MiniProject1_Rao.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the file, understanding the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exporting necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Required Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the CSV file in two ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the CSV file by hard-coding the file path\n",
    "airbnbData = pd.read_csv(\"/Users/anupamarao/Desktop/OneDrive - Syracuse University/Sem 3/IST 652 - Scripting for Data Analysis/Mini Project 1/DataSet/AB_NYC_2019.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>2787</td>\n",
       "      <td>John</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>2845</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>4632</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                 name  host_id  host_name  \\\n",
       "0  2539   Clean & quiet apt home by the park     2787       John   \n",
       "1  2595                Skylit Midtown Castle     2845   Jennifer   \n",
       "2  3647  THE VILLAGE OF HARLEM....NEW YORK !     4632  Elisabeth   \n",
       "\n",
       "  neighbourhood_group neighbourhood  latitude  longitude        room_type  \\\n",
       "0            Brooklyn    Kensington  40.64749  -73.97237     Private room   \n",
       "1           Manhattan       Midtown  40.75362  -73.98377  Entire home/apt   \n",
       "2           Manhattan        Harlem  40.80902  -73.94190     Private room   \n",
       "\n",
       "   price  minimum_nights  number_of_reviews last_review  reviews_per_month  \\\n",
       "0    149               1                  9  2018-10-19               0.21   \n",
       "1    225               1                 45  2019-05-21               0.38   \n",
       "2    150               3                  0         NaN                NaN   \n",
       "\n",
       "   calculated_host_listings_count  availability_365  \n",
       "0                               6               365  \n",
       "1                               2               355  \n",
       "2                               1               365  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the CSV file by letting user choose file from local computer\n",
    "from tkinter import *\n",
    "from tkinter.filedialog import askopenfilename\n",
    "file = askopenfilename()\n",
    "airbnbData = pd.read_csv(file, header = 0)\n",
    "airbnbData.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48895"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(airbnbData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  int64\n",
       "name                               object\n",
       "host_id                             int64\n",
       "host_name                          object\n",
       "neighbourhood_group                object\n",
       "neighbourhood                      object\n",
       "latitude                          float64\n",
       "longitude                         float64\n",
       "room_type                          object\n",
       "price                               int64\n",
       "minimum_nights                      int64\n",
       "number_of_reviews                   int64\n",
       "last_review                        object\n",
       "reviews_per_month                 float64\n",
       "calculated_host_listings_count      int64\n",
       "availability_365                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnbData.dtypes #Column names of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data wrangling and Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping insignificant columns\n",
    "airbnbData.drop(['id','host_name','last_review'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing null values with 0\n",
    "airbnbData.fillna({'reviews_per_month':0}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking to see if null values exist\n",
    "airbnbData.reviews_per_month.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis on the dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding hosts with the most listings\n",
    "MaxHostListings = airbnbData.host_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Top 10 hosts with most listings\n",
    "MaxHostListings = MaxHostListings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HostID</th>\n",
       "      <th>Count of Listings with the HostID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>219517861</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107434423</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30283594</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>137358866</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12243051</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16098958</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>61391963</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22541573</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>200380610</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7503643</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      HostID  Count of Listings with the HostID\n",
       "0  219517861                                327\n",
       "1  107434423                                232\n",
       "2   30283594                                121\n",
       "3  137358866                                103\n",
       "4   12243051                                 96\n",
       "5   16098958                                 96\n",
       "6   61391963                                 91\n",
       "7   22541573                                 87\n",
       "8  200380610                                 65\n",
       "9    7503643                                 52"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing it as structured data\n",
    "TopHosts = pd.DataFrame(MaxHostListings)\n",
    "TopHosts.reset_index(inplace=True)\n",
    "TopHosts.rename(columns={'index':'HostID', 'host_id':'Count of Listings with the HostID'}, inplace=True)\n",
    "TopHosts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11759</th>\n",
       "      <td>Room near JFK Queen Bed</td>\n",
       "      <td>47621202</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>40.66730</td>\n",
       "      <td>-73.76831</td>\n",
       "      <td>Private room</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>629</td>\n",
       "      <td>14.58</td>\n",
       "      <td>2</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>Great Bedroom in Manhattan</td>\n",
       "      <td>4734398</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.82085</td>\n",
       "      <td>-73.94025</td>\n",
       "      <td>Private room</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>607</td>\n",
       "      <td>7.75</td>\n",
       "      <td>3</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>Beautiful Bedroom in Manhattan</td>\n",
       "      <td>4734398</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.82124</td>\n",
       "      <td>-73.93838</td>\n",
       "      <td>Private room</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>597</td>\n",
       "      <td>7.72</td>\n",
       "      <td>3</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>Private Bedroom in Manhattan</td>\n",
       "      <td>4734398</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.82264</td>\n",
       "      <td>-73.94041</td>\n",
       "      <td>Private room</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>594</td>\n",
       "      <td>7.57</td>\n",
       "      <td>3</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13495</th>\n",
       "      <td>Room Near JFK Twin Beds</td>\n",
       "      <td>47621202</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>40.66939</td>\n",
       "      <td>-73.76975</td>\n",
       "      <td>Private room</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>576</td>\n",
       "      <td>13.40</td>\n",
       "      <td>2</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10623</th>\n",
       "      <td>Steps away from Laguardia airport</td>\n",
       "      <td>37312959</td>\n",
       "      <td>Queens</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>40.77006</td>\n",
       "      <td>-73.87683</td>\n",
       "      <td>Private room</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>543</td>\n",
       "      <td>11.59</td>\n",
       "      <td>5</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>Manhattan Lux Loft.Like.Love.Lots.Look !</td>\n",
       "      <td>2369681</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Lower East Side</td>\n",
       "      <td>40.71921</td>\n",
       "      <td>-73.99116</td>\n",
       "      <td>Private room</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "      <td>540</td>\n",
       "      <td>6.95</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20403</th>\n",
       "      <td>Cozy Room Family Home LGA Airport NO CLEANING FEE</td>\n",
       "      <td>26432133</td>\n",
       "      <td>Queens</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>40.76335</td>\n",
       "      <td>-73.87007</td>\n",
       "      <td>Private room</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>510</td>\n",
       "      <td>16.22</td>\n",
       "      <td>5</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4870</th>\n",
       "      <td>Private brownstone studio Brooklyn</td>\n",
       "      <td>12949460</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Park Slope</td>\n",
       "      <td>40.67926</td>\n",
       "      <td>-73.97711</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>488</td>\n",
       "      <td>8.14</td>\n",
       "      <td>1</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>LG Private Room/Family Friendly</td>\n",
       "      <td>792159</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bushwick</td>\n",
       "      <td>40.70283</td>\n",
       "      <td>-73.92131</td>\n",
       "      <td>Private room</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>480</td>\n",
       "      <td>6.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    name   host_id  \\\n",
       "11759                            Room near JFK Queen Bed  47621202   \n",
       "2031                          Great Bedroom in Manhattan   4734398   \n",
       "2030                      Beautiful Bedroom in Manhattan   4734398   \n",
       "2015                        Private Bedroom in Manhattan   4734398   \n",
       "13495                            Room Near JFK Twin Beds  47621202   \n",
       "10623                  Steps away from Laguardia airport  37312959   \n",
       "1879            Manhattan Lux Loft.Like.Love.Lots.Look !   2369681   \n",
       "20403  Cozy Room Family Home LGA Airport NO CLEANING FEE  26432133   \n",
       "4870                  Private brownstone studio Brooklyn  12949460   \n",
       "471                      LG Private Room/Family Friendly    792159   \n",
       "\n",
       "      neighbourhood_group    neighbourhood  latitude  longitude  \\\n",
       "11759              Queens          Jamaica  40.66730  -73.76831   \n",
       "2031            Manhattan           Harlem  40.82085  -73.94025   \n",
       "2030            Manhattan           Harlem  40.82124  -73.93838   \n",
       "2015            Manhattan           Harlem  40.82264  -73.94041   \n",
       "13495              Queens          Jamaica  40.66939  -73.76975   \n",
       "10623              Queens    East Elmhurst  40.77006  -73.87683   \n",
       "1879            Manhattan  Lower East Side  40.71921  -73.99116   \n",
       "20403              Queens    East Elmhurst  40.76335  -73.87007   \n",
       "4870             Brooklyn       Park Slope  40.67926  -73.97711   \n",
       "471              Brooklyn         Bushwick  40.70283  -73.92131   \n",
       "\n",
       "             room_type  price  minimum_nights  number_of_reviews  \\\n",
       "11759     Private room     47               1                629   \n",
       "2031      Private room     49               1                607   \n",
       "2030      Private room     49               1                597   \n",
       "2015      Private room     49               1                594   \n",
       "13495     Private room     47               1                576   \n",
       "10623     Private room     46               1                543   \n",
       "1879      Private room     99               2                540   \n",
       "20403     Private room     48               1                510   \n",
       "4870   Entire home/apt    160               1                488   \n",
       "471       Private room     60               3                480   \n",
       "\n",
       "       reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "11759              14.58                               2               333  \n",
       "2031                7.75                               3               293  \n",
       "2030                7.72                               3               342  \n",
       "2015                7.57                               3               339  \n",
       "13495              13.40                               2               173  \n",
       "10623              11.59                               5               163  \n",
       "1879                6.95                               1               179  \n",
       "20403              16.22                               5               341  \n",
       "4870                8.14                               1               269  \n",
       "471                 6.70                               1                 0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#10 most reviewed listings \n",
    "MostReviewedListings = airbnbData.nlargest(10,'number_of_reviews')\n",
    "MostReviewedListings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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></th>\n",
       "      <th>number_of_reviews</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>room_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Bronx</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private room</th>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shared room</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Brooklyn</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <td>9559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private room</th>\n",
       "      <td>10132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shared room</th>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Manhattan</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <td>13199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private room</th>\n",
       "      <td>7982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shared room</th>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Queens</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <td>2096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private room</th>\n",
       "      <td>3372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shared room</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Staten Island</th>\n",
       "      <th>Entire home/apt</th>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private room</th>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shared room</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     number_of_reviews\n",
       "neighbourhood_group room_type                         \n",
       "Bronx               Entire home/apt                379\n",
       "                    Private room                   652\n",
       "                    Shared room                     60\n",
       "Brooklyn            Entire home/apt               9559\n",
       "                    Private room                 10132\n",
       "                    Shared room                    413\n",
       "Manhattan           Entire home/apt              13199\n",
       "                    Private room                  7982\n",
       "                    Shared room                    480\n",
       "Queens              Entire home/apt               2096\n",
       "                    Private room                  3372\n",
       "                    Shared room                    198\n",
       "Staten Island       Entire home/apt                176\n",
       "                    Private room                   188\n",
       "                    Shared room                      9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary table of neighbourhood groups & room type  against number of reviews\n",
    "pd.DataFrame(airbnbData.groupby([\"neighbourhood_group\",\"room_type\"])[\"number_of_reviews\"].count())"
   ]
  }
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