# data[].attributes.attachment_link.title
DC_Bootcamp_Week_9_911 Calls Data Capstone Project - Ying Zhou

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

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

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

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

# data[].attributes.body
wk09

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

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

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

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

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

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

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

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

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

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

# 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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import visualization libraries and set %matplotlib inline. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the csv file as a dataframe called df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "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": 8,
   "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": 8,
     "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": 17,
   "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": 17,
     "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": 18,
   "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": 18,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#len(df['title'].unique())\n",
    "df['title'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** In the titles column there are \"Reasons/Departments\" specified before the title code. These are EMS, Fire, and Traffic. Use .apply() with a custom lambda expression to create a new column called \"Reason\" that contains this string value.** \n",
    "\n",
    "**For example, if the title column value is EMS: BACK PAINS/INJURY , the Reason column value would be EMS. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_reason(row):\n",
    "    title = row['title']\n",
    "    reason = title.split(':')[0]\n",
    "    return reason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df['Reason'] = df.apply(get_reason, axis=1)\n",
    "\n",
    "# or use this\n",
    "#df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS    48877\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts().head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2641044c128>"
      ]
     },
     "execution_count": 30,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You can now grab specific attributes from a Datetime object by calling them. For example:**\n",
    "\n",
    "    time = df['timeStamp'].iloc[0]\n",
    "    time.hour\n",
    "\n",
    "**You can use Jupyter's tab method to explore the various attributes you can call. Now that the timestamp column are actually DateTime objects, use .apply() to create 3 new columns called Hour, Month, and Day of Week. You will create these columns based off of the timeStamp column, reference the solutions if you get stuck on this step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month'] = df['timeStamp'].apply(lambda time: time.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day of Week'] = df['timeStamp'].apply(lambda time: time.dayofweek)"
   ]
  },
  {
   "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": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day of Week'] = df['Day of Week'].map(dmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2640f580b00>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEKCAYAAAAFJbKyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XmUVdWZ9/Hvj0HBkWLQNoIBI/GNAyIWitFEbBJAOyrO2CposNH1GltXx7QYsxSHNvbSbqcYDR0xOLRgjAPmNRGEpo1GZRJRcYCmUUpQZowyROB5/zi7ygtUFfcgt26V9fusddc9Z599zn0u1L3P3Xufs48iAjMzs2K1KHcAZmbWtDhxmJlZLk4cZmaWixOHmZnl4sRhZma5OHGYmVkuThxmZpaLE4eZmeXixGFmZrm0KncApdCxY8fo2rVrucMwM2tSZsyYsSwiOm2r3lcycXTt2pXp06eXOwwzsyZF0vvF1HNXlZmZ5eLEYWZmuThxmJlZLl/JMQ6z2nz++edUVVWxbt26codSdm3atKFz5860bt263KFYE+TEYc1GVVUVu+++O127dkVSucMpm4hg+fLlVFVV0a1bt3KHY02Qu6qs2Vi3bh0dOnRo1kkDQBIdOnRwy8u2mxOHNSvNPWlU87+DfRlOHGZmlosTh1mRWrZsSc+ePTnkkEM46aSTWLVqVblDMisLD443EUf85MHc+8y4dUgJImm+2rZty6xZswAYOnQo99xzD9dcc02ZozJreG5xmG2Ho48+mg8//LBm/dZbb6V379706NGD6667rqZ80KBBHHHEERx88MGMGjUKgI0bN3LBBRdwyCGHcOihh3L77bcDMGvWLPr06UOPHj049dRTWblyJQB9+/blqquu4sgjj+Sb3/wmf/rTnxrwnZptzS0Os5w2btzIpEmTGDZsGAATJkxg7ty5TJ06lYjg5JNP5oUXXuC73/0uo0ePpn379qxdu5bevXtz+umns2DBAj788EPefPNNgJouryFDhnD33Xdz3HHHce2113L99ddzxx13ALBhwwamTp3Ks88+y/XXX8/zzz9fdLxzFi6rtfyjlZ9yXh0t2cbUWnVru/Fxi8OsSGvXrqVnz5506NCBFStW8P3vfx/IEseECRM4/PDD6dWrF++88w5z584F4K677uKwww6jT58+LFy4kLlz57L//vszf/58LrvsMv74xz+yxx57sHr1alatWsVxxx0HZF1hL7zwQs1rn3baaQAcccQRLFiwoGHfuNkWnDjMilQ9xvH+++/z17/+lXvuuQfILqi7+uqrmTVrFrNmzWLevHkMGzaMKVOm8Pzzz/Pyyy/z+uuvc/jhh7Nu3ToqKip4/fXX6du3L/fccw8XXXTRNl975513BrIB+g0bNpT0fZptS8kSh6QDJc0qeHwi6QpJ7SVNlDQ3PVek+pJ0l6R5kmZL6lVwrKGp/lxJQ0sVs1kx9txzT+666y5uu+02Pv/8cwYMGMDo0aP59NNPAfjwww9ZsmQJq1evpqKigl122YV33nmHV155BYBly5axadMmTj/9dG688UZmzpzJnnvuSUVFRc34xUMPPVTT+jBrbEo2xhER7wI9ASS1BD4EngRGAJMi4hZJI9L6VcAJQPf0OAq4FzhKUnvgOqASCGCGpPERsbJUsZtty+GHH85hhx3G2LFjOf/883n77bc5+uijAdhtt914+OGHGThwIPfddx89evTgwAMPpE+fPkCWWC688EI2bdoEwM9//nMAxowZwyWXXMKaNWvYf//9eeCBB8rz5sy2oaEGx/sB/xMR70s6BeibyscAU8gSxynAgxERwCuS2knaJ9WdGBErACRNBAYCjzZQ7GYANS2Kas8880zN8uWXX87ll1++1T5/+MMfaj3WzJkztyrr2bNnTauk0JQpU2qWO3bs6DEOK7uGGuMYzBdf9HtHxGKA9LxXKt8XWFiwT1Uqq6vczMzKoOQtDkk7AScDV2+rai1lUU/5lq8zHBgOsN9++221g0/pMzPbMRqiq+oEYGZEfJzWP5a0T0QsTl1RS1J5FdClYL/OwKJU3neL8ilbvkhEjAJGAVRWVm6VWMzMyuGr+KO1IbqqzmHz8YjxQPWZUUOBpwvKh6Szq/oAq1NX1nNAf0kV6Qys/qnMzMzKoKQtDkm7AN8HLi4ovgV4TNIw4APgzFT+LHAiMA9YA1wIEBErJN0ITEv1bqgeKDczs4ZX0sQREWuADluULSc7y2rLugFcWsdxRgOjSxGjmZnl47mqrNnanr7n+hTTL92yZUsOPfTQmvXBgwczYsQI+vbty/z583n//fdrbrI0aNAgnn/+eT799FM2bdrEFVdcweTJk5FEmzZteOyxx3zrVysLJw6zBlQ4NfuW2rVrx0svvcSxxx7LqlWrWLx4cc22cePGsWjRImbPnk2LFi2oqqpi1113baiwzTbjuarMGonBgwczduxYAJ544omaiQ0BFi9ezD777EOLFtlHtnPnzlRUVJQlTjMnDrMGVD3DbvVj3LhxNdv69evHCy+8wMaNGxk7dixnn312zbazzjqLZ555hp49e/LjH/+Y1157rRzhmwHuqrIG8lU8l3171NdV1bJlS4499ljGjRvH2rVr6dq1a822zp078+677zJ58mQmT55Mv379+O1vf0u/fludZ2JWck4cZo3I4MGDOfXUUxk5cuRW23beeWdOOOEETjjhBPbee2+eeuopJw4rC3dVmTUi3/nOd7j66qs555xzNiufOXMmixYtAmDTpk3Mnj2br3/96+UI0cwtDmu+ytEVVj3GUW3gwIHccsstNeuSuPLKK7fab8mSJfzDP/wD69evB+DII4/kRz/6UekDNquFE4dZA9q4cWOt5YVTpxeqnsp94MCBDBw4sFRhmeXiriozM8vFLQ6zIsxZuCxX/YO6dCxRJGbl5xaHmZnl4sRhZma5OHGYmVkuThxmZpaLB8et2frghkO3XSnZrYg6n174X9uss+W06k899RTLli3jwQcf5K677io6HrNycuIwa0C1zVXVtWtXKisrt6q7YcMGWrXyR9QaH3dVmZXZlClT+MEPfgDAyJEjGT58OP3792fIkCFs3LiRn/zkJ/Tu3ZsePXrwq1/9qszRmrnFYdagCqcc6datG08++eRWdWbMmMGLL75I27ZtGTVqFHvuuSfTpk1j/fr1HHPMMfTv3993/rOyKmnikNQO+DVwCBDAD4F3gXFAV2ABcFZErFR2v8w7gROBNcAFETEzHWco8LN02JsiYkwp4zYrlfqmVa928skn07ZtWwAmTJjA7NmzefzxxwFYvXo1c+fOdeKwsip1i+NO4I8RcYaknYBdgJ8CkyLiFkkjgBHAVcAJQPf0OAq4FzhKUnvgOqCSLPnMkDQ+IlaWOHazsii8JWxEcPfddzNgwIAyRmS2uZKNcUjaA/gucD9ARPw1IlYBpwDVLYYxwKC0fArwYGReAdpJ2gcYAEyMiBUpWUwEPNubNQsDBgzg3nvv5fPPPwfgvffe47PPPitzVNbclbLFsT+wFHhA0mHADOByYO+IWAwQEYsl7ZXq7wssLNi/KpXVVW72pex37RtF1807V9WOctFFF7FgwQJ69epFRNCpUyeeeuqpssRiVq2UiaMV0Au4LCJelXQnWbdUXVRLWdRTvvnO0nBgOMB+++2XP1qzBlA9TXqhvn370rdvX4Ct7vzXokULbr75Zm6++eYGiM6sOKU8HbcKqIqIV9P642SJ5OPUBUV6XlJQv0vB/p2BRfWUbyYiRkVEZURUdurUaYe+ETMz+0LJEkdEfAQslHRgKuoHzAHGA0NT2VDg6bQ8HhiiTB9gderSeg7oL6lCUgXQP5WZmVkZlPqsqsuAR9IZVfOBC8mS1WOShgEfAGemus+SnYo7j+x03AsBImKFpBuBaaneDRGxosRxm5lZHUqaOCJiFtlptFvqV0vdAC6t4zijgdE7NjozM9sevnLczAw45u5jcu/z0mUvlSCSxs9zVZmZWS5ucViztT2/MOvzH4Oernf78uXL6dcv66X96KOPaNmyJdVnAE6dOpWddtppm68xZcoULr30UnbaaSdeffVVRowYwXPPPcdJJ51Ely5daNeuHeeee+6XfzNm9XDiMGsgHTp0qJmnauTIkey2225ceeWVm9WJCCKCFi1q7wx4+OGHGTFiBOeffz4Rwa9//WuWL19O69atSx6/WTUnDrMymzdvHoMGDeLYY4/l1Vdf5fe//z3XX389M2fOZO3atZx99tlce+213HfffTzxxBNMmjSJiRMnsmzZMj777DN69+7Nz372M2bNmkXHjh254ooreO+997jkkktYvnw5n28K7vzVb9i3iy+M/SrL24L+MuMzThxmjcCcOXN44IEHuO+++wC45ZZbaN++PRs2bOD444/njDPO4JJLLuHFF1/kjDPOYNCgQWzYsIGOHTvWtGIKZ90955xzGDlyJCeddBKvza1iU2wqy/uyryYPjps1At/4xjfo3bt3zfqjjz5Kr1696NWrF2+//TZz5swp+lgrV65k2bJlnHTSSQDs3KYNbdvussNjtubLLQ6zRqBwKvW5c+dy5513MnXqVNq1a8d5553HunXrch0vu72NWWm4xWHWyHzyySfsvvvu7LHHHixevJjnnss3w05FRQUdO3bkmWeeAWD9unWsXbumFKFaM+UWhzVbeQYHG3Ja9V69enHQQQdxyCGHsP/++3PMMflPG37kkUe4+OKLueaaa9hEC+741QO07ezuKtsxnDjMyqBw+vQDDjhgs4FtSTz00EO17vfwww/XLLdq1YpVq1bVrN900001ywceeCBTpkwByncvEfvqcuKwGp5ywcyK4TEOMzPLxS2OHawhL8Kx/CLCZxxRfYV6uaOwpsotDms22rRpw/Lly4lm/o0ZEfx1zV+oWpXvFF+zam5xWLPRuXNnqqqqWLp0ae59P1q59b3C66NP879GqWwZewRUrVrHAy9/UKaIrKlz4rBmo3Xr1nTr1m279j3vJw/mqj/j1iHb9TqlkDd2s21x4jCzHcJn5TUfHuMwM7NcnDjMzCyXkiYOSQskvSFplqTpqay9pImS5qbnilQuSXdJmidptqReBccZmurPlTS0lDGbmVn9GqLFcXxE9IyIyrQ+ApgUEd2BSWkd4ASge3oMB+6FLNEA1wFHAUcC11UnGzMza3jl6Ko6BRiTlscAgwrKH4zMK0A7SfsAA4CJEbEiIlYCE4GBDR20mZllSp04ApggaYak4als74hYDJCe90rl+wILC/atSmV1lW9G0nBJ0yVN357z9M3MrDilPh33mIhYJGkvYKKkd+qpW9s8EFFP+eYFEaOAUQCVlZXN+9JgM7MSKmmLIyIWpeclwJNkYxQfpy4o0vOSVL0K6FKwe2dgUT3lZmZWBiVLHJJ2lbR79TLQH3gTGA9Unxk1FHg6LY8HhqSzq/oAq1NX1nNAf0kVaVC8fyozM7MyKGVX1d7Ak2km0lbAf0bEHyVNAx6TNAz4ADgz1X8WOBGYB6wBLgSIiBWSbgSmpXo3RMSKEsZtZmb1KFniiIj5wGG1lC8H+tVSHsCldRxrNDB6R8doZmb5+cpxMzPLxYnDzMxyceIwM7NcnDjMzCwXJw4zM8vFicPMzHLxHQDtK8N3oDNrGG5xmJlZLk4cZmaWixOHmZnl4sRhZma5OHGYmVkuThxmZpaLE4eZmeXixGFmZrkUlTgkTSqmzMzMvvrqvXJcUhtgF6Bjum2r0qY9gK+VODYzM2uEtjXlyMXAFWRJYgZfJI5PgHtKGJeZmTVS9SaOiLgTuFPSZRFxdwPFZGZmjVhRYxwRcbekb0v6e0lDqh/F7CuppaTXJP0+rXeT9KqkuZLGSdople+c1uel7V0LjnF1Kn9X0oD8b9PMzHaUYgfHHwJuA44FeqdHZZGvcTnwdsH6vwK3R0R3YCUwLJUPA1ZGxAHA7akekg4CBgMHAwOBX0pqWeRrm5nZDlbstOqVwEEREXkOLqkz8HfAvwD/JEnA3wJ/n6qMAUYC9wKnpGWAx4FfpPqnAGMjYj3wv5LmAUcCL+eJxczMdoxir+N4E/ib7Tj+HcA/A5vSegdgVURsSOtVwL5peV9gIUDavjrVrymvZR8zM2tgxbY4OgJzJE0F1lcXRsTJde0g6QfAkoiYIalvdXEtVWMb2+rbp/D1hgPDAfbbb7+6wjIzsy+p2MQxcjuOfQxwsqQTgTZk137cAbST1Cq1KjoDi1L9KqALUCWpFbAnsKKgvFrhPjUiYhQwCqCysjJXl5qZmRWv2LOq/ru2xzb2uToiOkdEV7LB7ckRcS7wX8AZqdpQ4Om0PD6tk7ZPTmMq44HB6ayrbkB3YGqO92hmZjtQUS0OSX/hi+6hnYDWwGcRscd2vOZVwFhJNwGvAfen8vuBh9Lg9wqyZENEvCXpMWAOsAG4NCI2bsfrmpnZDlBU4oiI3QvXJQ0iO7OpKBExBZiSlufXtm9ErAPOrGP/fyE7M8vMzMpsu2bHjYinyE6rNTOzZqbYrqrTClZbkF3X4QFoM7NmqNizqk4qWN4ALCC7MM/MzJqZYsc4Lix1IGZm1jQUO1dVZ0lPSloi6WNJv0vTiZiZWTNT7OD4A2TXU3yNbLqPZ1KZmZk1M8WOcXSKiMJE8RtJV5QiILPm6pi7j8m9z0uXvVSCSMzqV2yLY5mk89K9NVpKOg9YXsrAzMyscSo2cfwQOAv4CFhMNiWIB8zNzJqhYruqbgSGRsRKAEntyW7s9MNSBWZmZo1TsS2OHtVJAyAiVgCHlyYkMzNrzIpNHC0kVVSvpBZHsa0VMzP7Cin2y//fgD9LepxsqpGz8KSDZmbNUrFXjj8oaTrZxIYCTouIOSWNzMzMGqWiu5tSonCyMDNr5rZrWnUzM2u+nDjMzCwXJw4zM8vFicPMzHIpWeKQ1EbSVEmvS3pL0vWpvJukVyXNlTRO0k6pfOe0Pi9t71pwrKtT+buSBpQqZjMz27ZStjjWA38bEYcBPYGBkvoA/wrcHhHdgZXAsFR/GLAyIg4Abk/1kHQQMBg4GBgI/FJSyxLGbWZm9ShZ4ojMp2m1dXoE2bUgj6fyMcCgtHxKWidt7ydJqXxsRKyPiP8F5gFHlipuMzOrX0nHONIU7LOAJcBE4H+AVRGxIVWpIrsxFOl5IUDavhroUFheyz5mZtbASpo4ImJjRPQEOpO1Er5VW7X0rDq21VW+GUnDJU2XNH3p0qXbG7KZmW1Dg5xVFRGrgClAH6CdpOor1jsDi9JyFdAFIG3fE1hRWF7LPoWvMSoiKiOislOnTqV4G2ZmRmnPquokqV1abgt8D3gb+C+yG0EBDAWeTsvj0zpp++SIiFQ+OJ111Q3oDkwtVdxmZla/Uk6Nvg8wJp0B1QJ4LCJ+L2kOMFbSTcBrwP2p/v3AQ5LmkbU0BgNExFuSHiObJ2sDcGlEbCxh3GZmVo+SJY6ImE0tN3uKiPnUclZURKwDzqzjWP+Cp3E3M2sUfOW4mZnl4sRhZma5OHGYmVkuThxmZpaLE4eZmeXixGFmZrk4cZiZWS5OHGZmlosTh5mZ5eLEYWZmuThxmJlZLk4cZmaWixOHmZnl4sRhZma5OHGYmVkuThxmZpaLE4eZmeVSylvHmlkT9cENh+bfqWKPHR9IM9XY//3d4jAzs1zc4qhHY8/6ZmblULLEIakL8CDwN8AmYFRE3CmpPTAO6AosAM6KiJWSBNwJnAisAS6IiJnpWEOBn6VD3xQRY0oVt5k1ff7RV1qlbHFsAH4cETMl7Q7MkDQRuACYFBG3SBoBjACuAk4AuqfHUcC9wFEp0VwHVAKRjjM+IlaWMPavhNwfnkb2wWnK8fuLy77KSjbGERGLq1sMEfEX4G1gX+AUoLrFMAYYlJZPAR6MzCtAO0n7AAOAiRGxIiWLicDAUsVtZmb1a5DBcUldgcOBV4G9I2IxZMkF2CtV2xdYWLBbVSqrq3zL1xguabqk6UuXLt3Rb8HMzJKSJw5JuwG/A66IiE/qq1pLWdRTvnlBxKiIqIyIyk6dOm1fsGZmtk0lTRySWpMljUci4olU/HHqgiI9L0nlVUCXgt07A4vqKTczszIoWeJIZ0ndD7wdEf9esGk8MDQtDwWeLigfokwfYHXqynoO6C+pQlIF0D+VmZlZGZTyrKpjgPOBNyTNSmU/BW4BHpM0DPgAODNte5bsVNx5ZKfjXggQESsk3QhMS/VuiIgVJYzbzMzqUbLEEREvUvv4BEC/WuoHcGkdxxoNjN5x0ZmZ2fbylCNmZpaLE4eZmeXixGFmZrk4cZiZWS5OHGZmlosTh5mZ5eLEYWZmuThxmJlZLk4cZmaWixOHmZnl4sRhZma5OHGYmVkuThxmZpaLE4eZmeXixGFmZrk4cZiZWS5OHGZmlosTh5mZ5eLEYWZmuZQscUgaLWmJpDcLytpLmihpbnquSOWSdJekeZJmS+pVsM/QVH+upKGlitfMzIpTyhbHb4CBW5SNACZFRHdgUloHOAHonh7DgXshSzTAdcBRwJHAddXJxszMyqNkiSMiXgBWbFF8CjAmLY8BBhWUPxiZV4B2kvYBBgATI2JFRKwEJrJ1MjIzswbU0GMce0fEYoD0vFcq3xdYWFCvKpXVVb4VScMlTZc0fenSpTs8cDMzyzSWwXHVUhb1lG9dGDEqIiojorJTp047NDgzM/tCQyeOj1MXFOl5SSqvAroU1OsMLKqn3MzMyqShE8d4oPrMqKHA0wXlQ9LZVX2A1akr6zmgv6SKNCjeP5WZmVmZtCrVgSU9CvQFOkqqIjs76hbgMUnDgA+AM1P1Z4ETgXnAGuBCgIhYIelGYFqqd0NEbDngbmZmDahkiSMizqljU79a6gZwaR3HGQ2M3oGhmZnZl9BYBsfNzKyJcOIwM7NcnDjMzCwXJw4zM8vFicPMzHJx4jAzs1ycOMzMLBcnDjMzy8WJw8zMcnHiMDOzXJw4zMwsFycOMzPLxYnDzMxyceIwM7NcnDjMzCwXJw4zM8vFicPMzHJx4jAzs1ycOMzMLJcmkzgkDZT0rqR5kkaUOx4zs+aqSSQOSS2Be4ATgIOAcyQdVN6ozMyapyaROIAjgXkRMT8i/gqMBU4pc0xmZs1SU0kc+wILC9arUpmZmTUwRUS5Y9gmSWcCAyLiorR+PnBkRFxWUGc4MDytHgi8W8KQOgLLSnj8UnP85eX4y6cpxw6lj//rEdFpW5ValTCAHakK6FKw3hlYVFghIkYBoxoiGEnTI6KyIV6rFBx/eTn+8mnKsUPjib+pdFVNA7pL6iZpJ2AwML7MMZmZNUtNosURERsk/Qh4DmgJjI6It8oclplZs9QkEgdARDwLPFvuOJIG6RIrIcdfXo6/fJpy7NBI4m8Sg+NmZtZ4NJUxDjMzayScOLYgqYOkWenxkaQP0/IqSXPKHV9ekjYWvJ9ZkrrWUudrkh5v+OjqJukaSW9Jmp3iPqqeuhdI+lpDxlefPLE3JpJC0kMF660kLZX0+3LGVax6Pruz0kk1jZKk2yVdUbD+nKRfF6z/m6R/KvJYIyVdWYo4CzWZMY6GEhHLgZ6Q/ScAn0bEbekLt0l8gLawNiJ61rVRUquIWASc0YAx1UvS0cAPgF4RsV5SR6C+D/4FwJtscYp2OWxH7I3JZ8AhktpGxFrg+8CHZY6paHV9dssaVHH+DJwJ3CGpBdm1GnsUbP82cEVtO5aLWxz5tJT0H+nX5ARJbQEkTZFUmZY7SlpQ1ii3If1C/62kZ4AJkrpKerPccRXYB1gWEesBImJZRCySdK2kaZLelDRKmTOASuCR9MuybVkjrzv2BSmJIKlS0pS0PFLS6PQ3NF/SP5YvdAD+APxdWj4HeLR6g6T2kp5KLalXJPVI5Y3tPWxG0gGSZhWsj5D0s7TcPf3CnyHpBUnfLEOIL5ElB4CDyX4E/UVShaSdgW8Br0n6Sfr7ny3p+uqdUwv3XUnPk138XHJOHPl0B+6JiIOBVcDpZY6nGG0LmutPFpQfDQyNiL8tV2D1mAB0kfSepF9KOi6V/yIiekfEIUBb4AcR8TgwHTg3InqmX8rlVFfs9fk/wACyOdmuk9S6pBHWbywwWFIboAfwasG264HXIqIH8FPgwYJtjek95DEK+L8RcQRwNfCLhg4gtfg3SNqPLIG8TPbvfjTZj6LZQF+y758jyVpVR0j6rqQjyK5rOxw4DejdEDG7qyqf/42I6l8uM4CuZYylWHV1VU2MiBUNHk0RIuLT9IH4DnA8ME7ZVPp/kfTPwC5Ae+At4JnyRbq1emKvz/9LLZT1kpYAe5PNltDgImJ26pY9h61Pfz+W9GMpIianMYU907ZG8x6KJakd0Af4naTq4nJ9J1a3Or4N/DvZXHzfBlaTdWX1T4/XUv3dyBLJ7sCTEbEGQFKDXBjtxJHP+oLljWS/egE28EXrrU2DRrT9Pit3APWJiI3AFGCKpDeAi8l+AVdGxMLUh90o/61riX0o9f+NbPl3Ve7P5XjgNrJfuR0KylVL3erz+RvbeyhU+G8P2b//BrL3s6y+McAG9GeyRHEoWVfVQuDHwCfAaLL/i59HxK8Kd0qD6g1+TYW7qnaMBcARabnRDDI3VZIOlNS9oKgnX0xauUzSbmz+7/wXsl9eZVdH7O+z+d9IY+/iHA3cEBFvbFH+AnAugKS+ZF+6nzRwbNvjI+BracygDWkMJyJWAoslnQogqYWkw8oU40tkJ1WsiIiNqTegHVl31ctks2b8MP3tI2lfSXuR/Z+cKqmtpN2Bkxoi2Mb0q6Apuw14TNmsvZPLHcxXwG7A3akrYQMwj2zm41XAG2RfwtMK6v8GuE/SWuDoMo9z1BX7t4D7Jf2UzccNGp2IqALurGXTSOABSbOBNWQtqUYvItZJupnsb2Y+UHha/WDg3tSC3Ql4GHi9wYPM/q47Av+5RdluEbGM7CSWbwEvp261T4HzImKmpHHALLIfKH9qiGB95biZmeXiriozM8vFicPMzHJx4jAzs1ycOMzMLBcnDjMzy8WJw5olfTFr8FuSXpf0T2mCuVK+5q3p9W4tKJOkZZIq0vo+ymapPbagzlJJHWo75jZe7wJJDT6Fhn31+ToOa65qpmKJO+zdAAAC3UlEQVRJF1L9J7AncF0JX/NioFP1BIgAERGSquclepbs6uHX0vOLkg4ku9BueQnjMsvFLQ5r9iJiCdlFej9KLYCukv4kaWZ6fBtA0kOSTqneT9Ijkk4uPFba/1ZlM/i+IensVD4e2BV4tbqsQOHsqNVzFR1dsP7ndIxOkn6XZkidJumYVL6rstlpp0l6rTDGgrj+TtLLSjP0mn0ZThxmQETMJ/s87AUsAb4fEb2As4G7UrVfAxcCpMn9vs3WEwGeRjbNyGHA94BbJe0TESeTWjkRMW6LfarnKYJs9tOngC5p/dtkiQWyq7lvj4jeZNOWVN/s5xpgcio/Pr3mrtUHT1NqjABOTFchm30p7qoy+0L1JH6tgV9I6kk2Yd83ASLivyXdk7q2TgN+FxEbtjjGscCjaaLDjyX9N9lU1/XNWjoVODx92bdOM+zOl3QAWeL4t1Tve8BBBTO57pHmJ+oPnKwv7vzWBtgvLR9PNjV3/yYyr5Q1AU4cZoCk/cmSxBKycY6PyVoNLYB1BVUfIpvobzDww9oOlfe1I2KNpHnpeDNT8SvAiWQtoOoJHltQy1xcyjLJ6RHx7hblR5HNzbQ/WfKbnjc2s9q4q8qaPUmdgPvIbhQVZIPkiyNiE3A+0LKg+m9It/GMiLdqOdwLwNmSWqbjfpesRbEtL6XjvpzWXwYuB16JLyaUmwD8qCDu6unAnwMuSwkESYcXHPd9stbRg5IOLiIOs21y4rDmqvrOiG8Bz5N9KVffjvOXwFBJr5D9Uq+5d0lEfAy8DTxQx3GfJLtj2+tkMyX/c0R8VEQ8L5G1DKoTx0ygM2lgPPlHoFLZrUPnAJek8hvJutdmK7sF8I2FB04tkXOB30r6RhGxmNXLs+Oa5SBpF7LprntFxOpyx2NWDm5xmBVJ0veAd4C7nTSsOXOLw8zMcnGLw8zMcnHiMDOzXJw4zMwsFycOMzPLxYnDzMxyceIwM7Nc/j8+W+0IcpLNQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Day of Week', hue = 'Reason', data=df, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2640f670048>"
      ]
     },
     "execution_count": 53,
     "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": [
    "# to relocate the legend to be outside of the chart\n",
    "sns.countplot(x='Day of Week', hue = 'Reason', data=df, )\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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2640f6eb198>"
      ]
     },
     "execution_count": 54,
     "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', hue = 'Reason', data=df)\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": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "byMonth = df.groupby('Month').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2640f7d60f0>"
      ]
     },
     "execution_count": 58,
     "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": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wong_huan_leong\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2640ed5c1d0>"
      ]
     },
     "execution_count": 60,
     "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": [
    "# reset_index is to conver index to column for the month field in byMonth\n",
    "# lmplot (linear model)\n",
    "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": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = df['timeStamp'].apply(lambda time: time.date())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": 62,
     "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26408bcc8d0>"
      ]
     },
     "execution_count": 63,
     "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": [
    "# choose any one column (e.g. 'lat' to perform the count by Date\n",
    "df.groupby('Date').count()['lat'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": [
    "# choose any one column (e.g. 'lat' to perform the count by Date\n",
    "df.groupby('Date').count()['lat'].plot()\n",
    "# to display better x-axis\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": 65,
   "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": [
    "# choose any one column (e.g. 'lat' to perform the count by Date\n",
    "df[df['Reason'] == 'Traffic'].groupby('Date').count()['lat'].plot()\n",
    "plt.title('Traffic')\n",
    "# to display better x-axis\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": [
    "# choose any one column (e.g. 'lat' to perform the count by Date\n",
    "df[df['Reason'] == 'Fire'].groupby('Date').count()['lat'].plot()\n",
    "plt.title('Fire')\n",
    "# to display better x-axis\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": [
    "# choose any one column (e.g. 'lat' to perform the count by Date\n",
    "df[df['Reason'] == 'EMS'].groupby('Date').count()['lat'].plot()\n",
    "plt.title('EMS')\n",
    "# to display better x-axis\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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day of Week  Hour\n",
       "Fri          0       275\n",
       "             1       235\n",
       "             2       191\n",
       "             3       175\n",
       "             4       201\n",
       "Name: lat, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the groupby without unstack\n",
    "byDayofWeek = df.groupby(['Day of Week', 'Hour']).count()['lat']\n",
    "byDayofWeek.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of Week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>275</td>\n",
       "      <td>235</td>\n",
       "      <td>191</td>\n",
       "      <td>175</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>372</td>\n",
       "      <td>598</td>\n",
       "      <td>742</td>\n",
       "      <td>752</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>980</td>\n",
       "      <td>1039</td>\n",
       "      <td>980</td>\n",
       "      <td>820</td>\n",
       "      <td>696</td>\n",
       "      <td>667</td>\n",
       "      <td>559</td>\n",
       "      <td>514</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>282</td>\n",
       "      <td>221</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>204</td>\n",
       "      <td>267</td>\n",
       "      <td>397</td>\n",
       "      <td>653</td>\n",
       "      <td>819</td>\n",
       "      <td>786</td>\n",
       "      <td>...</td>\n",
       "      <td>869</td>\n",
       "      <td>913</td>\n",
       "      <td>989</td>\n",
       "      <td>997</td>\n",
       "      <td>885</td>\n",
       "      <td>746</td>\n",
       "      <td>613</td>\n",
       "      <td>497</td>\n",
       "      <td>472</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>375</td>\n",
       "      <td>301</td>\n",
       "      <td>263</td>\n",
       "      <td>260</td>\n",
       "      <td>224</td>\n",
       "      <td>231</td>\n",
       "      <td>257</td>\n",
       "      <td>391</td>\n",
       "      <td>459</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>789</td>\n",
       "      <td>796</td>\n",
       "      <td>848</td>\n",
       "      <td>757</td>\n",
       "      <td>778</td>\n",
       "      <td>696</td>\n",
       "      <td>628</td>\n",
       "      <td>572</td>\n",
       "      <td>506</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>383</td>\n",
       "      <td>306</td>\n",
       "      <td>286</td>\n",
       "      <td>268</td>\n",
       "      <td>242</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "      <td>402</td>\n",
       "      <td>483</td>\n",
       "      <td>620</td>\n",
       "      <td>...</td>\n",
       "      <td>684</td>\n",
       "      <td>691</td>\n",
       "      <td>663</td>\n",
       "      <td>714</td>\n",
       "      <td>670</td>\n",
       "      <td>655</td>\n",
       "      <td>537</td>\n",
       "      <td>461</td>\n",
       "      <td>415</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>278</td>\n",
       "      <td>202</td>\n",
       "      <td>233</td>\n",
       "      <td>159</td>\n",
       "      <td>182</td>\n",
       "      <td>203</td>\n",
       "      <td>362</td>\n",
       "      <td>570</td>\n",
       "      <td>777</td>\n",
       "      <td>828</td>\n",
       "      <td>...</td>\n",
       "      <td>876</td>\n",
       "      <td>969</td>\n",
       "      <td>935</td>\n",
       "      <td>1013</td>\n",
       "      <td>810</td>\n",
       "      <td>698</td>\n",
       "      <td>617</td>\n",
       "      <td>553</td>\n",
       "      <td>424</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Hour          0    1    2    3    4    5    6    7    8    9   ...   14   15  \\\n",
       "Day of Week                                                    ...             \n",
       "Fri          275  235  191  175  201  194  372  598  742  752  ...  932  980   \n",
       "Mon          282  221  201  194  204  267  397  653  819  786  ...  869  913   \n",
       "Sat          375  301  263  260  224  231  257  391  459  640  ...  789  796   \n",
       "Sun          383  306  286  268  242  240  300  402  483  620  ...  684  691   \n",
       "Thu          278  202  233  159  182  203  362  570  777  828  ...  876  969   \n",
       "\n",
       "Hour           16    17   18   19   20   21   22   23  \n",
       "Day of Week                                            \n",
       "Fri          1039   980  820  696  667  559  514  474  \n",
       "Mon           989   997  885  746  613  497  472  325  \n",
       "Sat           848   757  778  696  628  572  506  467  \n",
       "Sun           663   714  670  655  537  461  415  330  \n",
       "Thu           935  1013  810  698  617  553  424  354  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDayofWeek = df.groupby(['Day of Week', 'Hour']).count()['lat'].unstack()\n",
    "byDayofWeek.head()\n",
    "# other method include 'pivot method'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2640ee73240>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAENCAYAAAAvwo97AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XmcXFWZ//HPk05n30jYAiEJSwRFVsMiICL4GwHRoKKAw+6Yn6+fEtTRQZ0FccQBXgoyOjK2MgioRASRjAgimxsEhBgS9iUJSSDsISEkJOnu5/fHOUWKSi2nuut21e3+vvO6r6p766l7T1eqnjp17rnnmLsjIiL5MajZBRARkfoocYuI5IwSt4hIzihxi4jkjBK3iEjOKHGLiOSMEreISM4ocYuI5IwSt4hIzihxi4jkzOBmF6CS6RPfk3wt/isb1yTvd+UbryXHvtG1MTl21JBhybEAXd3dybEfmrBncuyZnV3JsdOOSH8tbEhbcmzb9hOSYwdNmpgcC8DosemxZumxE7ZNDh201ZT02O2mJcd2P/tEcixA102zk2Nfv3VRcuz61en/1w8v2To59oW29HTzcp2Z6aylP63jP7u8jS8tSs457Vvu1Ovj9UbLJm4RkT7VnV7paTYlbhERAE//FdxsStwiIgB1NF82mxK3iAjgXZ3NLkIyJW4REVBTiYhI7ujkpIhIzqjGLSKSMzo5KSKSLzo5KSKSN2oqERHJGZ2cFBHJGdW4RURyZqCfnDSza9z9E2a2ECgeccsAd/f04e5ERPqCatycFW+PqedJZjYTmAkwecwubDUifahNEZHe8DqGcW62TBK3u68wszbgMnd/fx3P6wA6oL7xuEVEek01bnD3LjNba2Zj3X1VVscREWmIgd7GXeQNYKGZ/R54vbDR3WdlfFwRkfqoxv2mG+MiItLaBno/bjOb7O5L3f2KLPYvItJwObrkPatZ3n9duGNm12V0DBGRxvHu9KXJsmoqKZ4BeaeMjiEi0jg6OfmWi27UrU9EWp8SN3uZ2WpCzXt4vA+brpwck9FxRUR6xH2An5x097Ys9isikhnVuEVEciZHvUqUuEVEoCV6i6RS4hYRATWViIjkjmrcvTfI0q8N2tCdPhzj4Lb086bDzWoH9dCUkVsnx44kvcxbbrm6dlDU/o7JybHdr9QxTtjG9LZCX/lq+n4BGzc+fd8rnk3f77q1ybHdb6TH0j4kPXbYyPRYwKZOTY4dOnl5cuyK24Ylxz7dnp5C1tRxud8t/nJ6MJvGke6VBta4zews4NOEnnQ/cvfvmtl44BfAVGAJ8Al3X2lmBlwCHA2sBU5z93nV9p/VlZMiIvnS3Z2+VGFm7yQk7f2BvYBjzGwa8BXgNnefBtwW1wGOAqbFZSZwaa2iKnGLiEDoVZK6VPd2YK67r3X3TuAPwEeAGUBh/KYrgGPj/RnAlR7MBcaZ2cRqB1DiFhGBRo5V8iBwqJlNMLMRhCaQHYBt3H0FhMlmgEJ76fbAsqLnL4/bKmrZNm4RkT5VRxt38TSLUUecwQt3f8TMLgB+D6wBHgCqVdPLnUyrOlSIEreICNTVq6R4msUKj18GXAZgZt8i1KKfN7OJcWrHicALMXw5oUZeMAmoemZdTSUiItCwk5MAZrZ1vJ0MfBS4GpgDnBpDTgVuiPfnAKdYcCCwqtCkUolq3CIiAF0NHWTqOjObAGwEPhu7/Z0PXGNmnwKWAh+Psb8ltIM/SegOeHqtnStxi4hAQ/txu/t7ymx7GTiizHYHPlvP/pW4RURAl7yLiOSOLnkXEckZ1bhFRHLG8zPLohK3iAhApyZSeJOZtQHbFB/L3ZdmfVwRkbqojTswszOBc4DngcKr4sCeWR5XRKRe3q2mkoKzgF1j/8Waiq//nzJ2GluPqDpAlohI4+To5GTWl7wvA5JH4Hf3Dnef7u7TlbRFpE81bnTAzGVd414E3GlmNwLrCxvd/aKMjysiUh81lbxpaVyGxEVEpDWpV0ng7ucCmNnosOprsjyeiEiPqR93EOdeuwoYH9dfAk5x94eyPK6ISN1ydHIy66aSDuCL7n4HgJkdBvwIOCjj44qI1Edt3G8aWUjaAO5+p5mNzPiYIiL1a4HeIqky71ViZv9KaC4BOAlYnPExRUTq5p0NnUghU1n34z4D2Ar4FXB9vF9zdgcRkT7X7elLk2Xdq2QlMCvLY4iINMRAbyoxsznVHnf3D2dxXBGRHmuBmnSqrGrc7yZc7n41cA9gGR1HRKQx1B2QbYH/A5wIfBK4Ebha/bdFpGXlqMadyclJd+9y95vd/VTgQMK083fGYV5FRFpPV1f60mSZnZw0s6HABwm17qnAfxJ6lyTZfciWyccaMag9OXZl59rkWCf9G/jVDfVdzf/qxvT4P9Yx74QzOTl2yg82JsceN/L15Nix27+RHDvs7euSYwEGvZb+/2fjRifHdj36RPp+By9KjvWlT6WX4YH0MgDYqKHJseuXbkiObW9P/zxNW7e+dlC0ZHB6ed/fNiE5tlF8oDeVmNkVwDuBm4Bz3f3BLI4jItIwOWoqyarGfTLwOvA2YJbZm+cmjTDY1JiMjisi0jMDPXG7e9YX9oiINNZA78ctIpI7A73GLSKSN96Znxq3mjRERCBcgJO6VGFmu5rZ/KJltZl93sy+bmbPFG0/uug5XzWzJ83sMTP7QK2iqsYtIgINaypx98eAvQHMrA14hjDI3unAxe7+7eJ4M3sHcAKwO7AdcKuZvc3dK3YYV41bRASyGh3wCOApd3+6SswMYLa7r3f3xYQLFvevtlMlbhERwN2TlzqcQBizqeBzZrbAzP7HzLaI27YnjO1UsDxuq0iJW0QEoLM7eTGzmWZ2X9Eys3R3ZjYE+DDwy7jpUmBnQjPKCuA7hdAypan67aA2bhERwOtoAnH3DsKcutUcBcxz9+fjc54vPGBmPwJ+E1eXAzsUPW8S8Gy1HavGLSICWbRxn0hRM4mZTSx67CNAYSiQOcAJZjbUzHYEpgH3VtuxatwiIgAN7MZtZiMIQ1v/36LNF5rZ3oRmkCWFx9z9ITO7BngY6AQ+W61HCWScuM1sqLuvr7VNRKTZ6mkqqbkv97XAhJJtJ1eJPw84L3X/WTeV3J24TUSkufrTZMFmNt7dXynZtmPsb1jpOdsSurMMN7N92HTWdAwwosrzZgIzAd49fh92Hb1j7b9ARKQBvLP5CTlVSlPJ/5rZUe6+Gt68yucawnjblXwAOI1wdvSiou2vAV+r9KTiM7WnT/1Yfl5FEcm//AxVkpS4v0VI3h8EdgWuBP6+2hPc/QrgCjP7mLtf1/tiiohkq5Ft3Fmrmbjd/UYzawduAUYDx7p70hxL7n5dTPi7A8OKtn+jh+UVEclGf6hxm9n3eOvVO2OARcCZZoa7z6q1czP7b0Kb9vuAHwPHUaN/oohIM+RoHoWqNe77Stbv78H+D3L3Pc1sgbufa2bfoY4Jg0VE+op3NrsE6Som7thODYCZDQcmx+EK61GYwnutmW0HvAKoq4iItJ4c1bhr9uM2sw8B84Gb4/reZjYncf+/MbNxwIWEGvtiYHYPyyoikhnvTl+aLaVXydcJY8PeCeDu8+P19BWZ2X7AMnf/97g+ClgIPApc3IvyiohkohUScqqUKyc73X1VybZa/WZ+CGwAMLNDgfPjtlXUHlFLRKTP9bca94Nm9kmgzcymAbOAu2o8p63oasvjgY7Yn/s6M5vf8+KKiGTEyw2L3ZpSatxnEvphrwd+Tqg1f77Gc9rMrPClcARwe9FjGpFQRFpOd6clL82WcgHOWuCfzexb7v564n6vBv5gZi8Repb8CcDMdiEkfhGRltIKTSCpUnqVHGRmDwOPxPW9zOwH1Z4Thyj8R+AnwCG+aZK2QYQavIhIS3G35KXZUpotLiYMGjUHwN0fiCccq3L3uWW2PV53CUVE+kCeatxJ7c3uvszsLd8yVWdnaIS2svNnlmd1xA4fNCQ5dn33xuTYoW3p+63X+MEjk2Pb63gtRtXxRn3jjfbkWFuRvl/vfi09GBi+R/rft+b252sHRYMGpw8wZG3psW+sejk5dtT29V26t+6FDcmxG9elvz8fe21ccuzjw9KH9H/B0tPGo92rk2Mh/LzvLe9ufk06VUriXmZmBwEeZy2eRWw2ERHpLzw/gwMmJe7PAJcQJkZYThgl8LNZFkpEpK91d+Zn7vRqowNu4e4r3f0laoy/LSKSd/2lxv2Ymb1IuNjmL8BdOrkoIv1Vv2jjdvetzextwEFx+ZKZbQXMBf7i7hf2URlFRDLXCt38UlVt44417MeBn5jZzsDRwFnA3xFG/BMR6Rf6RXfA2JPkIODdwA6E2W/mAicB8/qkdCIifaSrux+cnAT+TEjQFwG/jpe+i4j0S/2ijRvYjk3t25+Jg0bNA+4G7nb3RX1QPhGRPtEvepW4+3OE+SF/BWBmI4AzgHMJ04+19UUBRUT6Qr+ocZvZWEL7dqHWvQ/wJPC/hO6BIiL9Rnc/6VXyJOFk5F3AvwP3uvu6KvEiIrnVyO6Aca7dHwPvJMwYdgbwGPALYCqwBPiEu6+0MBDUJYRee2uB09y9ageQak0lWzWg/CIiudDV2KaSS4Cb3f24OMbTCOBrwG3ufr6ZfQX4CnA2cBQwLS4HAJfG24oyn43GzNqAbYqP5e5Lsz6uiEg9GlXjNrMxwKHAaWG/vgHYYGYzgMNi2BWECdjPBmYAV8Z5C+aa2Tgzm+juFcfZzDRxm9mZwDnA80Che7sDe2Z5XBGRejWwV8lOwIvA5Wa2F3A/4cLFbQrJ2N1XmNnWMX57YFnR85fHbRUTd8Ue52Z2Qbz9eC/+gLOAXd19d3ffIy4Vk7aZzTSz+8zsvkdfU29DEek73W7JS3GuisvMol0NBvYFLnX3fYDXCc0ilZSr6lf9Gql2qdDRZtYOfLXaDmpYRh1zTLp7h7tPd/fpu43eqReHFRGpTz1TlxXnqrh0FO1qObDc3e+J69cSEvnzZjYRIN6+UBS/Q9HzJwHPVitrtaaSm4GXgJFmtprwreCFW3cfk/BaLALuNLMbCbPEQ3jyRQnPFRHpM43qDujuz5nZMjPb1d0fA44AHo7LqcD58faG+JQ5wOfMbDbhpOSqau3bUL1XyZeBL5vZDe4+o4d/w9K4DImLiEhL6mpsP+4zgZ/FHiWLgNMJLRzXmNmnCHmx0Az9W0JXwCcJ3QFPr7Xzmicn3X2GmW0D7Bc33ePuL6aU3N3PTYkTEWm2Rvbjdvf5wPQyDx1RJtapc1axmok7npz8NqHrigHfM7Mvu/u1Cc+9gzKN7O5+eD2FFBHJWo5GdU3qDvgvwH7u/gJAnEzhVkKDey1fKro/DPgYUN9U1iIifcDLdu5oTSmJe1AhaUcvU703ypvc/f6STX8xsz+kFk5EpK9094fRAYvcbGa/A66O68cTGtNrMrPxRauDCG0+29ZVQhGRPtCVVh9tCSknJ79sZh8FDiG0cXe4+/WJ+7+fTW3cnYSBVT7Vg3KKiGSqv7Vx4+5vjsudwsz2A5a5+45x/VRC+/YSQl9GEZGWkqc27qx+G/wQ2ABgZocC/0EYVGUV0FHleSIiTdFdx9JsWQ0y1ebur8T7xxOaV64DrjOz+RkdU0Skx1ohIaeqWeM2s2PMrN6aeVucoxJCh/Pbix7LfChZEZF6OZa8NFtKQj4BeMLMLjSztyfu92rgD2Z2A7AO+BOAme1CHYNOiYj0lU6z5KXZUnqVnBQHBj+RML6sA5cDV7v7axWec56Z3QZMBG6Jl3RC+KI4M6VgD2x4oXZQtOT155NjR7ePSI5dujq9DMMG1zcUy/D29Pj9RkxOjv3ouvQffNNPLvvfV9agbevoxdme/qOqa/Ez6fsFBh+0X+2gaNyxk9J3vPyp5FB7R9XJSd5i9Kr091DbTvsmxwIMvSp9rLZ1f1pWOyjaeVX6+2LY2pHJsXcMT094I609ObZRctSNO/lCmtXAdcBsQjL+CDAvTpRQ6Tlz3f16d3+9aNvjteZSExFphn51ctLMPkSY6HJn4Cpgf3d/wcxGAI8A38u2iCIi2etugSaQVCm/aT8OXOzufyze6O5rzeyMbIolItK38tRUktLGfUqVx25rbHFERJqjFZpAUqV0BzzQzP5qZmvMbIOZdcUZcURE+o089SpJOTn5fUKPkieA4cA/oHZtEelnvI6l2VLHKnnSzNrcvYvQJfCujMslItKnuptfkU6WkrjXxnnT5pvZhcAKIL3zpohIDvSrNm7g5Bj3OeB1wjTyH8uyUCIifa1fNZW4+9NxujJN/isi/VZnjppKKta4Lfi6mb0EPAo8bmYvmtm/9V3xRET6Rp6unKzWVPJ54GDCRMET3H0L4ADgYDP7Qp+UTkSkj7ilL81WLXGfApzo7osLG9x9EXBSfExEpN/IU427Wht3u7u/VLrR3V80qz10l5ktpkw7vrvvVF8RRUSy1woJOVW1xL2hh48VTC+6P4ww5sn4CrEiIk3VCr1FUlVL3HtVuLTdCIm4Knd/uWTTd83sz0DFk5tmNhOYCTBl7C5sNWJircOIiDREnnqVVEzc7t7Wmx2bWfGo8IMINfDR1Z7j7h3EyYT32+7QPH0BikjO9Zemkt76TtH9TmAJ8IkMjyci0mONrimaWRtwH/CMux9jZj8B3sum6RtPc/f5ZmbAJcDRwNq4veqEM5klbnd/X1b7FhFptAzGKjmLMNnMmKJtX3b3a0vijgKmxeUA4NJ4W1FmidvMhhIujZ9afBx3/0ZWxxQR6alGNpWY2STgg8B5wBdrhM8Aroxz8841s3FmNtHdV1R6QtKckz10QyxQJ2GMk8IiItJyGjxWyXeBf2Lz74PzzGyBmV0cK7cA2wPFszkvj9sqyrKNe5K7H5nh/kVEGqazjlbu4h5wUUfsXIGZHQO84O73m9lhRTFfBZ4DhhA6YZwNfIPQU69U1cJkmbjvMrM93H1hhscQEWmIek5OFveAK+Ng4MNmdjSh6/QYM/upu58UH19vZpcDX4rrywmjrhZMAp6tdvyGN5WY2YNmtgA4BJhnZo/FnwYL43YRkZbTqEve3f2r7j7J3acCJwC3u/tJZjYRwgB+wLHAg/Epc4BT4sB+BwKrqrVvQzY17u2BvTPYr4hIZvpgBpyfxSGyDZgPfCZu/y2hK+CThO6Ap9faURaJe7G7P53BfkVEMtOdwUXv7n4ncGe8f3iFGAc+W89+s0jcW5tZxe4v7n5RBscUEemVrmYXoA5ZJO42YBTlz5SKiLSkLGrcWckica/QRTYikjf5SdvZJG7VtEUkdwb6IFNHNGInnd3pLU7jhoxKjn1+7crk2MGD0gdIHDWk5ki3bzG6fURy7HaWvu+pEzeb+6Kitv3fkxzLxo3Jof5K6Yi+Vcqw8w7QXcdHxuuoF61bkxxqU3ZLL8Lq9Nd48G4Hp++3qzM5FmDQnumdtwY/9Exy7MhR65Njh61Nf29O8KG1g6LbNqS/hxplQDeVuPsrjd6n9GP1JG2RDOUnbWd75aSISG505Sh1K3GLiKA2bhGR3BnQbdwiInmUn7StxC0iAqjGLSKSOzo5KSKSMzo5KSKSM64at4hIvqjGLSKSM931DKfQZErcIiKoO6CISO505aixpOGTBReY2S5m9jszeyCu72lmX83qeCIivdGoyYL7QmaJG/gxcC6b/s6FwEmVw0VEmqcbT16aLcvEPdLd7yqsxAkxqw7qbGYzzew+M7vvpbXPZVg0EZG38jr+NVuWiftlM9uR2OZvZscCVbOxu3e4+3R3n77liG0zLJqIyFvlqakky5OTnwMuA3Yzs6eBFcCJGR5PRKTHXN0Bwd2fBA43s7GAufurWR1LRKS3OlugCSRVZonbzL5Wsg6Au38rq2OKiPRUK7Rdp8qyqaR4tt9hwAeBhzI8nohIj7VCb5FUWTaVXFC8bmYXAL/O6ngiIr2hNu7yhgI79+HxRESStUJvkVQNT9xmNtjdO83sb2y6/L8NmAiofVtEWlKjLnk3s2HAHwmV1cHAte5+TuwePRsYD8wDTnb3DWY2FLgSeBfwMnC8uy+pdowsatz3AvsCxxVt6wSec/f1GRxPRKTXGthUsh443N3XmFk78Gczuwn4InCxu882s/8GPgVcGm9XuvsuZnYCcAFwfLUDZHEBjgG4+1NFy9NK2iLSyhp1ybsHa+Jqe1wcOBy4Nm6/Ajg23p8R14mPH2GFbngVZFHj3srMvljpQXe/KINjioj0SiO7A5pZG3A/sAvwX8BTwKvu3hlDlgPbx/vbA8sAYjPzKmAC8FKl/WeRuNuAUcSat4hIHtQzkYKZzQRmFm3qcPeOwoq7dwF7m9k44Hrg7WV2UzhguVxZtTBZJO4V7v6NDPYrIpKZeurbMUl3JMS9amZ3AgcC4wqdN4BJwLMxbDmwA7DczAYDY4FXqu03szZuEZE86aQ7eanGzLaKNW3MbDjwfuAR4A42ddo4Fbgh3p8T14mP3+41zpRmUeM+ohE7eaO76giwb7G2843k2Bpt/m8xdHA7wwcPSY5/ae3q5NgJW4xJju2qoy4wauKG5Fg2pJ8v7l7ydPp+ATZ01o6JfGP6/3Xb1J3Sy/DiivQybEh/D/Fq1crQW3Q+/TiD9v9Acvygraakl6OOMq9fkf4eGjIi/f9uUB3VtAPXr+fh9qFJsf8weAo3Wt8Ob9TAXiUTgStiO/cg4Bp3/42ZPQzMNrNvAn8jDMJHvL3KzJ4k1LRPqHWAhidud09/V7e4rJJ2v5dR0s6jzJJ2DqUmbaDPkzY07pJ3d18A7FNm+yJg/zLb3wA+Xs8xNOekiAgaZEpEJHc0VomISM5odEARkZzp8vwMM6XELSKC2rhFRHKnnisnm02JW0QE1bhFRHJHNW4RkZzRyUkRkZxRU4mISM6oqUREJGdU4xYRyRkf6G3c1aYuA01fJiKtR5e8w+h4uyuwH2GgcIAPEaatFxFpKQO+V4m7nwtgZrcA+7r7a3H968AvKz2veB63bUdNYdzwrbMonojIZvI0OmAWU5cVmwwUT8myAZhaKdjdO9x9urtPV9IWkb7U7Z68NFvWJyevAu41s+sJc3F+BLgy42OKiNRNvUoidz/PzG4C3hM3ne7uf8vymCIiPZGnppK+6A44Aljt7pfH2Y93dPfFfXBcEZFk6lUSmdk5wHRC75LLgXbgp8DBWR5XRKReXd0DvFdJkY8QZjueB+Duz5rZ6OpPERHpe2oq2WSDu7uZOYCZjcz4eCIiPZKnppKsuwNeY2Y/BMaZ2aeBW4EfZXxMEZG6uXvy0mxZXfL+eeAvwHeB9wGrCe3c/+buv8/imCIivdEK/bNTZdVUMgm4BNgNWADcRUjk92d0PBGRXtEl7+5fAjCzIYReJQcBZwA/MrNX3f0dWRxXRKSnWqEJJFXWJyeHA2OAsXF5FliY8TFFROo24K+cNLMOYHfgNeAeQlPJRe6+MovjiYj0lmrcYXCpocATwDPAcuDVjI4lItJreUrcdXWBqbO7jAHvJAzT+hPgPuAW4Nxe7ndmf41tlXK0QmyrlKMVYlulHHmL7c9L9gcIPUyOJ/QyeQp4tZf7u6+/xrZKOVohtlXK0QqxrVKOvMX25yWrNu5ZhJ4kBwMbCV0B7wb+B52cFBHplazauKcC1wJfcPcVGR1DRGRAyqofd9XJgnupox/Htko5WiG2VcrRCrGtUo68xfZbFtuNREQkJ7IeZEpERBpMiVtEJGf6YuqyHjOz3YAZwPaEyYafBea4+yN9XI79AXf3v5rZO4AjgUfd/bcJz73S3U/JvJA9FMeTOQF41t1vNbNPEnoEPQJ0uPvGphZQRDbTsm3cZnY2cCIwm3DlJYQ+4ScAs939/F7ufzfCF8I97r6maPuR7n5z0fo5wFGEL7nfAwcAdwLvB37n7ucVxc4pPQxhWNvbAdz9w1XKcwiwP/Cgu99S8tgBwCPuvtrMhgNfAfYFHga+5e6rSuJnAde7+7KE1+Fn8W8bQbi6dRTwK+AIwvvj1JL4nQkzG+0AdBKujr26tAwikqFmdySv0tH+caC9zPYhwBN17uv0kvVZwGPAr4ElwIyix+aVxC4E2oiTHgNj4vbhwIKS2HmEOTUPA94bb1fE++8tib236P6ngfnAOYQ+718piX0IGBzvdxDGOT8kxv+qzN+7ivDr5E/A/wO2qvLaLIi3g4Hngba4bmX+vlmEL69/IYw/8wPgPMIXyGHNfs9k9D7cOqP9Tmj231ajfGOB84FHgZfj8kjcNq6O/dxUsj4G+A/gKuCTJY/9oGR9W+BS4L+ACcDX4+fxGmBis1+jpv7/NLsAVf7DHwWmlNk+BXiszn0tLVlfCIyK96cSLsc/K67/rST2b+Xux/X5JeuDgC/E5LZ33LaoQpmK9/vXQnIFRgILS2IfKbpf+sUyv9y+Y1n+DrgMeBG4GTgVGF0S+yDhy3ALwqBg4+P2YcXHLXrdCol9BHBnvD+59LWJ23P14QfGlywTCF/sWxRel6LYI0v+zssIY8//HNimJPZ8YMt4fzqwCHgSeJqSL/TC/zHhy3HnhNdmOnAHocKwQ3zvrYrvqX1KYkcB3yBUBFbF98Vc4LQy+/0dcDawbclreTbw+5LYfSss7wJWlMReF1+PY4E5cX1ohff2zcCZhF+YC+KxJ8dtN9STA/rb0vQCVHlDHhnf3DcRapkd8T/yyeIPTVH8ggrLQmB9SezDZd7QNwMXsXkyvgcYEe8PKto+tvSNVvTYJOCXwPcp+dIoinkgJoQJlFzGy+ZfEL8k/moALgemx/tvA/5aZt+lH4B24MPA1cCLJY99ISaSpwk16tsI08stBM4piV1Y9CHbAri/6LEHy5QjVx9+oBtYXLJsjLeLSmLnFd3/MfBNQqXiC8CvS1+3ovt3APsV/f9tdgl3PN63gaXAvXGf21V4H91LaMo7EVgGHBe3HwHcXRJ7A3BafH9+EfhXYBpwBaHJrTi2YuWo9DGgi9AceEeZZV1JbOnn658JvzInlPm/K67clFa+NquwDKSl6QWoWrhQazwQ+BhwXLzfViH2eWDv+OEpXqYSTrwVx95OrBEXbRsMXAl0lWwfWuF4WwJ71Cj/B0s/EEWPLSEkzMXxdtu4fVSZN/fyLYQOAAADsElEQVRYwkBdTxG+SDbG5/wB2KvMvjer/RY9NrzMtu0KiQEYF1/r/cvEnUVIfh2EWnThy2Qr4I9l4nP14Qe+REj0exRtW1yh/POq7Kd0/VE2NXXNLXlsYY19v4fQJPVcfC1mlsRW+/tKKwAPlKz/tehz9mjJY7cA/0TRrwdgG8IX360lsQ8C0yq8TstK1h+hqAIUt51K+BXwdKXyAt+s9boNpKXpBWjYHxJ+qh5S4bGfl6xPoqgWWPLYwU3+O0YAO1Z4bDSwF6EWuk2Vfbwtw/LtHhP7bgmxufvws+nX0kXx9a7U1LWcUGv9R8KXqBU9Vnpu4Mz4WhxOaKr5LnAocC5wVZl9b/ZLjnCe5Ujg8pLtdxOaxD5O+NV0bNz+Xjb/JXdX4TMCfIhwcr3wWOkX6RbABYQvnZXAK/F1v4DNm42OA3at8DodW7J+IfD+MnFHUnLuitCsM6pM7C7AtVm9x/OwNL0AWvrvUvLhf6Xkw79FSWxLffhjYpsLPFfh8XNKlsI5im2BK8vEHwb8gnD+YSHwW8KQx4PLxM6u4zXei9AkdRNhjtdLCL2DHgIOKondk9C08irwZ+IXPOEX06wy+96N0HtqVMn2ck2VuxGaZ3oTe1Rv9juQlqYXQMvAXCjp6dOKsYSeQ+9slfL2ZTmor+dVPbFnZhE70JamF0DLwFyocNJWsa1RDurredX02IG2tPSVk5JvZrag0kOEtm7FlsS2UDnaPF6Y5u5LzOww4FozmxLjWy12QFHilixtA3yAcHKrmBFOlCl289hWKcdzZra3u88HcPc1ZnYMYTKUPVowdkBR4pYs/YbwU3d+6QNmdqdiy8a2SjlOIQxp8CZ37wROMbMftmDsgNKyY5WIiEh5GtZVRCRnlLhFRHJGiVuazszWlKyfZmbfb1Z5RFqdErf0W2bW1uwyiGRBiVtamplNMbPbzGxBvJ0ct//EzI4rilsTbw8zszvM7OeECzhE+h11B5RWMNzMiruojScM1wphaNwr3f0KMzsD+E/CcK7V7E+4VH1x44sq0nxK3NIK1rn73oUVMzuNMEEAwLuBj8b7VxEGmKrlXiVt6c/UVCJ5U7jwoJP4/jUzI8ziU/B6XxdKpC8pcUuru4swQTTA3xOGI4UwWty74v0ZhFl+RAYEJW5pdbOA0+MASScTZuGBML3ae83sXuAAVMuWAUSXvIuI5Ixq3CIiOaPELSKSM0rcIiI5o8QtIpIzStwiIjmjxC0ikjNK3CIiOaPELSKSM/8fx4dK4H2y6UcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(byDayofWeek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2640e0fe6a0>"
      ]
     },
     "execution_count": 74,
     "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": [
    "# make it bigger\n",
    "plt.figure(figsize = (12,6))\n",
    "\n",
    "# try other colour palette\n",
    "sns.heatmap(byDayofWeek, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a clustermap using this DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x2640debc9e8>"
      ]
     },
     "execution_count": 75,
     "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(byDayofWeek,cmap='viridis')"
   ]
  },
  {
   "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": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "byDayofWeek_Month = df.groupby(['Day of Week', 'Month']).count()['lat'].unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byDayofWeek_Month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x2640f4984e0>"
      ]
     },
     "execution_count": 79,
     "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(byDayofWeek_Month, cmap='viridis') "
   ]
  },
  {
   "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
}

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

# data[].attributes.body
updated file

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