Data Science and Machine Learning Bootcamp - JP/10-Data-Capstone-Projects/Project - 911 Calls/01-911 Calls Data Capstone Project - My Solutions.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)"
   ]
  },
  {
   "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 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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read in the csv file as a dataframe called df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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": 6,
     "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": 10,
   "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": 10,
     "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": 11,
   "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": 11,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 13,
     "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Reason\"] = df[\"title\"].apply(lambda x : x.split(\":\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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",
       "      <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",
       "  </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",
       "\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",
       "\n",
       "                         addr  e Reason  \n",
       "0      REINDEER CT & DEAD END  1    EMS  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Reason\"].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now use seaborn to create a countplot of 911 calls by Reason.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "sns.countplot(x = \"Reason\" , data = df, palette = \"viridis\");"
   ]
  },
  {
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"timeStamp\"][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": 36,
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 12, 3)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = df[\"timeStamp\"].iloc[0]\n",
    "\n",
    "time.hour, time.month, time.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "\n",
    "df.head(2)"
   ]
  },
  {
   "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": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0: \"Mon\", 1: \"Tue\", 2: \"Wed\", 3: \"Thur\", 4: \"Fri\", 5: \"Sat\", 6: \"Sun\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Day of Week\"] = df[\"Day of Week\"].map(dmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thur</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>Thur</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",
       "\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",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12        Thur  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12        Thur  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "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": 53,
   "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": [
    "sns.countplot(x = \"Day of Week\", data = df, hue = \"Reason\", palette = \"viridis\");\n",
    "\n",
    "plt.legend(bbox_to_anchor =(1,1.1), loc = 2, borderaxespad = 2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": [
    "sns.countplot(x = \"Month\", data = df, hue = \"Reason\", palette = \"viridis\");\n",
    "\n",
    "plt.legend(bbox_to_anchor = (1,1), loc = 2, borderaxespad = 2);"
   ]
  },
  {
   "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": 77,
   "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth = df.groupby(\"Month\").count()\n",
    "byMonth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a simple plot off of the dataframe indicating the count of calls per month.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": [
    "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": 79,
   "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>total_calls</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  total_calls  \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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth[\"total_calls\"] = byMonth.lat\n",
    "byMonth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = \"Month\", y = \"total_calls\", 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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>Thur</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>Thur</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",
       "\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",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12        Thur  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12        Thur  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0        17:40:00\n",
       " 1        17:40:00\n",
       " 2        17:40:00\n",
       " 3        17:40:01\n",
       " 4        17:40:01\n",
       "            ...   \n",
       " 99487    11:06:00\n",
       " 99488    11:07:02\n",
       " 99489    11:12:00\n",
       " 99490    11:17:01\n",
       " 99491    11:17:02\n",
       " Name: timeStamp, Length: 99492, dtype: object,\n",
       " 0        2015-12-10\n",
       " 1        2015-12-10\n",
       " 2        2015-12-10\n",
       " 3        2015-12-10\n",
       " 4        2015-12-10\n",
       "             ...    \n",
       " 99487    2016-08-24\n",
       " 99488    2016-08-24\n",
       " 99489    2016-08-24\n",
       " 99490    2016-08-24\n",
       " 99491    2016-08-24\n",
       " Name: timeStamp, Length: 99492, dtype: object)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"timeStamp\"].apply(lambda t : t.time()), df[\"timeStamp\"].apply(lambda t : t.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Date\"] = df[\"timeStamp\"].apply(lambda t: t.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Thur</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>Thur</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",
       "\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",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week        Date  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12        Thur  2015-12-10  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12        Thur  2015-12-10  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "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": 93,
   "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",
    "\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": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the various Reason\n",
    "df[\"Reason\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2015-12-10</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>108</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>137</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>109</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>144</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>54</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>150</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>184</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            lat  lng  desc  zip  title  timeStamp  twp  addr    e  Reason  \\\n",
       "Date                                                                        \n",
       "2015-12-10   43   43    43   35     43         43   43    41   43      43   \n",
       "2015-12-11  141  141   141  108    141        141  141   137  141     141   \n",
       "2015-12-12  146  146   146  109    146        146  146   144  146     146   \n",
       "2015-12-13   78   78    78   54     78         78   78    76   78      78   \n",
       "2015-12-14  186  186   186  150    186        186  186   184  186     186   \n",
       "\n",
       "            Hour  Month  Day of Week  \n",
       "Date                                  \n",
       "2015-12-10    43     43           43  \n",
       "2015-12-11   141    141          141  \n",
       "2015-12-12   146    146          146  \n",
       "2015-12-13    78     78           78  \n",
       "2015-12-14   186    186          186  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_Traffic = df[df[\"Reason\"] == \"Traffic\"].groupby(\"Date\").count()\n",
    "by_Traffic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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": [
    "by_Traffic[\"twp\"].plot();\n",
    "plt.title('Traffic')\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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": [
    "by_Fire = df[df[\"Reason\"] == \"Fire\"].groupby(\"Date\").count()\n",
    "\n",
    "by_Fire[\"twp\"].plot();\n",
    "plt.title(\"Fire\")\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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": [
    "by_EMS = df[df[\"Reason\"] == \"EMS\"].groupby(\"Date\").count()\n",
    "\n",
    "by_EMS[\"twp\"].plot();\n",
    "plt.title(\"EMS\")\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": 116,
   "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>Thur</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>Thur</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",
       "\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",
       "\n",
       "                         addr  e Reason  Hour  Month Day of Week        Date  \n",
       "0      REINDEER CT & DEAD END  1    EMS    17     12        Thur  2015-12-10  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12        Thur  2015-12-10  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>Thur</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",
       "Thur         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",
       "Thur          935  1013  810  698  617  553  424  354  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_hour = df.groupby([\"Day of Week\", \"Hour\"]).count()[\"Reason\"].unstack()\n",
    "day_hour.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a HeatMap using this new DataFrame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(data = day_hour, cmap = \"viridis\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a clustermap using this DataFrame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "sns.clustermap(data = day_hour, 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": 143,
   "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>1523</td>\n",
       "      <td>1958</td>\n",
       "      <td>1730</td>\n",
       "      <td>1649</td>\n",
       "      <td>2045</td>\n",
       "      <td>1310</td>\n",
       "      <td>1064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>1727</td>\n",
       "      <td>1964</td>\n",
       "      <td>1533</td>\n",
       "      <td>1597</td>\n",
       "      <td>1779</td>\n",
       "      <td>1617</td>\n",
       "      <td>1692</td>\n",
       "      <td>1509</td>\n",
       "      <td>1256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2290</td>\n",
       "      <td>1440</td>\n",
       "      <td>1264</td>\n",
       "      <td>1732</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>1100</td>\n",
       "      <td>1488</td>\n",
       "      <td>1422</td>\n",
       "      <td>1331</td>\n",
       "      <td>1672</td>\n",
       "      <td>1021</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</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>1227</td>\n",
       "      <td>1265</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  1523  1958  1730  1649  2045  1310  1064\n",
       "Mon          1727  1964  1533  1597  1779  1617  1692  1509  1256\n",
       "Sat          2290  1440  1264  1732  1444  1388  1695  1099   978\n",
       "Sun          1960  1229  1100  1488  1422  1331  1672  1021   907\n",
       "Thur         1584  1596  1900  1601  1590  2065  1646  1227  1265"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_month = df.groupby([\"Day of Week\", \"Month\"]).count()[\"twp\"].unstack()\n",
    "day_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#heatmap\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.heatmap(data = day_month, cmap = \"viridis\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "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(data = day_month, cmap = \"viridis\");"
   ]
  }
 ],
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