRHZZi93ntzsMVfa998qIlc2Gy+0r8y8zX6s878xqkfICPld0xdODfr4yYVjue4LVktiT0U91z69hr+s15ZET9CVP2FPADOOGrsV+JsxZgTwNz5e4nweMMJ+m4t19Q1EJBu4E/gMMBW4sym07ftc1exxR38tpdot6Lf2kUgNuKMlccnkAfz2sknkZwRpjMb59ZsfcM8rxdQ16rURElmXBbAxZhnWFTSamwn80b79R+CiZuNPGstyIFNE8oFzgTeNMeXGmArgTWCG/bl0Y8xyYx0GPNnsuZTqFF6PkJcepE+aO1oSo/qlMX92IdNGWC2Jt4rLuHrRaraVaUsiUXX331h9jTH77Nv7gabF7/2BPc3uV2qPnWi89BjjSnW69KCffulBPC4I4dSgjzu/PJbrzxyO3yuUVtRz7dOrWbruQ21JJCDHmlz2kWu3/MSIyFwRWSUiqw4ePNgdX1L1MKEkawmz0/tIgNWSuGhSf3572SQKMoNEYob7/7qVn768hdoGbUkkku7+aTpgtw+w3zftSr0XGNjsfgPssRONDzjG+DEZYxYYY6YYY6bk5uqlwlX7JPk89M8MkeKCvjDAyL5WS2L6SOtn+u33D3L1otVsPXDE4cpUa3V3AC8FmmYyXAm81Gz8Cns2xGlAld2qeB04R0Sy7JNv5wCv25+rFpHT7NkPVzR7LqW6jMcj9E0Pkp3i/D4SYF3540cXjOHGL47A7xX2VtZz3TNr+POavdqSSABdOQ3tGeDfWFfUKBWRbwP3AmeLyFbgi/bHAK8AJcA24DHgGgB74/e7gffst7uabQZ/DfB7+zHbgVe76rUodbSm6865oS8sIlx4SgG/u3wyA7JCRGKGB9/axk/+spkabUm4mm7IrlQHNERjHKhqIBqPO10KAHWNUX795lbeKra6e/kZQe64YCyj+qU5XFnPoBuyK+Ui1v7C7thHAiA5ycft54/mprNHkuTzsK8qzH8/s4YXVpdqS8KFNICV6qCP9pFwyck5EeGCk/P53eWTGJgVIho3PPT2du5cupmasLYk3EQDWKlOIGIt2shJcceiDYBhuak8OruQL47JA+Cf2w4xd2ERW/ZVO1yZaqIBrFQnyki29pFww3xhsOYv33beaG4+ZyQBn4f91WFuWLyWPxVpS8IN3PFTolQPEvR7XTVfWEQ4b0I+D39jMidlJxONGx55Zzs/emkT1fURp8vr1TSAleoCTfOF3bK1JcCQPik8PHsy546zdgB4d/th5i4sYvOH2pJwigawUl0oI+SnINNFLQm/l1tmjOb7544i6PNQdqSBG5asZcl7e4hrS6LbueOnQqkeLOCzWhJumSUBMGN8Px6ePZnBOcnE4ob5y0r44Z83UqUtiW6lAaxUN/DYW1u6qSUxOCeFh78xmfPG9wNgeUk58xYWsXFvlcOV9R4awEp1I7e1JIJ+LzefO4pbzxtN0G+1JG5cspbF2pLoFu74KVCqFwn4vK6aqgZwzti+PPqNQob0SSFuYMGyEm5/cSNVddqS6Eru+QlQqhfxeT0UZIZI8rnnV3BQTjIPXz6J8ydYLYkVO8q5auEqNpRqS6KruOe7r1Qv4/UI+RnuCuGA38v/nDOK288fTcjv5VBNI999di1Pr9itLYku4J7vvFK9UFMIu6kdAXDWmL48Onsyw3KtlsTv/7mD217YQGVdo9Ol9Sju+q4r1Qt5PUK/jCA+j7t+HQdmJ/PQZZP48sn5ALy3s4KrFhaxrrTS2cJ6EHd9x5XqpfxeD/kumh3RJOD38t2zR/LDL40hOcnL4ZpGvvfsOhYt36UtiU7gru+2Ur2Y32tdcy6U5I69hZs7c3Qej86ezPC8VOIGHv/XTm55fgPltdqS6AgNYKVcxGP3hNNDfqdL+ZQBWVZLYubEAgCKdlUwd2ERa3ZXOFxZ4tIAVsqF+qQG6JPmnlVzTZJ8Hm44awR3XDCWlCQv5bWN3Pzcep78905icW1JtJUGsFIulR609hb2etwVwgDTR+Xy6JxCRtgtiSfe3cX3n1+vLYk20gBWysWCfq/rFmw06Z8Z4reXTeIiuyWxZnclVz25itW7tCXRWu77riqlPsHv9VCQ4Z4N3ptL8nm4/qwR/PjLY0kJeKmoi3Dzc+t54l/akmgNDWClEkDTBu9ZyUlOl3JM00bmMn92IaP6pWGAJ5fv4ubn1nG4psHp0lxNA1ipBJKVkkTf9CAel52cAyjIDPHgrIl8ZXJ/ANbuqWLuwiJW7Sx3uDL30gBWKsGkBHyuXLQBVrvk2i8M5+6Z40gN+Kioi3DL8xv4wz93aEviGNz3HVRKtSjgs07OuXHRBsDpw/uw4IpCxuRbLYmnVuzme39ax8Ej2pJoTgNYqQTldfGiDYB+6UHuv3QiXyscAMD6UqslsXKHtiSaaAArleD6pAbISQ04XcYx+b0evjN9GD+9aBxpQR9V9RFufWEDj/2jRFsSaAAr1SNkhPzkZ4RceXIO4HPD+rBgTiFj89MBeGblHm56di1l1WGHK3OWBrBSPUQoyeoLu/HkHEDf9CD3X3oKs04dCMCGvdXMXVjE8pLDDlfmHHd+p5RS7ZLks3ZUS05y36INsC7FNHfaUH528XjSgz6qw1F+8OJG5v99O9FY3Onyup0GsFI9jMfe4D3DpSfnAE4bmsOCOYWML7BaEktWlXLjknUc6GUtCQ1gpXqonNQAuS7cUa1JXnqQX3/9FC6barUkNu+rZt7CIt7dfsjhyrqPBrBSPViavaOa2y531MTn9XDV54dy7yUTyAj5qQ5H+eGfN/HIO9uJ9IKWhDu/K0qpTmPtqBYk4Hfnog2AqUOyWTCnkJMHZADwp6JSblyylv09vCUhppdd12nKlClm1apVTpehVLu8U1zG/GUl7KmoY2BWMvOmDWX66LxWPdYYw8EjDdQ0RLu4yvaLxQ1PvLuTp1fsxgCpAR+3zBjF6cP7OF0aYP1FkZvWrjnXx+wD6RGwUgnineIy7li6ibIjYTJDfsqOhLlj6SbeKS5r1eNFhLz0INkp7txRDazVfd8+Ywj3fmUCmSE/NQ1RfvTSJn739rYe2ZLQAFYqQcxfVoLfKyQn+RCx3vu9wvxlJW16nsxk9+6o1uTUwdksuKKQiQOtlsTzq/dy/eK17Kuqd7iyzuVIAIvIDSKyUUQ2iciN9li2iLwpIlvt91n2uIjIgyKyTUTWi8jkZs9zpX3/rSJypROvRanusqeijtBRfdyQ30tpRV2bn8vNO6o16ZMa4JdfPYUrPnsSAry//whzFxaxbOtBp0vrNN3+ry8i44GrgKnAKcAFIjIcuBX4mzFmBPA3+2OA84AR9ttc4BH7ebKBO4HP2M91Z1NoK9UTDcxKpj4S+8RYfSTGgKzkdj1fwOclPyPoyssdNfF6hG9+bjC//OrJZCX7qW2I8eOlm/ntW9tojCZ+S8KJf/kxwApjTJ0xJgr8HbgEmAn80b7PH4GL7NszgSeNZTmQKSL5wLnAm8aYcmNMBfAmMKMbX4dS3WretKFEYoa6xijGWO8jMcO8aUPb/Zw+r4f8jJCrZ0gATD4pi8eumMKkQZkAvLhmL9cvXsPeysRuSTgRwBuBz4tIjogkA+cDA4G+xph99n32A33t2/2BPc0eX2qPHW/8U0RkroisEpFVBw/2nD9fVO8yfXQed104jry0IFX1EfLSgtx14bhWz4I4Hq9HyE8Punb5cpPslCR+8ZWTudJuSXxwoIarFxbx9w8S93e62//FjTFbROQ+4A2gFlgLxI66jxGRTpsfZ4xZACwAaxpaZz2vUt1t+ui8DgfusVjXnAtwsAZqwu6dpub1CFd+bjAnD8jgnleKKa9t5Cf/t5mZpxTwnenDXN1OORZHqjXG/MEYU2iMmQZUAB8AB+zWAvb7prk1e7GOkJsMsMeON66UagcRIS/NvRf+bG7SoCwWzCmk8CTrtM9L6z7kumfWtOuEpJOcmgWRZ78fhNX/fRpYCjTNZLgSeMm+vRS4wp4NcRpQZbcqXgfOEZEs++TbOfaYUqoDslKSXL2HRJPslCTu+8oEvnX6YDwC28pquHrRat5q5bxoN3Cq6fO8iOQAEeBaY0yliNwLPCsi3wZ2AV+37/sKVp94G1AH/CeAMaZcRO4G3rPvd5cxRq91olQnSAv68Xk8HKgOE3fxalmPCLNPO4kJAzL46ctbOFzTyE9f3sK6PZVcM32Y608u6lJkpdRxhSMx9le5O4SbVNY18vNXi3lvZwUAQ3NTuPOCsQzMbt80vWPRpchKqW4T9Hvpl+HuVXNNMpOT+PklE/ivM4bgESg5WMu8RUX8dcsBp0s7Lg1gpdQJJVIIe0S4/DOD+PXXT6FPahLhSJyfvVLMr15/n/BRi1jcQANYKdWioN9LfmYQr8f9IQxw8oBMHpszhalDsgF4ZeN+rn16DbsPu2uWhAawUqpVrKXLIddu7n60jGQ/P7t4PHM/b7Ukdhyq5eqninhjs3taEonxL6mUcoUkn4cCl2/i05xHhFlTB3H/pRPJSwsQjsS599VifumSlkRi/CsqpVzD2j8icUIYYHz/DObPKeS0oVZL4tWN+7nmqdXsPFzraF2J8y+olHINn9dDQWYooUI4I+TnnovGc/V/DMXrEXYeruOaRat5beN+x2pKnH89pZSreD1CQWYoofZfEBG+PmUgDzS1JKJxfvH6+9z7avGntvrsDonzL6eUch2vRxJiO8ujjS1IZ8GcQj43LAeANzYf4JpFq9lxqHtbEhrASqkOadrOMphgIZwe8nP3zHF8Z/owfB5hV3kd1zy1mlc27KO7VghrACulOszjEfIzgqQE3L2n8NFEhK8VDuCBWRPplx6kIRrnV298wM9fLaa+setbEhrASqlOISL0TQ+SFvQ7XUqbjclPZ/6cyZwxvA8Af91SxrxFRWw/WNOlX1cDWCnVqXLTAuSktGvDGkelBf385MKxXPcFqyVRWlHPtU+v4S/ru64loQGslOp0Gcl+8jNCCbF/RHMiwiWTB/DbyyaRnxGkMRrn129+wD2vFFPX2PlXCtEAVkp1iVCStX9Eoixdbm5UvzTmzylk2kirJfFWcRlXL1rNBweOdOrXSbx/GaVUwgj4vAm1dLm51ICPOy8Yyw1nDcfvtVoS//m/77Fo+a5Oa0kk3r+KUiqhNK2aS7S5wmC1JGZO7M9vL5tEQWaQxlic//3XDhqi8U55fg1gpVSXa5orHEpKvBAGGNk3jfmzC/nShHx+943JnTbnWQNYKdUtPB6hX3qQ5KTEmivcJCXg48cXjmN0v/ROe04NYKVUt7HmCgdITbAFG11FA1gp1a1EhLwEXbDR2TSAlVKOyE0LkJmc5HQZjtIAVko5JjslieyU3hvCGsBKKUdlJifRJy3xli53Bg1gpZTj0oN++qYHkQRbutxRGsBKKVdICfjIzwji9fSeENYAVkq5RtDvTbhrzXVE73iVSqmE4fd66J8ZSthVc22hAayUcp2mVXOpwZ69YEMDWCnlSiJCXlqwR88V1gBWSrladoo1Ta0nzpDQAFZKuV560N8jZ0hoACulEkJPnCHRc16JUqrH8yfw5u7HogGslEooXo9QkJG4+wo3pwGslEo4TfsKpyT4vsIawEqphGRNUwsk9JGwBrBSKmE1HQknagg7EsAi8l0R2SQiG0XkGREJisgQEVkhIttEZImIJNn3Ddgfb7M/P7jZ89xmj78vIuc68VqUUs5qCuFEXLrc7QEsIv2B64EpxpjxgBeYBdwH/MYYMxyoAL5tP+TbQIU9/hv7fojIWPtx44AZwMMiknjfAaVUh4lYS5e7MoRXlpRz9cIizrjvLS5bsJx3iss6/JxOtSB8QEhEfEAysA84E3jO/vwfgYvs2zPtj7E/f5ZYS2JmAouNMQ3GmB3ANmBq95SvlHKbrgzhlSXlPPDWVg7VNpAZ8lN2JMwdSzd1OIS7PYCNMXuBXwG7sYK3CigCKo0xUftupUB/+3Z/YI/92Kh9/5zm48d4zCeIyFwRWSUiqw4ePNi5L0gp5RoiQt+0IMFOnie8+L09+DxCyO9FREhO8uH3CvOXlXToeZ1oQWRhHb0OAQqAFKwWQpcxxiwwxkwxxkzJzc3tyi+llHJY005qnRnC+6rrCfo/GZchv5fSiroOPa8TLYgvAjuMMQeNMRHgBeB0INNuSQAMAPbat/cCAwHsz2cAh5uPH+MxSqlerCmEO2vFXH56iHAk/omx+kiMAVnJHXpeJwJ4N3CaiCTbvdyzgM3A28BX7ftcCbxk315qf4z9+beMMcYen2XPkhgCjABWdtNrUEq5nMcj5HdSCM86dSDRuKE+EsMYQ11jlEjMMG/a0I7V2OHK2sgYswLrZNpqYINdwwLgFuAmEdmG1eP9g/2QPwA59vhNwK3282wCnsUK79eAa40xsW58KUopl+usEJ46NJsbzhxBn5QAVfUR8tKC3HXhOKaPzuvQ84p1MNl7TJkyxaxatcrpMpRS3SgeN+yrDtMQ6dgxWlrQT25aoD0PPeY+mroSTinV4zUdCXf27IiO0gBWSvUKHo+Q77Jd1DSAlVK9RtOy5VSX7KKmAayU6lVEhLz0IGlBv9OlaAArpXqn3LQAGSFnQ1gDWCnVa+WkBshy8LL3GsBKqV4tKyWJnJR2TS3rMA1gpVSvl5Hsp0/75vd2iAawUkoB6UE/eelBrB0SuocGsFJK2VIDPvLSAt0WwhrASinVTEo3hrAGsFJKHaUphLuaBrBSSh1DSsDX5SfmNICVUuo40oP+Lp2ipgGslFInkJHs77IVc+7YkUIppVwsJzVAvAu2TtcAVkqpVshNC1DXGG35jm2gLQillGqlzt5LWANYKaUcogGslFIO0QBWSimHaAArpZRDNICVUsohGsBKKeUQDWCllHKIBrBSSjlEA1gppRyiAayUUg7RAFZKKYdoACullEM0gJVSyiFiTBdsculiInIQ2OV0Hc30AQ45XUQHJHL9WrszErl2aF/9h4wxM44e7HUB7DYissoYM8XpOtorkevX2p2RyLVD59avLQillHKIBrBSSjlEA9h5C5wuoIMSuX6t3RmJXDt0Yv3aA1ZKKYfoEbBSSjlEA1gppRyiAewQERkoIm+LyGYR2SQiNzhdU1uJiFdE1ojIX5yupS1EJFNEnhORYhHZIiKfdbqmthCR79o/MxtF5BkRCTpd0/GIyOMiUiYiG5uNZYvImyKy1X6f5WSNx3Oc2n9p/9ysF5EXRSSzI19DA9g5UeB7xpixwGnAtSIy1uGa2uoGYIvTRbTDA8BrxpjRwCkk0GsQkf7A9cAUY8x4wAvMcraqE3oCOHoBwq3A34wxI4C/2R+70RN8uvY3gfHGmJOBD4DbOvIFNIAdYozZZ4xZbd8+ghUC/Z2tqvVEZADwJeD3TtfSFiKSAUwD/gBgjGk0xlQ6WlTb+YCQiPiAZOBDh+s5LmPMMqD8qOGZwB/t238ELurOmlrrWLUbY94wxkTtD5cDAzryNTSAXUBEBgOTgBUOl9IW9wPfB+IO19FWQ4CDwP/a7ZPfi0iK00W1ljFmL/ArYDewD6gyxrzhbFVt1tcYs8++vR/o62QxHfAt4NWOPIEGsMNEJBV4HrjRGFPtdD2tISIXAGXGmCKna2kHHzAZeMQYMwmoxb1/An+K3S+difUfSQGQIiKzna2q/Yw1Dzbh5sKKyO1YbcSnOvI8GsAOEhE/Vvg+ZYx5wel62uB04EIR2QksBs4UkUXOltRqpUCpMabpr43nsAI5UXwR2GGMOWiMiQAvAJ9zuKa2OiAi+QD2+zKH62kTEfkmcAHwDdPBhRQawA4REcHqQ24xxvza6XrawhhzmzFmgDFmMNYJoLeMMQlxFGaM2Q/sEZFR9tBZwGYHS2qr3cBpIpJs/wydRQKdRLQtBa60b18JvORgLW0iIjOwWm8XGmPqOvp8GsDOOR2Yg3X0uNZ+O9/ponqJ/waeEpH1wETgZ86W03r2kftzwGpgA9bvsGuX9orIM8C/gVEiUioi3wbuBc4Wka1YR/T3Olnj8Ryn9oeANOBN+3f20Q59DV2KrJRSztAjYKWUcogGsFJKOUQDWCmlHKIBrJRSDtEAVkoph2gAq15FREzzRSMi4hORg+3d0c3eWe2aZh9PT7Td4ZRzNIBVb1MLjBeRkP3x2cDeDjxfJnBNS3dS6lg0gFVv9ArWTm4AlwHPNH3C3qv2z/Z+r8tF5GR7/Mf2/rDviEiJiFxvP+ReYJg9Kf+X9lhqs/2Gn7JXrCn1KRrAqjdaDMyyNzI/mU/uQvcTYI293+sPgCebfW40cC4wFbjT3svjVmC7MWaiMeZm+36TgBuBscBQrFWPSn2KBrDqdYwx64HBWEe/rxz16TOAhfb93gJyRCTd/tzLxpgGY8whrA1kjreN4kpjTKkxJg6stb+WUp/ic7oApRyyFGtf3elATisf09Dsdozj//609n6ql9MjYNVbPQ78xBiz4ajxfwDfAGtGA3CohX2aj2BtzqJUm+n/zKpXMsaUAg8e41M/Bh63d0qr4+NtE4/3PIdF5F/2hRtfBV7u7FpVz6W7oSmllEO0BaGUUg7RAFZKKYdoACullEM0gJVSyiEawEop5RANYKWUcogGsFJKOeT/A6+O2vLjNorzAAAAAElFTkSuQmCC\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())"
   ]
  },
  {
   "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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = df['timeStamp'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = df['timeStamp'].apply(lambda t: t.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thu</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week        Date  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12         Thu  2015-12-10  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12         Thu  2015-12-10  \n",
       "2                    HAWS AVE  1   Fire    17     12         Thu  2015-12-10  \n",
       "3          AIRY ST & SWEDE ST  1    EMS    17     12         Thu  2015-12-10  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12         Thu  2015-12-10  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now groupby this Date column with the count() aggregate and create a plot of counts of 911 calls.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Date').count()['lat'].plot()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason'] == 'Traffic'].groupby('Date').count()['lat'].plot()\n",
    "plt.title('Traffic')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason'] == 'EMS'].groupby('Date').count()['lat'].plot()\n",
    "plt.title('EMS')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason'] == 'Traffic'].groupby('Date').count()['lat'].plot()\n",
    "plt.title('Traffic')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dayHour = df.groupby(by=['Day of Week', 'Hour']).count()['Reason'].unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hour', ylabel='Day of Week'>"
      ]
     },
     "execution_count": 34,
     "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(dayHour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a clustermap using this DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f7156001490>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dayHour)"
   ]
  },
  {
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of Week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>1970</td>\n",
       "      <td>1581</td>\n",
       "      <td>1525</td>\n",
       "      <td>1958</td>\n",
       "      <td>1730</td>\n",
       "      <td>1649</td>\n",
       "      <td>2045</td>\n",
       "      <td>1310</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>1727</td>\n",
       "      <td>1964</td>\n",
       "      <td>1535</td>\n",
       "      <td>1598</td>\n",
       "      <td>1779</td>\n",
       "      <td>1617</td>\n",
       "      <td>1692</td>\n",
       "      <td>1511</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2291</td>\n",
       "      <td>1441</td>\n",
       "      <td>1266</td>\n",
       "      <td>1734</td>\n",
       "      <td>1444</td>\n",
       "      <td>1388</td>\n",
       "      <td>1695</td>\n",
       "      <td>1099</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>1960</td>\n",
       "      <td>1229</td>\n",
       "      <td>1102</td>\n",
       "      <td>1488</td>\n",
       "      <td>1424</td>\n",
       "      <td>1333</td>\n",
       "      <td>1672</td>\n",
       "      <td>1021</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>1584</td>\n",
       "      <td>1596</td>\n",
       "      <td>1900</td>\n",
       "      <td>1601</td>\n",
       "      <td>1590</td>\n",
       "      <td>2065</td>\n",
       "      <td>1646</td>\n",
       "      <td>1230</td>\n",
       "      <td>1266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month          1     2     3     4     5     6     7     8     12\n",
       "Day of Week                                                      \n",
       "Fri          1970  1581  1525  1958  1730  1649  2045  1310  1065\n",
       "Mon          1727  1964  1535  1598  1779  1617  1692  1511  1257\n",
       "Sat          2291  1441  1266  1734  1444  1388  1695  1099   978\n",
       "Sun          1960  1229  1102  1488  1424  1333  1672  1021   907\n",
       "Thu          1584  1596  1900  1601  1590  2065  1646  1230  1266"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayMonth = df.groupby(by=['Day of Week', 'Month']).count()['Reason'].unstack()\n",
    "dayMonth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month', ylabel='Day of Week'>"
      ]
     },
     "execution_count": 37,
     "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(dayMonth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f7155d87eb0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dayHour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Continue exploring the Data however you see fit!**\n",
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project - Clara Chia (SDC)

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_weiQiang

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_aaron

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_Clara_Wong

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_Vincent

# data[].attributes.body
Week 9 assignment.

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_Clement

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_(Berlinda Law)

# data[].attributes.attachment_link.title
Chai Si Aun_01-911 Calls Data Capstone Project

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

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

# data[].attributes.attachment_link.title
Ahmadkame_01-911 Calls Data Capstone Project

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_Cheryl Leong

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

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

# data[].attributes.body
LEE_Clarence_Jeremiah@scdf.gov.sg

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_GhweeCiqin

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_azrina

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_Clifton

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_Alvin_Lim

# data[].attributes.body
#SoTough

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project [Loo Khim]

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_Amberlin

# data[].attributes.body
a little stuck as i'm not sure how to change the date format...

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_AiLing

# data[].attributes.body
Submission for Week 9

# data[].attributes.attachment_link.title
Week_9 _01-911 Calls Data Capstone Project(Alex)

# data[].attributes.attachment_link.title
01-911 Calls Data Capstone Project_CT_MOM