911 Calls Data Capstone Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls Capstone Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this capstone project we will be analyzing some 911 call data from [Kaggle](https://www.kaggle.com/mchirico/montcoalert). The data contains the following fields:\n",
    "\n",
    "* lat : String variable, Latitude\n",
    "* lng: String variable, Longitude\n",
    "* desc: String variable, Description of the Emergency Call\n",
    "* zip: String variable, Zipcode\n",
    "* title: String variable, Title\n",
    "* timeStamp: String variable, YYYY-MM-DD HH:MM:SS\n",
    "* twp: String variable, Township\n",
    "* addr: String variable, Address\n",
    "* e: String variable, Dummy variable (always 1)\n",
    "\n",
    "Just go along with this notebook and try to complete the instructions or answer the questions in bold using your Python and Data Science skills!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Import numpy and pandas **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import visualization libraries and set %matplotlib inline. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sb\n",
    "import matplotlib.pyplot as mp"
   ]
  },
  {
   "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')\n",
    "df = pd.DataFrame(df)"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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": [
    "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:40:01</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e Reason  \n",
       "0      REINDEER CT & DEAD END  1    EMS  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS  \n",
       "2                    HAWS AVE  1   Fire  \n",
       "3          AIRY ST & SWEDE ST  1    EMS  \n",
       "4    CHERRYWOOD CT & DEAD END  1    EMS  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'] = df['title'].apply(lambda x: x.split(':')[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the most common Reason for a 911 call based off of this new column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8873eb88>"
      ]
     },
     "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": [
    "sb.countplot(x = 'Reason', data=df, alpha = 0.8)"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda x: x.hour)\n",
    "df['Month'] = df['timeStamp'].apply(lambda x: x.month)\n",
    "df['Day of Week'] = df['timeStamp'].apply(lambda x: x.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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day of the Week'] = df['Day of Week'].apply(lambda x: dmap[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Day of the 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",
       "      <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>3</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>3</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>3</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>3</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99487</th>\n",
       "      <td>40.132869</td>\n",
       "      <td>-75.333515</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST; NORRISTOWN; 2016-08-2...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:06:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>MARKLEY ST &amp; W LOGAN ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99488</th>\n",
       "      <td>40.006974</td>\n",
       "      <td>-75.289080</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL; LOWER MERION; ...</td>\n",
       "      <td>19003.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:07:02</td>\n",
       "      <td>LOWER MERION</td>\n",
       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99489</th>\n",
       "      <td>40.115429</td>\n",
       "      <td>-75.334679</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST;  NORRISTOWN; Station ...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: FALL VICTIM</td>\n",
       "      <td>2016-08-24 11:12:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>CHESTNUT ST &amp; WALNUT ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99490</th>\n",
       "      <td>40.186431</td>\n",
       "      <td>-75.192555</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN;  HORSHAM; Station 352; ...</td>\n",
       "      <td>19002.0</td>\n",
       "      <td>EMS: NAUSEA/VOMITING</td>\n",
       "      <td>2016-08-24 11:17:01</td>\n",
       "      <td>HORSHAM</td>\n",
       "      <td>WELSH RD &amp; WEBSTER LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99491</th>\n",
       "      <td>40.207055</td>\n",
       "      <td>-75.317952</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST; UPPER GWYNEDD; 2016-08...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2016-08-24 11:17:02</td>\n",
       "      <td>UPPER GWYNEDD</td>\n",
       "      <td>MORRIS RD &amp; S BROAD ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Traffic</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>Wed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99492 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             lat        lng  \\\n",
       "0      40.297876 -75.581294   \n",
       "1      40.258061 -75.264680   \n",
       "2      40.121182 -75.351975   \n",
       "3      40.116153 -75.343513   \n",
       "4      40.251492 -75.603350   \n",
       "...          ...        ...   \n",
       "99487  40.132869 -75.333515   \n",
       "99488  40.006974 -75.289080   \n",
       "99489  40.115429 -75.334679   \n",
       "99490  40.186431 -75.192555   \n",
       "99491  40.207055 -75.317952   \n",
       "\n",
       "                                                    desc      zip  \\\n",
       "0      REINDEER CT & DEAD END;  NEW HANOVER; Station ...  19525.0   \n",
       "1      BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...  19446.0   \n",
       "2      HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...  19401.0   \n",
       "3      AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...  19401.0   \n",
       "4      CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...      NaN   \n",
       "...                                                  ...      ...   \n",
       "99487  MARKLEY ST & W LOGAN ST; NORRISTOWN; 2016-08-2...  19401.0   \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL; LOWER MERION; ...  19003.0   \n",
       "99489  CHESTNUT ST & WALNUT ST;  NORRISTOWN; Station ...  19401.0   \n",
       "99490  WELSH RD & WEBSTER LN;  HORSHAM; Station 352; ...  19002.0   \n",
       "99491  MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...  19446.0   \n",
       "\n",
       "                             title           timeStamp                twp  \\\n",
       "0           EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
       "1          EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2              Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
       "3           EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
       "4                   EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
       "...                            ...                 ...                ...   \n",
       "99487  Traffic: VEHICLE ACCIDENT - 2016-08-24 11:06:00         NORRISTOWN   \n",
       "99488  Traffic: VEHICLE ACCIDENT - 2016-08-24 11:07:02       LOWER MERION   \n",
       "99489             EMS: FALL VICTIM 2016-08-24 11:12:00         NORRISTOWN   \n",
       "99490         EMS: NAUSEA/VOMITING 2016-08-24 11:17:01            HORSHAM   \n",
       "99491  Traffic: VEHICLE ACCIDENT - 2016-08-24 11:17:02      UPPER GWYNEDD   \n",
       "\n",
       "                                 addr  e   Reason  Hour  Month  Day of Week  \\\n",
       "0              REINDEER CT & DEAD END  1      EMS    17     12            3   \n",
       "1          BRIAR PATH & WHITEMARSH LN  1      EMS    17     12            3   \n",
       "2                            HAWS AVE  1     Fire    17     12            3   \n",
       "3                  AIRY ST & SWEDE ST  1      EMS    17     12            3   \n",
       "4            CHERRYWOOD CT & DEAD END  1      EMS    17     12            3   \n",
       "...                               ... ..      ...   ...    ...          ...   \n",
       "99487         MARKLEY ST & W LOGAN ST  1  Traffic    11      8            2   \n",
       "99488  LANCASTER AVE & RITTENHOUSE PL  1  Traffic    11      8            2   \n",
       "99489         CHESTNUT ST & WALNUT ST  1      EMS    11      8            2   \n",
       "99490           WELSH RD & WEBSTER LN  1      EMS    11      8            2   \n",
       "99491          MORRIS RD & S BROAD ST  1  Traffic    11      8            2   \n",
       "\n",
       "      Day of the Week  \n",
       "0                 Thu  \n",
       "1                 Thu  \n",
       "2                 Thu  \n",
       "3                 Thu  \n",
       "4                 Thu  \n",
       "...               ...  \n",
       "99487             Wed  \n",
       "99488             Wed  \n",
       "99489             Wed  \n",
       "99490             Wed  \n",
       "99491             Wed  \n",
       "\n",
       "[99492 rows x 14 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23c8b50b148>"
      ]
     },
     "execution_count": 18,
     "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": [
    "m =sb.countplot(x = 'Day of the Week', data = df, hue='Reason')\n",
    "m.legend(loc='center left', bbox_to_anchor=(1,0.8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23c8b522548>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m =sb.countplot(x = 'Month', data = df, hue='Reason')\n",
    "m.legend(loc='center left', bbox_to_anchor=(1,0.8))"
   ]
  },
  {
   "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": 20,
   "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",
       "      <th>Day of the 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>11527</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13203</td>\n",
       "      <td>13096</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>9930</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11465</td>\n",
       "      <td>11396</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>9755</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11092</td>\n",
       "      <td>11059</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>9895</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11323</td>\n",
       "      <td>11283</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>9946</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11420</td>\n",
       "      <td>11378</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "Month                                                                      \n",
       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "\n",
       "       Reason   Hour  Day of Week  Day of the Week  \n",
       "Month                                               \n",
       "1       13205  13205        13205            13205  \n",
       "2       11467  11467        11467            11467  \n",
       "3       11101  11101        11101            11101  \n",
       "4       11326  11326        11326            11326  \n",
       "5       11423  11423        11423            11423  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df.groupby('Month').count()\n",
    "s.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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8b6f27c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+eJhfvbWNNbsOkdK3O/9x4yhuy09v1axikWJidoDlWytwznX4sBjStUTv/6JOoFtsDA/efCFzLhvK/ywt5rd/3cHT75Ywe3IWcy4bqslLGqk6cZLSyuPsqqymtLKa0oPV7KoM/QQrj1Nb3wBAakJPvn/TGGbmpHWJiYTysgM8v7aM7fuPMawT3xchkU9h0AEyk3rz05kXcdeUYTzyxlbmvr2dp1aW8MVLsvjKp4aS0Cve7xLbXV19A3sOn6C08qMP+dKDH334Vx6r/djyfXvEkRHoxfkD+3L1BQNJD/QiM6kXE7OTwjarWCQ4dY/B6h2VCgNpV+ac87uGVsnNzXWFhYV+l9EqxeVV/Pz1rby8bg99usfxpUuzufPSbPr3jOzz3YerT/7tg36X9+3+1Id/2cHj1DV89LcWF2OkJvYkI9CL9EAv0hN7kRH46Ceazv23hXOO3P96ncvPS+bhWzQ0urSdmRU553JPb9eRgQ+Gp/Tl0c9P4J6pR/jF61t55I2t/PavO/jKp4byxUuy6NvJ70twzrFlXxUFG/exee+R0If/gWqOnKj72HKB3vGkB3oxNi2BG8cODn3wJ4Y+/Af376GB/M6CmZGXFdCgddLuFAY+GjmoH49/IYeNuw/zs4KtPFzwIU/+dQdzLhvK7MlZ9O5EE5vUNzjW7jrIko17eW3TPkoOVGMG2Um9yUjqxYSMxI99y08P9Oz0oRYp8rIDvLpxL7sPHWdIQk+/y5Eo1Xk+bbqw0UP688TsXNYFD/Gzgg/58atbeOLtHXz18qHcMSmLnvH+dJTW1NXzTvEBXtu0l4JN+9h/tJZuscYlwwfwD5cN46pRKRF9U1ekmOjdb7B6ZyXTx6X6XI1EK4VBJzI2LYHffjGfNbsO8rOCD3lo8QfMXb6Df5wyjNsnZnTI1TNVJ06ybEsFr23cy5tbKv429eKU85O5ZvQgrjg/Wd/4O9gFg/vRp3scq3YoDKT9KAw6oQkZiTx150RW76zkZwUf8v2XNzF3+TbuvmI4t+Slh318+4qqGgo27eO1TXt5p/gAtfUNDOgTz6cvGsw1owdx8bAkjanvo9gYY0Jmom4+k3alMOjE8rIC/OErk1ix7QAPF2zhP1/cyONvbuOeqcOZmZPepkssSw4cY8nGvSzZuI81uw7iHGQEejH74kyuHT2I8d7Ui9I5TMwO8JMlWzh4rJbE3tF/KbJ0PIVBBJg8LIlnh07mr8UH+O+CLXz7hQ08/uY27p06gpsnpNLtLK7Kcc6xcfcRXvMCYMu+KgBGDe7H1688j2vHDOT8gX11l2sn9bf7DXZWcs1oTZok4acwiBBmxqUjBnDJ8CTe+rCChws+5JvPreOxN4u5d+oIpo8b8olLNevqG1i98yCvbdrLaxv3UXboODEGuVkB/uPGUVwzamDEjO/f1Y1N6098XIzCQNqNwiDCmBlTzk/h8vOSeWNzOQ8XfMi/Lnyfx5YV87WrRnD1qIH8tfgAr23cy+ub93Gw+iTxcTFcNmIAX7tyBFdekKJhMCJQj26xjEtL0GQ30m4UBhHKzLhq1ECuvCCFJRv38fPXP+RrC94jxqDBhYZzuHJkCteMHsTl5yV3qnsWpHXyshP51VvbOVZTp39PCbsW/6LM7EngRqDcOTfGa5sJfBe4AMh3zhU2Wv4B4E6gHrjXObfEa58G/AKIBZ5wzv3Qa88GFgABYA1wh3Pu4wPVSLPMjGljBnHNqIG8smEv7wcPcenwAUwa2rXG8OkK8rOTeGzZNtbsOsinRiT7XY5EmbP5tPgdMO20tg3AZ4HljRvNbBRwKzDaW+eXZhZrZrHAY8B1wCjgNm9ZgB8BP3POjQAOEgoSOUcxMcYNYwfzresv4LLzkhUEUWhCRgIxFhq0TiTcWvzEcM4tBypPa9vsnNvSxOLTgQXOuRrn3A6gGMj3foqdc9u9b/0LgOkWunRlKrDIW38ecFOr90YkivXt0Y1RQ/ppnCJpF+H++pgKlDZ6HvTammtPAg455+pOa2+Smc0xs0IzK6yoqAhr4SKRID8ribW7DlFTV+93KRJlwh0GTV2k7lrR3iTn3FznXK5zLjc5WedMpevJz06kpq6BDWWH/S5Foky4wyAIpDd6ngbsPkP7fiDBzOJOaxeRJpy6+WzVjoM+VyLRJtxh8BJwq5l1964SGgGsAlYDI8ws28ziCXUyv+RCM+ssA2Z4688GXgxzTSJRI6lPd4Yl92bVjgN+lyJRpsUwMLNngBXA+WYWNLM7zexmMwsCk4E/m9kSAOfcRuBZYBPwKnC3c67e6xO4B1gCbAae9ZYFuA/4FzMrJtSH8Jvw7qJIdMnPDlBYcpD6hsicpVA6pxbvM3DO3dbMSy80s/yDwINNtC8GFjfRvp3Q1UYichbyswM8s6qULXurGDWkn9/lSJTQxegiEeajfgOdKpLwURiIRJi0xF4M6d+D1TvViSzhozAQiUD52QHe3VFJ6BoMkbZTGIhEoLzsAPuP1rDzQLXfpUiUUBiIRKD8U5PdaJwiCROFgUgEGp7Sh0DveN5VGEiYKAxEIpCZkZuZyGoNWidhojAQiVD52QF2VVaz9/AJv0uRKKAwEIlQ+dne/QY6OpAwUBiIRKhRg/vROz5WncgSFgoDkQgVFxvDhMxEVikMJAwUBiIRLD8rwJZ9VRyq1rTh0jYKA5EIluf1GxRqaAppI4WBSAQbl55AfGyMOpGlzRQGIhGsR7dYxqb1V7+BtJnCQCTC5WUH2FB2mOraOr9LkQimMBCJcPnZAeoaHGt3HfK7FIlgCgORCJeTmYgZOlUkbaIwEIlw/Xp0Y9TgfhqnSNpEYSASBfKyAqzZdZDauga/S5EIpTAQiQL52QFOnGxgw+7DfpciEUphIBIF8rzJbtRvIK2lMBCJAsl9uzN0QG8NWietpjAQiRJ5WQFW76ykocH5XYpEIIWBSJTIzw5w5EQdW/ZV+V2KRCCFgUiUODXZjS4xldZQGIhEibTEngzq10OdyNIqCgORKGFm5GcHWLWjEufUbyDnRmEgEkXysgOUV9Wwq7La71IkwrQYBmb2pJmVm9mGRm0BMysws63e70Sv3czsETMrNrN1Zjah0TqzveW3mtnsRu05ZrbeW+cRM7Nw76RIVzExW/cbSOuczZHB74Bpp7XdD7zhnBsBvOE9B7gOGOH9zAEeh1B4AN8BJgL5wHdOBYi3zJxG653+XiJyloYn9yGhVzeFgZyzFsPAObccOP0vazowz3s8D7ipUft8F7ISSDCzwcC1QIFzrtI5dxAoAKZ5r/Vzzq1woZOc8xttS0TOUUyMkZsZ0BVFcs5a22cw0Dm3B8D7neK1pwKljZYLem1nag820d4kM5tjZoVmVlhRUdHK0kWi28TsADsPVFN+5ITfpUgECXcHclPn+10r2pvknJvrnMt1zuUmJye3skSR6JZ3qt9ARwdyDlobBvu8Uzx4v8u99iCQ3mi5NGB3C+1pTbSLSCuNHtKPnt1iNU6RnJPWhsFLwKkrgmYDLzZqn+VdVTQJOOydRloCXGNmiV7H8TXAEu+1KjOb5F1FNKvRtkSkFbrFxpCTmci7CgM5B2dzaekzwArgfDMLmtmdwA+Bq81sK3C19xxgMbAdKAb+F7gLwDlXCXwfWO39fM9rA/hH4AlvnW3AK+HZNZGuKy8rwJZ9VRw+ftLvUiRCxLW0gHPutmZeurKJZR1wdzPbeRJ4son2QmBMS3WIyNnLy07EOSgqqWTqyIF+lyMRQHcgi0Sh8emJdIs1nSqSs6YwEIlCPeNjuTC1vzqR5awpDESiVH52EuvLDnO8tt7vUiQCKAxEolR+diIn6x1rSw/6XYpEAIWBSJTKyQxgBqt3KAykZQoDkSjVv2c3Rg7qx6qdB/wuRSKAwkAkiuVnJbKm5BAn6xv8LkU6OYWBSBTLyw5w/GQ9G3cf8bsU6eQUBiJRLD/r1GQ3OlUkZ6YwEIliKf16kJXUi1XqRJYWKAxEolx+doDCkkoaGpodHV5EYSAS7fKyAhyqPsnW8qN+lyKdmMJAJMrla7IbOQsKA5EolxHoxcB+3VmlcYrkDBQGIlHOzMjLCrB6RyWhUeZFPklhINIF5GcH2HvkBMGDx/0uRTophYFIF3Cq30DzG0hzFAYiXcB5KX3p37Ob5jeQZikMRLqAmBgjNzOR1bqiSJqhMBDpIvKzA2zff4zyqhN+lyKdkMJApIvI8/oNCndqaAr5JIWBSBcxZkh/enaL1f0G0iSFgUgXER8Xw/iMBIWBNElhINKF5GUF2Lz3CEdOnPS7FOlkFAYiXcjE7ADOQZH6DeQ0CgORLmR8RiJxMaZB6+QTFAYiXUjP+FjGpPbXzWfyCQoDkS5mYnaA94OHOHGy3u9SpBNRGIh0MXlZAU7WOx5/cxsHj9X6XY50Em0KAzP7mpltMLONZvZ1ry1gZgVmttX7nei1m5k9YmbFZrbOzCY02s5sb/mtZja7bbskImdy8fAkcjMT+cUbW5n40Bvc9XQRyz4op66+we/SxEfW2vHNzWwMsADIB2qBV4F/BL4CVDrnfmhm9wOJzrn7zOx64J+A64GJwC+ccxPNLAAUArmAA4qAHOfcGS93yM3NdYWFha2qXURg4+7DLCoK8qe1ZRysPklK3+7cPCGVmTnpDE/p43d50k7MrMg5l3t6e1wbtnkBsNI5V+29wVvAzcB0YIq3zDzgTeA+r32+C6XPSjNLMLPB3rIFzrlKbzsFwDTgmTbUJiItGD2kP6OH9OeB6y5g6Qf7WFgY5Im3d/Drt7YzPiOBmTnp3HjRYPr16OZ3qdIB2hIGG4AHzSwJOE7oG38hMNA5twfAObfHzFK85VOB0kbrB7225to/wczmAHMAMjIy2lC6iJwSHxfDtDGDmTZmMOVVJ3hhTRkLi4J864X1fO/ljUwbPYiZuelMHppETIz5Xa60k1aHgXNus5n9CCgAjgLvA3VnWKWpvyJ3hvam3nMuMBdCp4nOqWARaVFK3x78w+XDmHPZUN4PHmZhYSkvvb+bP723m9SEnnxuQiozctLJSOrld6kSZm05MsA59xvgNwBm9hChb/X7zGywd1QwGCj3Fg8C6Y1WTwN2e+1TTmt/sy11iUjbmBnj0hMYl57Af9w4iiUb97KoKMj/LCvmkaXFTMwOMDM3nesvHESv+DZ9jEgn0eoOZAAzS3HOlZtZBvAaMBn4FnCgUQdywDn3TTO7AbiHjzqQH3HO5XsdyEXAqauL1hDqQD7jXTHqQBbpeLsPHef5NUEWFgUpOVBN7/hYbhg7mJm56eRmJmKm00idXXMdyG0Ng7eBJOAk8C/OuTe8PoRngQxgFzDTOVdpob+SRwl1DlcDX3TOFXrb+RKhEAF40Dn325beW2Eg4h/nHKt3HmRhYSl/Xr+H6tp6spJ6MSMnjc9OSGNIQk+/S5RmtEsY+ElhINI5HKupY/H6PSwqCvLujkrM4NLhA5iZm841owbSo1us3yVKIwoDEWl3JQeO8VxRkOfWlFF26Dj9esTx6YuGMDM3nYvS+us0UiegMBCRDtPQ4Hhn2wEWFpXy6oa91NQ1cN7APszISeOm8amk9O3hd4ldlsJARHxx+PhJ/rxuDwuLSlm76xCxMcYV5yczIyedqSNTiI/TEGkdSWEgIr4rLq9iYVGQ59eUUVFVQ6B3PDeNS2VGThqjhvTzu7wuQWEgIp1GXX0Dy7dWsLAwyOub93Gy3jF6SD9m5qQxfVwqib3j/S4xaikMRKRTOnislhffCw2BsXH3EeJjY7hqVAozc9L51IgBxMXqNFI4KQxEpNPbtPsIC4tKefG93VQeq9VIqu1AYSAiEaO2roGlH+xjUVGQZVsqqG9wGkk1TBQGIhKRyqtO8Ke1ZSwsDLK1/Cjd42KYNmYQM3PSuXiYRlI9VwoDEYlozjneDx5mUVEpL723myMn6jSSaisoDEQkapw4Wc9rm/axsLCUvxTvxzk0kupZUhiISFQ6NZLqoqIgOxuNpDojJ528LI2kejqFgYhENecchSXeSKrr9nBMI6k2SWEgIl3GsZo6Xtmwl4WFpRpJ9TQKAxHpkjSS6scpDESkS2tocKzYfoCFhaW84o2kOiKlDzNzu9ZIqgoDERHPkRMnefn9j4+kOuW8ZNnI9UcAAAhiSURBVGbmpjF15MCoHklVYSAi0oTi8ioWFZXx/Jog5d5IqtPHDWFmTnpUjqSqMBAROYO6+gbe3rqfhUWlvL6pnNr6hqgcSVVhICJylpobSXVGThqXjUiO6JFUFQYiIq2wafcRFhUF+dN7ZVExkqrCQESkDUIjqZazqKg0okdSVRiIiIRJedUJXly7m4VFpXy4L7JGUlUYiIiEmXOOdcHDLIygkVQVBiIi7ejUSKqLioK8vbXiYyOpXjdmEL27d46RVBUGIiIdZM/h4zy/poyFhaV/G0n1+gsHMzPX/5FUFQYiIh2sM46kqjAQEfFRdW0dr6zfy8KiUlZu/2gk1Rk5aVw7elCHjaTaLmFgZv8MfBlwwHrgi8BgYAEQANYAdzjnas2sOzAfyAEOALc453Z623kAuBOoB+51zi1p6b0VBiISqXYdqGbRmiDPFQUpO3Scvj3i+EwHjaQa9jAws1TgL8Ao59xxM3sWWAxcDzzvnFtgZr8C3nfOPW5mdwFjnXNfNbNbgZudc7eY2SjgGSAfGAK8DpznnKs/0/srDEQk0vkxkmpzYdDWe6rjgJ5mFgf0AvYAU4FF3uvzgJu8x9O953ivX2mh+JsOLHDO1TjndgDFhIJBRCSqxcQYlwwfwM9vHc/qf7+KH3z2Qvr2iOOhxR8w+QdLufN3q3l1wx5q6xravZZWX+vknCszs58Cu4DjwGtAEXDIOVfnLRYEUr3HqUCpt26dmR0Gkrz2lY023XgdEZEuoV+PbtyWn8Ft+RkUlx9lUVGQ59cEeeOD8g4ZSbXVRwZmlkjoW302odM7vYHrmlj01Hmopk6CuTO0N/Wec8ys0MwKKyoqzr1oEZEIMDylD/dfN5J37p/Kb/8+j8lDk3h65S6uf+RtbnjkbcqrToT9PdtyF8RVwA7nXAWAmT0PXAwkmFmcd3SQBuz2lg8C6UDQO63UH6hs1H5K43U+xjk3F5gLoT6DNtQuItLpxcXGcMXIFK4YmcLBY7W89P5u3tm2n+Q+3cP+Xm3pM9gFTDKzXt65/yuBTcAyYIa3zGzgRe/xS95zvNeXulDv9UvArWbW3cyygRHAqjbUJSISdRJ7xzP74ix+fUduu1xt1JY+g3fNbBGhy0frgLWEvrX/GVhgZv/ltf3GW+U3wFNmVkzoiOBWbzsbvSuRNnnbubulK4lERCS8dNOZiEgX0l6XloqISBRQGIiIiMJAREQUBiIigsJARERQGIiICBF8aamZVQAlftdxlgYA+/0uop1o3yJXNO+f9q15mc655NMbIzYMIomZFTZ1XW800L5FrmjeP+3budNpIhERURiIiIjCoKPM9buAdqR9i1zRvH/at3OkPgMREdGRgYiIKAxERASFQbsxs3QzW2Zmm81so5l9ze+aws3MYs1srZm97Hct4WZmCWa2yMw+8P4NJ/tdU7iY2T97f5MbzOwZM+vhd01tYWZPmlm5mW1o1BYwswIz2+r9TvSzxtZqZt9+4v1drjOzF8wsIRzvpTBoP3XAvzrnLgAmAXeb2Sifawq3rwGb/S6infwCeNU5NxK4iCjZTzNLBe4Fcp1zY4BYvImmItjvgGmntd0PvOGcGwG84T2PRL/jk/tWAIxxzo0FPgQeCMcbKQzaiXNuj3Nujfe4itCHSaq/VYWPmaUBNwBP+F1LuJlZP+AyvFn6nHO1zrlD/lYVVnFAT28u8l40M+d4pHDOLSc0e2Jj04F53uN5wE0dWlSYNLVvzrnXvDnmAVYSmje+zRQGHcDMsoDxwLv+VhJWPwe+CTT4XUg7GApUAL/1ToM9YWa9/S4qHJxzZcBPCc1hvgc47Jx7zd+q2sVA59weCH0xA1J8rqe9fAl4JRwbUhi0MzPrAzwHfN05d8TvesLBzG4Eyp1zRX7X0k7igAnA48658cAxIvc0w8d4586nA9nAEKC3mX3B36qkNczs24RORz8dju0pDNqRmXUjFARPO+ee97ueMLoE+IyZ7QQWAFPN7Pf+lhRWQSDonDt1JLeIUDhEg6uAHc65CufcSeB54GKfa2oP+8xsMID3u9znesLKzGYDNwK3uzDdLKYwaCdmZoTOOW92zj3sdz3h5Jx7wDmX5pzLItT5uNQ5FzXfLp1ze4FSMzvfa7oS2ORjSeG0C5hkZr28v9EriZLO8dO8BMz2Hs8GXvSxlrAys2nAfcBnnHPV4dquwqD9XALcQehb83vez/V+FyVn7Z+Ap81sHTAOeMjnesLCO9pZBKwB1hP6DIjooRvM7BlgBXC+mQXN7E7gh8DVZrYVuNp7HnGa2bdHgb5Agfe58quwvJeGoxARER0ZiIiIwkBERBQGIiKCwkBERFAYiIgICgORJpmZM7OnGj2PM7OK1o7Q6o2Celej51OicbRXiVwKA5GmHQPGmFlP7/nVQFkbtpcA3NXiUiI+URiINO8VQiOzAtwGPHPqBW+8/D95Y8qvNLOxXvt3vTHo3zSz7WZ2r7fKD4Fh3k1CP/Ha+jSaM+Fp745gEV8oDESatwC41Zv8ZSwfH3X2/wFrvTHlvwXMb/TaSOBaIB/4jjdG1f3ANufcOOfcN7zlxgNfB0YRGin1kvbcGZEzURiINMM5tw7IInRUsPi0ly8FnvKWWwokmVl/77U/O+dqnHP7CQ2QNrCZt1jlnAs65xqA97z3EvFFnN8FiHRyLxEa/38KkNSovalTOqfGdqlp1FZP8//PznY5kXanIwORM3sS+J5zbv1p7cuB2yF0ZRCwv4X5KqoIDS4m0inpm4jIGTjngoTmQz7ddwnNhLYOqOaj4ZKb284BM/urN7H5K8Cfw12rSFto1FIREdFpIhERURiIiAgKAxERQWEgIiIoDEREBIWBiIigMBAREeD/Azhh/Ls2dKZsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s['twp'].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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23c8bd92908>"
      ]
     },
     "execution_count": 22,
     "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": [
    "sb.lmplot(x = 'Month', y = 'twp', data = s.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": [
    "df['Date']=df['timeStamp'].apply(lambda t: t.date())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now groupby this Date column with the count() aggregate and create a plot of counts of 911 calls.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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()['twp'].plot()\n",
    "mp.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": 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']=='Traffic'].groupby('Date').count()['twp'].plot()\n",
    "mp.title('Traffic')\n",
    "mp.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']=='Fire'].groupby('Date').count()['twp'].plot()\n",
    "mp.title('Fire')\n",
    "mp.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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()['twp'].plot()\n",
    "mp.title('EMS')\n",
    "mp.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": 34,
   "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",
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       "      <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>0</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>1</th>\n",
       "      <td>269</td>\n",
       "      <td>240</td>\n",
       "      <td>186</td>\n",
       "      <td>170</td>\n",
       "      <td>209</td>\n",
       "      <td>239</td>\n",
       "      <td>415</td>\n",
       "      <td>655</td>\n",
       "      <td>889</td>\n",
       "      <td>880</td>\n",
       "      <td>...</td>\n",
       "      <td>943</td>\n",
       "      <td>938</td>\n",
       "      <td>1026</td>\n",
       "      <td>1019</td>\n",
       "      <td>905</td>\n",
       "      <td>731</td>\n",
       "      <td>647</td>\n",
       "      <td>571</td>\n",
       "      <td>462</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>250</td>\n",
       "      <td>216</td>\n",
       "      <td>189</td>\n",
       "      <td>209</td>\n",
       "      <td>156</td>\n",
       "      <td>255</td>\n",
       "      <td>410</td>\n",
       "      <td>701</td>\n",
       "      <td>875</td>\n",
       "      <td>808</td>\n",
       "      <td>...</td>\n",
       "      <td>904</td>\n",
       "      <td>867</td>\n",
       "      <td>990</td>\n",
       "      <td>1037</td>\n",
       "      <td>894</td>\n",
       "      <td>686</td>\n",
       "      <td>668</td>\n",
       "      <td>575</td>\n",
       "      <td>490</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
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       "      <td>828</td>\n",
       "      <td>...</td>\n",
       "      <td>876</td>\n",
       "      <td>969</td>\n",
       "      <td>935</td>\n",
       "      <td>1013</td>\n",
       "      <td>810</td>\n",
       "      <td>698</td>\n",
       "      <td>617</td>\n",
       "      <td>553</td>\n",
       "      <td>424</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "  </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",
       "0            282  221  201  194  204  267  397  653  819  786  ...  869  913   \n",
       "1            269  240  186  170  209  239  415  655  889  880  ...  943  938   \n",
       "2            250  216  189  209  156  255  410  701  875  808  ...  904  867   \n",
       "3            278  202  233  159  182  203  362  570  777  828  ...  876  969   \n",
       "4            275  235  191  175  201  194  372  598  742  752  ...  932  980   \n",
       "\n",
       "Hour           16    17   18   19   20   21   22   23  \n",
       "Day of Week                                            \n",
       "0             989   997  885  746  613  497  472  325  \n",
       "1            1026  1019  905  731  647  571  462  274  \n",
       "2             990  1037  894  686  668  575  490  335  \n",
       "3             935  1013  810  698  617  553  424  354  \n",
       "4            1039   980  820  696  667  559  514  474  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayHour = df.groupby(by=['Day of Week','Hour']).count()['Reason'].unstack()\n",
    "dayHour.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c8c122148>"
      ]
     },
     "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": [
    "mp.figure(figsize=(12,6))\n",
    "sb.heatmap(dayHour,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a clustermap using this DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x23c8c1fd288>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQBYd6rquWPV3jhWYcaxsLCwJcmm1ezhAXnyau4eAJgjVbU5yeZu0bbW2rZlbPqKJG8coycBef3ZtLi4uHU1G3jyd7z8otXcPwAwP4YwvGQgrqqP72WzSvKgsXoSkAEAWKselOQHknxlj+WV5P1j7VRABgBgrfqLJMe21q7Y84qqumysnQrIAACsSa215+/jup8Ya7+OYgEAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAnY2r3QDrTzv8sHEKH33kKGXrtjtHqZudu0YpW0eOczskSTaO85Q/7L42St37TzpqlLqbNo70mEiSu0d6fhy7Y5Sym24ZpWzaSDfDFV89c5zCSbYfd9UodQ/POM+P23YeO0rdw2vnKHWT5MNf/eZR6p5yxNdGqfuR275plLobN4zz9yNJTj7yrlHq3r7z6FHqrlVGkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQGfjajdwKFtYWNiSZNOUm501QisAAAwE5NW1aXFxces0GywsLEy1PgAA0zHFAgAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAzsbVbgAAAJZSVd+a5PwkpydpSa5Pcmlr7eox92sEGQCANaeqXprkLUkqyYeTfGQ4/+aq2jLmvo0gr3MLCwtbkmw6mPs8JU87mLsDAOZYVW1OsrlbtK21ti3J85P8m9ba/Xus/9tJPpHk4rF6EpDXv02Li4tbD+YOf+Cciy46mPsDAObXEIa3LXHVriTfmOQLeyw/bbhuNAIyAABr0UuSvK+qPpPkumHZg5N8S5KfG3PHAjIAAGtOa+3dVfXwJOdm8iG9SrI9yUdaazvH3LeADADAmtRa25Xkgwd7v45iAQAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAACdjavdAOvPhq/dO1LhGqdua6OU3fHlL49S97DjjhulbpLUUZtGqbtr4zj3XRup7i23HztK3SSpE+4bpe4RR98/St17Tj18lLpH3TjOfffwY24ape6YHrxxnOf05+994Ch1Tzv89lHqJskDj/jqKHUfdPgdo9Q9/ogHjVL3vl3jxa9b7xnn9e3w43aOUnetEpDXn3sWFha2dpfPWqU+AADmkoC8ziwuLl7cX94jLAMAsELmIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAwJpUVd9aVedV1bF7LH/ymPsVkAEAWHOq6kVJ3pnkhUmuqqrzu6t/Y8x9bxyzOAfFPQsLC1sP5g4fmO87mLsDAOZYVW1OsrlbtK21ti3Jf0hyTmvta1V1VpK3VdVZrbXXJqkxexKQ17nFxcWLD/Y+n/KILRcd7H0CAPNpCMPblrjqsNba14Z1rq2qJ2YSkr8pIwdkUywAAFiLbqyqs3dfGMLy05M8IMn/NeaOBWQAANaiZye5sV/QWtvRWnt2kieMuWNTLAAAWHNaa9v3cd0/jrlvI8gAANARkAEAoCMgAwBAR0AGAIDOAX9Ib2FhYUuSTTPs5VB01mo3AADAv7aSo1hsWlxc3DqrRg5FB/sb8AAA2D9TLAAAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAZ+NqN8D6s+voI0epu2H7TaPUHcuGo48epW6dfOIodZNkxxeuG6XukXc8ZJS6O48Y5z38YRvvH6Vukuz88jjPj53Xbxql7oaRbopNt7VR6v79rd8ySt0keeRR20epe/KGW0ep+7Tjrxyl7j/c9fBR6ibJo46+dpS6H//6g0epe8zG+0ape9KGu0epmySfuuPUUep+8PaHjlI3SV48WuUDZwQZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAMDcqaqTl1i2rAP3C8gAAMyjd1XV8bsvVNW3J3nXcjYUkAEAmEe/kUlIPraqzkny1iQ/tZwNfdU0AABzp7X2l1V1eJL3JDkuyQ+11j6znG0FZAAA5kZV/fckrVt0fJLPJXlhVaW19qL91RCQAQCYJ4t7XP7otAUEZAAA5kZr7ZLd56vqqCQPbq19apoaPqQHAMDcqaofTHJFkncPl8+uqkuXs62ADADAPNqa5NwktydJa+2KJI6DDADAIWtHa+2OPZa1JdfcgznIAADMo6uq6ieSHFZVD0vyoiTvX86GRpABAJhHL0zyb5Lcm+SPk9yR5CXL2dAIMgAAc6e1dneSl1XVb7TW7ppmWyPIAADMnap6XFX9c5Krh8vfWVX/73K2FZABAJhHr0nyA0m+nCSttSuTPGE5GwrIAADMpdbadXss2rmc7cxBBgBgHl1XVY9L0qrqiEyOYnH1cjY0ggwAwDz62SQvSHJ6ku1Jzh4u75cRZAAA5kZVndRa+0pr7dYkP3kgNQRkAADmyaeq6pZMvhTkH5O8v7X26WkKmGIBAMDcaK2dmuSHMwnHj0vy9qq6qareWVW/tJwaRpABAJgrw4jxp5P8QVV9c5KnJnlxku9P8qr9bS8gAwAwN4YjVzwuyXcnOTPJ55J8MMlPJbl8OTUEZAAA5sk/ZBKEfzvJnw9fOT0VARkAgHnyjZmMID8uyc9W1cZMAvMHknygtfa5/RUQkAEAmButtRuTvH04paqOTvK8JK9I8pAkh+2vhoAMAMDcqKoTMpl/vHsU+VFJrknyrkyObLFfAjIAAPPkmkw+lPf+JL+a5MOtta9PU0BABgBgbrTWHrjSGgIyAABrUlWdm6S11j5SVd+e5MlJPtla+6sx9ysgAwCw5lTVRUmekmRjVb03yWOSXJZkS1U9qrX262PtW0BeQxYWFrYk2bTafezPKXnaarcAAMyJqtqcZHO3aFtrbVuSZyY5O8mRSW5MckZr7c6q+s0kH0qyZECuqle21l5aVRe01t56ID0JyGvLpsXFxa2r3cT+/MCjLrpotXsAAObDEIa3LXHVjtbaziR3V9VnW2t3Dut/vap27aPkU6vq5Ul+OYmADADA3Livqo4evgnvnN0Lh8O47SsgvzvJrUmOqao7k1SStvtna+34/e14w4raBgCAcTxh99dEt9b6QHx4kgv3tlFr7Rdbayck+cvW2vGtteP6n8vZsRFkAADWnNbavXtZfmsmI8T72/78qnpQku8aFn2otXbLcvZtBBkAgLlTVRck+XCSC5L8aJIPV9Uzl7OtEWQAAObRy5N8V2vt5iSpqgcm+V9J3ra/DY0gAwAwjzbsDseDL2eZ2dcIMgAA8+jdVfXXSd48XP6xJMv6Bj4BGQCAudNa+8Wq+pEk35PJId62tdbesZxtBWQAAOZSa+3tSd4+7XbmIAMAQEdABgCAjoAMAMDcqaqnV9UBZV0BGQCAefSsJJ+pqldV1bdNs6GADADA3Gmt/VSSRyX5bJI3VtUHqmpzVR23v20FZAAA5lJr7c4kf5bkLUlOS/LDSS6vqhfuazsBGQCAuVNVP1hV70jyN0kOT3Jua+0pSb4zyS/sa1vHQQYAYB5dkOQ1rbW/7xe21u6uqufta0MBGQCAudNae/Y+rnvfvrY1xQIAgLlTVY+tqo9U1deq6r6q2llVdy5nWwEZAIB59DtJfjzJZ5IcleRnkvz35WxoigUAAHOptXZNVR3WWtuZyaHe3r+c7QRkAADm0d1VdUSSK6rqVUluSHLMcjY0xQIAgHn005lk3Z9LcleSM5P8++VsaAQZAIC501r7QlU9cDj/imm2NYIMAMDcqImtVXVrkk8m+XRV3VJVv7LcGgIyAADz5CVJHp/ku1prp7TWTkrymCSPr6qfX04BARkAgHny7CQ/3lr7/O4FrbXPJfmp4br9EpABAJgnh7fWbt1zYWvtliSHL6eAgAwAwDy57wCv+xeOYgEAwDz5zr18pXQl2bScAgIyAABzo7V22EprmGIBAAAdARkAADoCMgAAdARkAADoCMgAANBxFIvVdc/CwsLW7vJZq9THVKq1Ueq2nTtHqVsnHDdK3bHeXbbblzoyzWxsOHJZR7eZWjusRql7x0NHeolq945TN0kdf/8odXftGOc23rBjnEfyXaeN02/uPnqcukl+8Oi7R6n7iXEeEtlUO0apuzMj3XdJrrn3G0ap+22bvjRK3b/40iNHqXvGsXeMUjdJvuuUL45S94Z7Thil7lolIK+ixcXFi/vLe4RlAABWgSkWAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwCwLlTVmw7GfjYejJ0AAMA0qurSPRcl+XdVdWKStNaeMda+BeS15Z6FhYWtq93E/jwgT13tFgCAOVFVm5Ns7hZta61tS3JGkn9O8vtJWiYBeSHJb43dk4C8hiwuLl682j0sx5PP/pWLVrsHAGA+DGF42xJXLSR5cZKXJfnF1toVVfX11trfjd2TgAwAwJrTWtuV5DVV9dbh5005SNlVQAYAYM1qrW1PckFVPS3JnQdjnwIyAABrXmvtL5P85cHYl8O8AQBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBn42o3wPrztYceP0rd427/6ih1d5x28ih1N163c5S6OfGUceomqV3j1D32U7eNUvfOMx84St0xtbsOH6Xuaf84StncfM44dcd6rO34i/EeE//5tEePUvcjtz54lLoPPOquUepuqDZK3SQ55Yhxev6bWx8xSt3rrxvn78eXTzhmlLpJcuvJR49S94jDRvqbt0YZQQYAgI6ADHRE0eEAAA9bSURBVAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdDaudgMAALA/VfU9Sc5NclVr7T1j7ktAnsLCwsKWJJtWu4/VtunBz1rtFgCAOVFVm5Ns7hZta61tq6oPt9bOHdb5D0lekOQdSS6qqke31i4eqycBeTqbFhcXt652E6vte37k1Retdg8AwHxorW1Lsm2Jqw7vzm9O8n+31m6pqlcn+WASARkAgEPKhqo6KZPPzFVr7ZYkaa3dVVU7xtyxgAwAwFp0QpKPJqkkraq+obV2Y1UdOywbjYAMAMCa01o7ay9X7Uryw2PuW0AGAGDdaK3dneTzY+7DcZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANDZuNoNsP60sd5WbRzp4VjjlM2RR4xSth15+Ch1k6Tu2zFK3Z3HbBql7imfuHeUup87Z5z7Lkk23XjYKHV3bdw1St1jvzhK2Rzx1TZK3XtOGesJnbzj42ePUvfo4+8Zpe5NXzl+lLoPfsBto9RNko994YxR6taGcR5vdd84f/DuvfPIUeomyfZ20ih1d9w3zmvbWrWSRHLPwsLC1lk1sk6ctdoNAAAwrgMOyIuLixfPspH14BB8QwAAcMgxBxkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAANacqnpMVR0/nD+qql5RVe+qqldW1Qlj7ltABgBgLXpDkruH869NckKSVw7L3jjmjjeOWXwO3bOwsLB1tZtYbUee9azVbgEAmBNVtTnJ5m7RttbatiQbWms7hmULrbVHD+f/oaquGLMnAXkKi4uLF692D2vB45/56otWuwcAYD4MYXjbElddVVXPba29McmVVbXQWlusqocnuX/MnkyxAABgLfqZJN9bVZ9N8u1JPlBVn0vye8N1ozGCDADAmtNauyPJc6rquCQPzSS3bm+t3TT2vgVkAADWrNbaV5NceTD3aYoFAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAPRaa05Oo52SbFZ3ffa83uqux57XW9312PN6q7see15vdddjz+oe/JMRZMa2Wd3Ra6s7fm11x6+t7vi11R2/trrj1j1oBGQAAOgIyAAA0BGQGds2dUevre74tdUdv7a649dWd/za6o5b96CpYTI1AAAQI8gAAPCvCMgAANARkBlFVT25qj5VVddU1ZYZ1762qv6pqq6oqsUZ1XxDVd1cVVfNot4S9Q+rqo9V1V/MqN6ZVfW3VXV1VX2iql48i7pD7U1V9eGqunKo/YoZ1j6xqt5WVZ8cev/uA6yz5O9fVb9aVR8fHhvvqapvPIDa/7/HQlV9Z1V9YHjcvauqjp9hzydX1Xur6jPDz5Omrd3t4xHD7777dGdVveQAa+2t3wuGy7uqamGGdX9zeFx8vKreUVUnHkjfe+zr54d9XFVVb66qTQdYZ8nXh6p64fA694mqetUs6lbVn3T337VVdcWseq6qrVX1pa7+U2dUd8X33V7qjvJ87q77hapqVfWAaesuUevFw+PsEwf6nOtqLXVbrPh1Yi91V/R83kfds6vqg8N9t1hV5x5I7VW12gdidpq/U5LDknw2yUOTHJHkyiTfPsP61yZ5wIx7fkKSRye5aqTb5D8l+eMkfzGjeqclefRw/rgkn57VbZykkhw7nD88yYeSPHZGtS9J8jPD+SOSnDjL3z/J8d06L0ryu7N4LCT5SJLvHc4/L8mvzrDnVyXZMizfkuSVM7qtD0tyY5JvmvFt/G1JHpHksiQLM6z7/Uk2DstfudLbIcnpST6f5Kjh8p8mec4B1lrqMfHvkvyvJEcOl0+dRd09rv+tJL8yw563JvmFFd6uS9Vd8X23l7qjPJ+H5Wcm+eskX8gK/54keWSSq5IcnWTj8Lh42Ixv4xW/Tuyl7oqez/uo+54kTxnOPzXJZSu5jVfjZASZMZyb5JrW2udaa/cleUuS81e5p31qrf19ktvGqF1VZyR5WpLfn1XN1toNrbXLh/NfTXJ1JoFgFrVba+1rw8XDh9OKP807jLo+Icnrh/3c11q7/QB7XPL3b63d2a12TA6g7708Fh6R5O+H8+9N8u9n1XMmz41LhtUuSfJD09bei/OSfLa19oUD2Xgft/HVrbVPHWhT+6j7ntbajmG1DyY540D30dmY5Kiq2phJeLn+AHte6jHxH5Nc3Fq7d1jn5hnVTZJUVSX50SRvnrbu/mqvxFJ1Z3Hf7aXuWM/nJHlNkl86kJpL+LYkH2yt3T3cDn+X5IcPtNheel7x68RebuMVPZ/3VjeT23X3f9pOyAE+91aTgMwYTk9yXXd5e2YU3gYtyXuq6qNVtR6+ree/ZvJCvGuM4lV1VpJHZTLSO6uahw3/2r05yXtba7Oo/dAktyR5Y02mm/x+VR2z0qJ7/v5V9etVdV2Sn0zyKyutP7gqyTOG8xdkMvp0wPbo+UGttRuSSXhMcupKaneelQMMV3sa4zG2n7rPS/I/V1K7tfalJK9O8sUkNyS5o7X2npXU3MPDk/zbqvpQVf1dVX3XDGsnyb9NclNr7TMzrvtzw7SFN6xkOs8+rPi+643xfK6qZyT5UmvtylnUy+T14QlVdUpVHZ3JiOmKXiOWMNbrxFhekuQ3h/vu1Ul+eZX7mZqAzBhqiWWzPJ7g41trj07ylCQvqKonzLD2TFXV05Pc3Fr76Ej1j03yZ0lessdoy4q01na21s7OZCTo3Kp65AzKbszk33Cva609Ksldmfyr8IAt9fu31l7WWjszyR8l+bmVtfwvnpfJY+2jmUwLuO9AC411n+2xjyMyCfRvnUGtUfrdW92qelmSHZncfyupf1Imo24PSfKNSY6pqp9aSc09bExyUpLHJvnFJH86jPrOyo9nRm9wOq9L8s1Jzs7kTcNvzbL4rO673qyfz0OAfVlm9+Y5rbWrM5la8t4k785kWuGOfW40//5jkp8f7rufz/Cfw/VEQGYM2/Ov3z2fkRn+e6W1dv3w8+Yk78hkSsda9fgkz6iqazOZavKkqvofsyhcVYdnEjD+qLX29lnU3NMwBeKyJE+eQbntSbZ3o9FvyyQwH5Bl/P5/nAOYCrGU1tonW2vf31o7J5PQ8tkDqbOXnm+qqtOG60/LZNR+pZ6S5PLW2k0rKTLWY2xvdavqwiRPT/KTrbWVvqn+viSfb63d0lq7P8nbkzxuhTV725O8fZiS9OFM/kO04g97JckwJeRHkvzJLOrt1lq7aXjzuyvJ72WGr50zvu+WMqvn8zdn8qbpyuF1+Ywkl1fVN6ykaGvt9a21R7fWnpDJdINZj/yP8Toxpgszec4lkzfqa/nv9JIEZMbwkSQPq6qHDCNZz0py6SwKV9UxVXXc7vOZfDhklCNPzEJr7Zdba2e01s7K5Hb4m9baikexhpGq1ye5urX22yutt0ftB+7+FHpVHZVJ0PjkSuu21m5Mcl1VPWJYdF6Sfz7AHpf8/avqYd1qz8gM+h7qnjr83JDk5Ul+9wBq7O0+uzSTPyYZfr5zZd0mmcHo41iPsX3cd09O8tIkz2it3T2DXX0xyWOr6uhhn+dlMt95Vv48yZOSpKoensmHTm+dUe3vS/LJ1tr2GdVL8i/BarcfzoxeO0e473bXnfnzubX2T621U1trZw2vy9sz+dDojSup271GPDiTNzezHv0f43ViTNcn+d7h/JMy+zcM49vzU3tOTrM4ZTIH69OZjLS9bIZ1H5rJv6+uTPKJWdXO5MXshiT3Z/KC+fwRbpMnZnZHsfieTKatfDzJFcPpqTOq/R1JPjbUvioH+Cn6vdQ+O8niUPvPk5w0y98/k1HJq4bl78rkw18rfiwkefHweP50koszfAvpjHo+Jcn7MvkD8r4kJ6/wNj46yZeTnDDGYyyTYLU9yb1Jbkry1zOqe00mn13YvWzqIxYssa9XZBKqrkryhxmOODGjx8QRSf7HUPvyJE+aRd1h+R8k+dkV/u5L9fyHSf5puO0vTXLajOqu+L7bS91Rns97XH9tZnBUpCT/O5M3/FcmOW+E+27FrxN7qbui5/M+6n5Pko8Ot8eHkpyz0tv4YJ981TQAAHRMsQAAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIACxbVX1tj8vPqarfWa1+AMYgIAOw6qrqsNXuAWA3ARmAmaiqb6qq91XVx4efDx6W/0FVPbNb72vDzydW1d9W1R9n8gUWAGvCxtVuAIB15aiquqK7fHL+v6+S/50kb2qtXVJVz0vy35L80H7qnZvkka21z8++VYADIyADMI2vt9bO3n2hqp6TZGG4+N1JfmQ4/4dJXrWMeh8WjoG1xhQLAMbShp87Mvy9qapKckS3zl0HuymA/RGQAZiV9yd51nD+J5P8w3D+2iTnDOfPT3L4wW0LYDoCMgCz8qIkz62qjyf56SQvHpb/XpLvraoPJ3lMjBoDa1y11va/FgAAHCKMIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAAND5P/vsy6GyGlnBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.clustermap(dayHour,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": 39,
   "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>0</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>1</th>\n",
       "      <td>1973</td>\n",
       "      <td>1753</td>\n",
       "      <td>1884</td>\n",
       "      <td>1430</td>\n",
       "      <td>1918</td>\n",
       "      <td>1676</td>\n",
       "      <td>1670</td>\n",
       "      <td>1612</td>\n",
       "      <td>1234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1700</td>\n",
       "      <td>1903</td>\n",
       "      <td>1889</td>\n",
       "      <td>1517</td>\n",
       "      <td>1538</td>\n",
       "      <td>2058</td>\n",
       "      <td>1717</td>\n",
       "      <td>1295</td>\n",
       "      <td>1262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
       "    <tr>\n",
       "      <th>4</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month          1     2     3     4     5     6     7     8     12\n",
       "Day of Week                                                      \n",
       "0            1727  1964  1535  1598  1779  1617  1692  1511  1257\n",
       "1            1973  1753  1884  1430  1918  1676  1670  1612  1234\n",
       "2            1700  1903  1889  1517  1538  2058  1717  1295  1262\n",
       "3            1584  1596  1900  1601  1590  2065  1646  1230  1266\n",
       "4            1970  1581  1525  1958  1730  1649  2045  1310  1065"
      ]
     },
     "execution_count": 39,
     "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x23c8c37f448>"
      ]
     },
     "execution_count": 43,
     "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": [
    "sb.clustermap(dayMonth,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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}