Airbnb_nyc_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Airbnb NYC Listings Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Airbnb](New_York_city_.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Context</b>\n",
    "\n",
    "Analysis of a public Airbnb dataset from Kaggle: https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data\n",
    "\n",
    "Dataset describes the Airbnb listing activity and metrics in NYC, NY for 2019. It includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_df = pd.read_csv('AB_NYC_2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3831</td>\n",
       "      <td>Cozy Entire Floor of Brownstone</td>\n",
       "      <td>4869</td>\n",
       "      <td>LisaRoxanne</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>7192</td>\n",
       "      <td>Laura</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                              name  host_id  \\\n",
       "0  2539                Clean & quiet apt home by the park     2787   \n",
       "1  2595                             Skylit Midtown Castle     2845   \n",
       "2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n",
       "3  3831                   Cozy Entire Floor of Brownstone     4869   \n",
       "4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n",
       "\n",
       "     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
       "0         John            Brooklyn    Kensington  40.64749  -73.97237   \n",
       "1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n",
       "2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n",
       "3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
       "4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n",
       "\n",
       "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0     Private room    149               1                  9  2018-10-19   \n",
       "1  Entire home/apt    225               1                 45  2019-05-21   \n",
       "2     Private room    150               3                  0         NaN   \n",
       "3  Entire home/apt     89               1                270  2019-07-05   \n",
       "4  Entire home/apt     80              10                  9  2018-11-19   \n",
       "\n",
       "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "0               0.21                               6               365  \n",
       "1               0.38                               2               355  \n",
       "2                NaN                               1               365  \n",
       "3               4.64                               1               194  \n",
       "4               0.10                               1                 0  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the raw data provided we can explore the following questions:\n",
    "- Which neighbourhoods are the most/least expensive?\n",
    "- What is the average price for listings in each listing?\n",
    "- Which neighbourhoods are the most/least reviewed?\n",
    "- What types of rooms are most popular?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning and Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this stage the data is checked for accuracy and completeness prior to beginning the analysis. \n",
    "\n",
    "- Removing extraneous data and outliers.\n",
    "- Filling in missing values.\n",
    "- Conforming data to a standardized pattern.\n",
    "- Identifying errors revealed when new variables are created.\n",
    "- Deleting data that cannot be corrected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48895 entries, 0 to 48894\n",
      "Data columns (total 16 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              48895 non-null  int64  \n",
      " 1   name                            48879 non-null  object \n",
      " 2   host_id                         48895 non-null  int64  \n",
      " 3   host_name                       48874 non-null  object \n",
      " 4   neighbourhood_group             48895 non-null  object \n",
      " 5   neighbourhood                   48895 non-null  object \n",
      " 6   latitude                        48895 non-null  float64\n",
      " 7   longitude                       48895 non-null  float64\n",
      " 8   room_type                       48895 non-null  object \n",
      " 9   price                           48895 non-null  int64  \n",
      " 10  minimum_nights                  48895 non-null  int64  \n",
      " 11  number_of_reviews               48895 non-null  int64  \n",
      " 12  last_review                     38843 non-null  object \n",
      " 13  reviews_per_month               38843 non-null  float64\n",
      " 14  calculated_host_listings_count  48895 non-null  int64  \n",
      " 15  availability_365                48895 non-null  int64  \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "nyc_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The name, host_name, last_review and reviews_per_month columns have missing values.   \n",
    "\n",
    "The missing values originate from a variety of reasons:\n",
    "\n",
    "- The host or person inputting data may have forgotten to enter the value.\n",
    "- Hardware or software error during the collection is affecting accuracy of trip data and so on.\n",
    "\n",
    "Additionally, it is recommended to make the above values a required field for listing data collection purposes to avoid missing data in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identifying and Replacing Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    48879\n",
       "True        16\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Rows with missing value for last_review\n",
    "\n",
    "nyc_df['name'].isnull().value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    48874\n",
       "True        21\n",
       "Name: host_name, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Rows with missing value for last_review\n",
    "\n",
    "nyc_df['host_name'].isnull().value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    38843\n",
       "True     10052\n",
       "Name: last_review, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Rows with missing value for last_review\n",
    "\n",
    "nyc_df['last_review'].isnull().value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    38843\n",
       "True     10052\n",
       "Name: reviews_per_month, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Rows with missing value for reviews_per_month\n",
    "\n",
    "nyc_df['reviews_per_month'].isnull().value_counts() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of the missing data is from the \"last_review\" and \"reviews_per_month\" columns. Since the other two columns only have relatively few missing values we can just delete the associated rows. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reviews_per_month and last_review values are connected to each other. The missing values represent the fact that a review has not been left for that listing. \n",
    "\n",
    "- The reviews_per_month will be replaced with a score of 0.0.\n",
    "- The last_review will be set to \"2019-12-31\" to indicate there has been no review as of the last day of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing missing values\n",
    "nyc_df['reviews_per_month'] = nyc_df['reviews_per_month'].fillna(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    48895\n",
       "Name: reviews_per_month, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We have replaced all the missing reviews_per_month with a 0.0\n",
    "\n",
    "nyc_df['reviews_per_month'].isnull().value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing missing values\n",
    "nyc_df['last_review'] = nyc_df['last_review'].fillna(\"2019-12-31\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    48895\n",
       "Name: last_review, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Rows with missing value for last_review\n",
    "\n",
    "nyc_df['last_review'].isnull().value_counts() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deleting rows with missing name values\n",
    "nyc_df.dropna(subset=['name'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deleting rows with missing 'host_name' values\n",
    "nyc_df.dropna(subset=['host_name'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48858 entries, 0 to 48894\n",
      "Data columns (total 16 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              48858 non-null  int64  \n",
      " 1   name                            48858 non-null  object \n",
      " 2   host_id                         48858 non-null  int64  \n",
      " 3   host_name                       48858 non-null  object \n",
      " 4   neighbourhood_group             48858 non-null  object \n",
      " 5   neighbourhood                   48858 non-null  object \n",
      " 6   latitude                        48858 non-null  float64\n",
      " 7   longitude                       48858 non-null  float64\n",
      " 8   room_type                       48858 non-null  object \n",
      " 9   price                           48858 non-null  int64  \n",
      " 10  minimum_nights                  48858 non-null  int64  \n",
      " 11  number_of_reviews               48858 non-null  int64  \n",
      " 12  last_review                     48858 non-null  object \n",
      " 13  reviews_per_month               48858 non-null  float64\n",
      " 14  calculated_host_listings_count  48858 non-null  int64  \n",
      " 15  availability_365                48858 non-null  int64  \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 6.3+ MB\n"
     ]
    }
   ],
   "source": [
    "nyc_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All missing values have been dealt with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correcting formatting issues in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting last_review to datetime values \n",
    "nyc_df['last_review'] = pd.to_datetime(nyc_df['last_review'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting host_id to string\n",
    "nyc_df['host_id'] = nyc_df['host_id'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this stage we are adding new features that will provide more insight into the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identifying which month the review was left\n",
    "\n",
    "nyc_df['month'] = nyc_df['last_review'].apply(lambda time: time.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We needs to convert the values in the Month column from numbers to names of Months\n",
    "\n",
    "dmap = {1:'January',2:'February',3:'March',4:'April',5:'May',6:'June',7:'July',8:'August',9:'September',10:'October',11:'November',12:'December'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mapping our new dictionary to the Month column in the Dataframe\n",
    "\n",
    "nyc_df['month'] = nyc_df['month'].map(dmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Listing Coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>'listing_coordinate'</b> is the latitude and longitude pair for each listing that can be used to calculate distances to other points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a dataframe for latitude and longitude of each listing\n",
    "locations_df = nyc_df[[\"latitude\",\"longitude\"]]\n",
    "\n",
    "#Creating a list of tuples\n",
    "locations_df = locations_df.to_records(index=False)\n",
    "\n",
    "#There is now a column 'locations' with the latitude and longitude pair for each listing. Example, \n",
    "nyc_df['listing_coordinates'] = list(locations_df) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Major Attactions and Major Attraction Coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The neighbourhood groups represent the boroughs of New York City. As people use Airbnb for short visits primarily it is useful to know how far  listings are from popular locations in each borough like Central Park in Manhattan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brooklyn', 'Manhattan', 'Queens', 'Staten Island', 'Bronx'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying unique neighbourhoods groups (boroughs)\n",
    "nyc_df['neighbourhood_group'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Major Attraction in Each Neighbourhoods Group (Borough)\n",
    "- Brooklyn: Brooklyn Bridge\n",
    "- Manhattan: Central Park\n",
    "- Queens: Citi Field  \n",
    "- Staten Island: St. George's Ferry Terminal (Staten Island Ferry)\n",
    "- Bronx: Bronx Zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Coordinates of Major Attractions\n",
    "\n",
    "brooklyn_bridge = (40.706001,-73.997002)\n",
    "central_park = (40.769361,-73.977655)\n",
    "citi_field = (40.75416365,-73.84082997)\n",
    "staten_island_ferry = (40.643333,-74.074167) \n",
    "bronx_zoo = (40.852905,-73.872971)\n",
    "\n",
    "#Creating a dictionary with the values in the neighbourhood group column to the names of the related attraction\n",
    "dmap_locations = {'Brooklyn':\"Brooklyn Bridge\", 'Manhattan':\"Central Park\", 'Queens':\"Citi Field\", 'Staten Island':\"St. Georges Ferry Terminal\", 'Bronx':\"Bronx Zoo\"}\n",
    "\n",
    "#Creating a dictionary with the values in the neighbourhood group column to the coordinates of the related attraction\n",
    "dmap_coordinates = {'Brooklyn':brooklyn_bridge, 'Manhattan':central_park, 'Queens':citi_field, 'Staten Island':staten_island_ferry, 'Bronx':bronx_zoo}\n",
    "\n",
    "#Mapping our new dictionary to the neighbourhood_group column in the Dataframe\n",
    "nyc_df['major_attraction_location'] = nyc_df['neighbourhood_group'].map(dmap_locations)\n",
    "\n",
    "#Mapping our new dictionary to the neighbourhood_group column in the Dataframe\n",
    "nyc_df['major_attraction_coordinates'] = nyc_df['neighbourhood_group'].map(dmap_coordinates)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance to Major Attactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "import haversine\n",
    "from haversine import haversine, Unit\n",
    "\n",
    "#haversine can calculate the distance (in various units) between two points on Earth using their latitude and longitude.\n",
    "\n",
    "\n",
    "# We use the lambda function to apply haversine to calculate the distance to central park from each listing in Miles\n",
    "# The default units for haversine are Km but we have set it to Miles here\n",
    "# x.listing_coordinates and x.major_attraction_coordinates represent the coordinates of the listing location and the major attraction\n",
    "nyc_df['distance_to_major_attractions'] = nyc_df.apply(lambda x: haversine(x.listing_coordinates,x.major_attraction_coordinates,unit=Unit.MILES), axis = 1)\n",
    "\n",
    "#Rounding the values for readability\n",
    "nyc_df['distance_to_major_attractions'] = nyc_df['distance_to_major_attractions'].round(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this stage, we will examine the data to identify any patterns, trends and relationships between the variables. It will help us analyze the data and extract insights that can be used to make decisions.\n",
    "\n",
    "Data Visualization will give us a clear idea of what the data means by giving it visual context."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for any correlation in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(nyc_df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does not appear to be any significant correlation between variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Host ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         48858\n",
       "unique        37425\n",
       "top       219517861\n",
       "freq            327\n",
       "Name: host_id, dtype: object"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df['host_id'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are <b>37425</b> unique hosts with host <b>219517861</b> having the most listings (327)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>...</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",
       "      <th>month</th>\n",
       "      <th>listing_coordinates</th>\n",
       "      <th>major_attraction_location</th>\n",
       "      <th>major_attraction_coordinates</th>\n",
       "      <th>distance_to_major_attractions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38293</th>\n",
       "      <td>30181691</td>\n",
       "      <td>Sonder | 180 Water | Incredible 2BR + Rooftop</td>\n",
       "      <td>219517861</td>\n",
       "      <td>Sonder (NYC)</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>40.70637</td>\n",
       "      <td>-74.00645</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>302</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>327</td>\n",
       "      <td>309</td>\n",
       "      <td>December</td>\n",
       "      <td>[40.70637, -74.00645]</td>\n",
       "      <td>Central Park</td>\n",
       "      <td>(40.769361, -73.977655)</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38294</th>\n",
       "      <td>30181945</td>\n",
       "      <td>Sonder | 180 Water | Premier 1BR + Rooftop</td>\n",
       "      <td>219517861</td>\n",
       "      <td>Sonder (NYC)</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>40.70771</td>\n",
       "      <td>-74.00641</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>229</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-05-29</td>\n",
       "      <td>0.73</td>\n",
       "      <td>327</td>\n",
       "      <td>219</td>\n",
       "      <td>May</td>\n",
       "      <td>[40.70771, -74.00641]</td>\n",
       "      <td>Central Park</td>\n",
       "      <td>(40.769361, -73.977655)</td>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38588</th>\n",
       "      <td>30347708</td>\n",
       "      <td>Sonder | 180 Water | Charming 1BR + Rooftop</td>\n",
       "      <td>219517861</td>\n",
       "      <td>Sonder (NYC)</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Financial District</td>\n",
       "      <td>40.70743</td>\n",
       "      <td>-74.00443</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>232</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>0.60</td>\n",
       "      <td>327</td>\n",
       "      <td>159</td>\n",
       "      <td>May</td>\n",
       "      <td>[40.70743, -74.00443]</td>\n",
       "      <td>Central Park</td>\n",
       "      <td>(40.769361, -73.977655)</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39769</th>\n",
       "      <td>30937590</td>\n",
       "      <td>Sonder | The Nash | Artsy 1BR + Rooftop</td>\n",
       "      <td>219517861</td>\n",
       "      <td>Sonder (NYC)</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Murray Hill</td>\n",
       "      <td>40.74792</td>\n",
       "      <td>-73.97614</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>262</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2019-06-09</td>\n",
       "      <td>1.86</td>\n",
       "      <td>327</td>\n",
       "      <td>91</td>\n",
       "      <td>June</td>\n",
       "      <td>[40.74792, -73.97614]</td>\n",
       "      <td>Central Park</td>\n",
       "      <td>(40.769361, -73.977655)</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39770</th>\n",
       "      <td>30937591</td>\n",
       "      <td>Sonder | The Nash | Lovely Studio + Rooftop</td>\n",
       "      <td>219517861</td>\n",
       "      <td>Sonder (NYC)</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Murray Hill</td>\n",
       "      <td>40.74771</td>\n",
       "      <td>-73.97528</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>255</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>2.59</td>\n",
       "      <td>327</td>\n",
       "      <td>81</td>\n",
       "      <td>June</td>\n",
       "      <td>[40.74771, -73.97528]</td>\n",
       "      <td>Central Park</td>\n",
       "      <td>(40.769361, -73.977655)</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                           name    host_id  \\\n",
       "38293  30181691  Sonder | 180 Water | Incredible 2BR + Rooftop  219517861   \n",
       "38294  30181945     Sonder | 180 Water | Premier 1BR + Rooftop  219517861   \n",
       "38588  30347708    Sonder | 180 Water | Charming 1BR + Rooftop  219517861   \n",
       "39769  30937590        Sonder | The Nash | Artsy 1BR + Rooftop  219517861   \n",
       "39770  30937591    Sonder | The Nash | Lovely Studio + Rooftop  219517861   \n",
       "\n",
       "          host_name neighbourhood_group       neighbourhood  latitude  \\\n",
       "38293  Sonder (NYC)           Manhattan  Financial District  40.70637   \n",
       "38294  Sonder (NYC)           Manhattan  Financial District  40.70771   \n",
       "38588  Sonder (NYC)           Manhattan  Financial District  40.70743   \n",
       "39769  Sonder (NYC)           Manhattan         Murray Hill  40.74792   \n",
       "39770  Sonder (NYC)           Manhattan         Murray Hill  40.74771   \n",
       "\n",
       "       longitude        room_type  price  ...  number_of_reviews  last_review  \\\n",
       "38293  -74.00645  Entire home/apt    302  ...                  0   2019-12-31   \n",
       "38294  -74.00641  Entire home/apt    229  ...                  1   2019-05-29   \n",
       "38588  -74.00443  Entire home/apt    232  ...                  1   2019-05-21   \n",
       "39769  -73.97614  Entire home/apt    262  ...                  8   2019-06-09   \n",
       "39770  -73.97528  Entire home/apt    255  ...                 14   2019-06-10   \n",
       "\n",
       "      reviews_per_month  calculated_host_listings_count  availability_365  \\\n",
       "38293              0.00                             327               309   \n",
       "38294              0.73                             327               219   \n",
       "38588              0.60                             327               159   \n",
       "39769              1.86                             327                91   \n",
       "39770              2.59                             327                81   \n",
       "\n",
       "          month    listing_coordinates major_attraction_location  \\\n",
       "38293  December  [40.70637, -74.00645]              Central Park   \n",
       "38294       May  [40.70771, -74.00641]              Central Park   \n",
       "38588       May  [40.70743, -74.00443]              Central Park   \n",
       "39769      June  [40.74792, -73.97614]              Central Park   \n",
       "39770      June  [40.74771, -73.97528]              Central Park   \n",
       "\n",
       "      major_attraction_coordinates distance_to_major_attractions  \n",
       "38293      (40.769361, -73.977655)                          4.61  \n",
       "38294      (40.769361, -73.977655)                          4.52  \n",
       "38588      (40.769361, -73.977655)                          4.50  \n",
       "39769      (40.769361, -73.977655)                          1.48  \n",
       "39770      (40.769361, -73.977655)                          1.50  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df[nyc_df['host_id'] == '219517861'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top host <i>(219517861)</i>  is <b>Sonder (NYC)</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neighbourhood "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count            48858\n",
       "unique             221\n",
       "top       Williamsburg\n",
       "freq              3917\n",
       "Name: neighbourhood, dtype: object"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df['neighbourhood'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are <b>221</b> neighbourhoods with <b>Williamsburg</b> having the most listings (3917). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 Neighbourhoods with the Most Listings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Williamsburg          3917\n",
       "Bedford-Stuyvesant    3713\n",
       "Harlem                2655\n",
       "Bushwick              2462\n",
       "Upper West Side       1969\n",
       "Hell's Kitchen        1954\n",
       "East Village          1852\n",
       "Upper East Side       1797\n",
       "Crown Heights         1563\n",
       "Midtown               1545\n",
       "Name: neighbourhood, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df['neighbourhood'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23427"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calulating the total number of listings that the top 10 neighbourhoods account for\n",
    "nyc_df['neighbourhood'].value_counts().head(10).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.95"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round((23427/48858)*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top 10 neighbourhoods represent about <b>47.95%</b> of all listings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neighbourhood Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         48858\n",
       "unique            5\n",
       "top       Manhattan\n",
       "freq          21643\n",
       "Name: neighbourhood_group, dtype: object"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying unique neighbourhoods\n",
    "nyc_df['neighbourhood_group'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are <b>5</b> neighbourhood groups with <b>Manhattan</b> having the most listings (21643). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Listings in Each Neighbourhood Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manhattan        21643\n",
       "Brooklyn         20089\n",
       "Queens            5664\n",
       "Bronx             1089\n",
       "Staten Island      373\n",
       "Name: neighbourhood_group, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df['neighbourhood_group'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Room Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entire home/apt    25393\n",
       "Private room       22306\n",
       "Shared room         1159\n",
       "Name: room_type, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying number of rooms of each time\n",
    "nyc_df['room_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Rooms of Each Type')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='room_type',data=nyc_df,palette='viridis')\n",
    "plt.title(\"Number of Rooms of Each Type\",fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of the listings are Entire home/apts or Private rooms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Airbnb Listings Each Month')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_dims = (12, 4)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "\n",
    "# We use order = nyc_df['Month'].value_counts().index to help us sort the count plot by the value counts\n",
    "\n",
    "sns.countplot(x='month',data=nyc_df,order = nyc_df['month'].value_counts().index,palette='viridis')\n",
    "plt.title(\"Airbnb Listings Each Month\",fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The majority of reviews are left in the month of <b>June</b> which indicates that the majority of customers used a rental in June. \n",
    "- Meanwhile, the least reviews are left in <b>February</b>, which indicates that the fewest of customers used a rental in February."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112.80142453641164"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df['availability_365'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, any given listing is available <b>113 days</b> in a year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>average_availability</th>\n",
       "      <th>average_price</th>\n",
       "      <th>average_number_of_reviews_per_listing</th>\n",
       "      <th>total_number_of_reviews</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>100.24</td>\n",
       "      <td>124.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>486174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>112.01</td>\n",
       "      <td>197.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>454126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>144.49</td>\n",
       "      <td>100.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>156902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>165.70</td>\n",
       "      <td>87.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>28334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>199.68</td>\n",
       "      <td>115.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>11541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     average_availability  average_price  \\\n",
       "neighbourhood_group                                        \n",
       "Brooklyn                           100.24          124.0   \n",
       "Manhattan                          112.01          197.0   \n",
       "Queens                             144.49          100.0   \n",
       "Bronx                              165.70           87.0   \n",
       "Staten Island                      199.68          115.0   \n",
       "\n",
       "                     average_number_of_reviews_per_listing  \\\n",
       "neighbourhood_group                                          \n",
       "Brooklyn                                              24.0   \n",
       "Manhattan                                             21.0   \n",
       "Queens                                                28.0   \n",
       "Bronx                                                 26.0   \n",
       "Staten Island                                         31.0   \n",
       "\n",
       "                     total_number_of_reviews  \n",
       "neighbourhood_group                           \n",
       "Brooklyn                              486174  \n",
       "Manhattan                             454126  \n",
       "Queens                                156902  \n",
       "Bronx                                  28334  \n",
       "Staten Island                          11541  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the average availability for each neighbourhood group (rounded to 2 decimal places)\n",
    "nbhd_group = nyc_df.groupby('neighbourhood_group')['availability_365'].mean().round(2)\n",
    "\n",
    "#Converting the series nbhd to a dataframe\n",
    "nbhd_group = nbhd_group.to_frame()\n",
    "\n",
    "#Renaming columns\n",
    "nbhd_group.rename(columns={'availability_365': 'average_availability'}, inplace=True)\n",
    "\n",
    "# Identifying the average price for each neighbourhood group (rounded to 2 decimal places)\n",
    "nbhd_group['average_price'] = nyc_df.groupby('neighbourhood_group')['price'].mean().round()\n",
    "\n",
    "# Identifying the average number of reviews per listing for each neighbourhood group (rounded to 2 decimal places)\n",
    "nbhd_group['average_number_of_reviews_per_listing'] = nyc_df.groupby('neighbourhood_group')['number_of_reviews'].mean().round()\n",
    "\n",
    "# Identifying the total number of reviews for each neighbourhood group(rounded to 2 decimal places)\n",
    "nbhd_group['total_number_of_reviews'] = nyc_df.groupby('neighbourhood_group')['number_of_reviews'].sum().round(2)\n",
    "\n",
    "nbhd_group.sort_values(by=['average_availability'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, \n",
    "- Listings in <b>Staten Island</b> have the greatest availability and receive the most reviews per listing. \n",
    "    - However, Staten Island also receives the least number of reviews overall.\n",
    "- Listings in <b>Manhattan</b> have the least availability and receive the least reviews per listing. \n",
    "    - However, Manhattan receives the second highest number of reviews overall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duration of Stay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.012444226124688"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df['minimum_nights'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average duration of stay for all listings is <b>7 days.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood\n",
       "Breezy Point                   1.000000\n",
       "New Dorp                       1.000000\n",
       "Oakwood                        1.200000\n",
       "East Morrisania                1.400000\n",
       "Woodlawn                       1.454545\n",
       "                                ...    \n",
       "Bay Terrace, Staten Island    16.500000\n",
       "Vinegar Hill                  18.352941\n",
       "Olinville                     23.500000\n",
       "North Riverdale               41.400000\n",
       "Spuyten Duyvil                48.250000\n",
       "Name: minimum_nights, Length: 221, dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df.groupby('neighbourhood')['minimum_nights'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listings in the <b>Spuyten Duyvil</b> neighbourhood offer the longest average duration of stay at approximately <b>48 days.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood_group\n",
       "Bronx            4.564738\n",
       "Brooklyn         6.057693\n",
       "Manhattan        8.538188\n",
       "Queens           5.182910\n",
       "Staten Island    4.831099\n",
       "Name: minimum_nights, dtype: float64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df.groupby('neighbourhood_group')['minimum_nights'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listings in the <b>Manhattan</b> neighbourhood group offer the longest average duration of stay at approximately <b>9 days.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance to Major Attractions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0813486020713086"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df['distance_to_major_attractions'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, any given listing is <b>3.1 miles</b> from the closest major attraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>average_distance_to_major_attractions</th>\n",
       "      <th>average_price</th>\n",
       "      <th>average_number_of_reviews_per_listing</th>\n",
       "      <th>total_number_of_reviews</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>2.42</td>\n",
       "      <td>87.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>28334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>2.56</td>\n",
       "      <td>197.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>454126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>3.12</td>\n",
       "      <td>115.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>11541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>3.37</td>\n",
       "      <td>124.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>486174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>4.19</td>\n",
       "      <td>100.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>156902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     average_distance_to_major_attractions  average_price  \\\n",
       "neighbourhood_group                                                         \n",
       "Bronx                                                 2.42           87.0   \n",
       "Manhattan                                             2.56          197.0   \n",
       "Staten Island                                         3.12          115.0   \n",
       "Brooklyn                                              3.37          124.0   \n",
       "Queens                                                4.19          100.0   \n",
       "\n",
       "                     average_number_of_reviews_per_listing  \\\n",
       "neighbourhood_group                                          \n",
       "Bronx                                                 26.0   \n",
       "Manhattan                                             21.0   \n",
       "Staten Island                                         31.0   \n",
       "Brooklyn                                              24.0   \n",
       "Queens                                                28.0   \n",
       "\n",
       "                     total_number_of_reviews  \n",
       "neighbourhood_group                           \n",
       "Bronx                                  28334  \n",
       "Manhattan                             454126  \n",
       "Staten Island                          11541  \n",
       "Brooklyn                              486174  \n",
       "Queens                                156902  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the average distance to the closest major attraction for each neighbourhood group (rounded to 2 decimal places)\n",
    "nbhd_group = nyc_df.groupby('neighbourhood_group')['distance_to_major_attractions'].mean().round(2)\n",
    "\n",
    "#Converting the series nbhd to a dataframe\n",
    "nbhd_group = nbhd_group.to_frame()\n",
    "\n",
    "#Renaming columns\n",
    "nbhd_group.rename(columns={'distance_to_major_attractions': 'average_distance_to_major_attractions'}, inplace=True)\n",
    "\n",
    "# Identifying the average price for each neighbourhood group (rounded to 2 decimal places)\n",
    "nbhd_group['average_price'] = nyc_df.groupby('neighbourhood_group')['price'].mean().round()\n",
    "\n",
    "# Identifying the average number of reviews for each neighbourhood group (rounded to 2 decimal places)\n",
    "nbhd_group['average_number_of_reviews_per_listing'] = nyc_df.groupby('neighbourhood_group')['number_of_reviews'].mean().round()\n",
    "\n",
    "# Identifying the total number of reviews per listing for each neighbourhood group(rounded to 2 decimal places)\n",
    "nbhd_group['total_number_of_reviews'] = nyc_df.groupby('neighbourhood_group')['number_of_reviews'].sum().round(2)\n",
    "\n",
    "nbhd_group.sort_values(by=['average_distance_to_major_attractions'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, \n",
    "- Listings in the <b>Bronx</b> are the closest a major attraction in the city. \n",
    "    - However, the Bronx also has the second lowest number of listings.\n",
    "- Listings in <b>Queens</b> have the least availability and receive the least reviews per listing. \n",
    "- <b>Manhattan</b> has the greatest number of listings and they are second closest to  a major attraction in the city. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Price Across all listings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152.74"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_all_listings = round(nyc_df['price'].mean(),2)\n",
    "avg_all_listings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price and Reviews Per Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Reviews per Month vs. Price')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "afig_dims = (12, 4)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "\n",
    "sns.scatterplot(y='reviews_per_month',x='price', data=nyc_df)\n",
    "plt.title(\"Reviews per Month vs. Price\",fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Reviews vs. Price')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_dims = (12, 4)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "\n",
    "sns.scatterplot(y='number_of_reviews',x='price', data=nyc_df)\n",
    "plt.title(\"Number of Reviews vs. Price\",fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plot we can see that the majority of more expensive listings receive fewers reviews as compared to less expensive ones. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Price by Neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "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>average_price</th>\n",
       "      <th>average_number_of_reviews</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Allerton</th>\n",
       "      <td>87.60</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arden Heights</th>\n",
       "      <td>67.25</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrochar</th>\n",
       "      <td>115.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arverne</th>\n",
       "      <td>171.78</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Astoria</th>\n",
       "      <td>117.19</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               average_price  average_number_of_reviews\n",
       "neighbourhood                                          \n",
       "Allerton               87.60                       43.0\n",
       "Arden Heights          67.25                        8.0\n",
       "Arrochar              115.00                       15.0\n",
       "Arverne               171.78                       29.0\n",
       "Astoria               117.19                       21.0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the average listing price for each neighbourhood (rounded to 2 decimal places)\n",
    "nbhd = nyc_df.groupby('neighbourhood')['price'].mean().round(2)\n",
    "\n",
    "#Converting the series nbhd to a dataframe\n",
    "nbhd = nbhd.to_frame()\n",
    "\n",
    "#Renaming columns\n",
    "nbhd.rename(columns={'price': 'average_price'}, inplace=True)\n",
    "\n",
    "# Identifying the average number of reviews for each neighbourhood (rounded to 2 decimal places)\n",
    "nbhd['average_number_of_reviews'] = nyc_df.groupby('neighbourhood')['number_of_reviews'].mean().round()\n",
    "\n",
    "nbhd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Reviews vs. Price: Aggregated by Neighbourhood')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_dims = (12, 4)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "\n",
    "sns.scatterplot(y='average_number_of_reviews',x='average_price', data=nbhd)\n",
    "plt.title(\"Number of Reviews vs. Price: Aggregated by Neighbourhood\",fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that once the data is aggregated by neighbourhood averages, there is still a larger number of reviews left for the less expensive listings as compared to the more expensive ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neighbourhoods with Listings Above Average Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_price                55\n",
       "average_number_of_reviews    55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbhd[nbhd['average_price']>avg_all_listings].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are <b>55</b> neighbourhoods with average listing price above the average for all listings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neighbourhoods with Listings Below Average Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_price                166\n",
       "average_number_of_reviews    166\n",
       "dtype: int64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbhd[nbhd['average_price']<avg_all_listings].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are <b>166</b> neighbourhoods with average listing price below the average for all listings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price in Each Neighbourhood Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood_group\n",
       "Bronx            106.798933\n",
       "Brooklyn         186.936694\n",
       "Manhattan        291.489822\n",
       "Queens           167.128794\n",
       "Staten Island    277.620403\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df.groupby('neighbourhood_group')['price'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Largest standard deviation in price is in <b>Manhattan</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Listing Price by Neighbourhood Group')"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_dims = (12, 4)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "\n",
    "sns.scatterplot(y='price',x='neighbourhood_group', data=nyc_df)\n",
    "plt.title(\"Listing Price by Neighbourhood Group\",fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spread of prices is greatest in <b>Manhattan.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Price, Total Number of Reviews and Number of Listings by Neighbourhood Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "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>average_price</th>\n",
       "      <th>total_number_of_reviews</th>\n",
       "      <th>number_of_listings</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>87.47</td>\n",
       "      <td>28334</td>\n",
       "      <td>1089</td>\n",
       "      <td>26.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>124.41</td>\n",
       "      <td>486174</td>\n",
       "      <td>20089</td>\n",
       "      <td>24.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>196.90</td>\n",
       "      <td>454126</td>\n",
       "      <td>21643</td>\n",
       "      <td>20.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>99.54</td>\n",
       "      <td>156902</td>\n",
       "      <td>5664</td>\n",
       "      <td>27.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staten Island</th>\n",
       "      <td>114.81</td>\n",
       "      <td>11541</td>\n",
       "      <td>373</td>\n",
       "      <td>30.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     average_price  total_number_of_reviews  \\\n",
       "neighbourhood_group                                           \n",
       "Bronx                        87.47                    28334   \n",
       "Brooklyn                    124.41                   486174   \n",
       "Manhattan                   196.90                   454126   \n",
       "Queens                       99.54                   156902   \n",
       "Staten Island               114.81                    11541   \n",
       "\n",
       "                     number_of_listings  ratio  \n",
       "neighbourhood_group                             \n",
       "Bronx                              1089  26.02  \n",
       "Brooklyn                          20089  24.20  \n",
       "Manhattan                         21643  20.98  \n",
       "Queens                             5664  27.70  \n",
       "Staten Island                       373  30.94  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the average listing price for each neighbourhood (rounded to 2 decimal places)\n",
    "nbhd_group = nyc_df.groupby('neighbourhood_group')['price'].mean().round(2)\n",
    "\n",
    "#Converting the series nbhd to a dataframe\n",
    "nbhd_group = nbhd_group.to_frame()\n",
    "\n",
    "#Renaming columns\n",
    "nbhd_group.rename(columns={'price': 'average_price'}, inplace=True)\n",
    "\n",
    "# Identifying the average number of reviews for each neighbourhood group(rounded to 2 decimal places)\n",
    "nbhd_group['total_number_of_reviews'] = nyc_df.groupby('neighbourhood_group')['number_of_reviews'].sum().round(2)\n",
    "\n",
    "nbhd_group['number_of_listings'] = nyc_df['neighbourhood_group'].value_counts()\n",
    "\n",
    "#Ratio of reviews as compared to total number of listings for each neighbourhood group\n",
    "nbhd_group['ratio'] = (nbhd_group['total_number_of_reviews']/nbhd_group['number_of_listings']).round(2)\n",
    "\n",
    "\n",
    "nbhd_group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice something interesting in the data here:\n",
    "\n",
    "- Staten island has the largest number of reviews as compared to the actual number of listings, which indicates that reviews were left more frequently for stays in listings that were within the Staten island neighbourhood group.\n",
    "- Manhattan has the second largest number of listings but has the least number of reviews compared to the actual number of listings, which indicates that reviews are left less frequently for stays in the Manhattan neighbourhood group. The possible reasons for this are as follows:\n",
    "\n",
    "    - The average listing price is also the highest of all neighbourhood groups.\n",
    "    - Manhattan's average listing price is also above the average for all listings.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Price by Room Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "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>average_price</th>\n",
       "      <th>total_number_of_reviews</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Entire home/apt</th>\n",
       "      <td>211.81</td>\n",
       "      <td>579856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private room</th>\n",
       "      <td>89.79</td>\n",
       "      <td>537965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shared room</th>\n",
       "      <td>70.08</td>\n",
       "      <td>19256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 average_price  total_number_of_reviews\n",
       "room_type                                              \n",
       "Entire home/apt         211.81                   579856\n",
       "Private room             89.79                   537965\n",
       "Shared room              70.08                    19256"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_type = nyc_df.groupby('room_type')['price'].mean().round(2)\n",
    "\n",
    "#Converting the series nbhd to a dataframe\n",
    "room_type = room_type.to_frame()\n",
    "\n",
    "#Renaming columns\n",
    "room_type.rename(columns={'price': 'average_price'}, inplace=True)\n",
    "\n",
    "# Identifying the average number of reviews for each neighbourhood (rounded to 2 decimal places)\n",
    "room_type['total_number_of_reviews'] = nyc_df.groupby('room_type')['number_of_reviews'].sum().round()\n",
    "\n",
    "room_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, listings with Entire home/apt are the most expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Reviews vs. Price for Each Room Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Number of Reviews vs. Price for Each Room Type')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Form a facetgrid using columns with a hue\n",
    "graph = sns.FacetGrid(nyc_df, col ='room_type')\n",
    "\n",
    "# map the above form facetgrid with some attributes\n",
    "graph.map(sns.scatterplot, \"price\",\"number_of_reviews\")\n",
    "\n",
    "#Setting the title for the FacetGrid \n",
    "graph.fig.subplots_adjust(top=0.8)\n",
    "graph.fig.suptitle('Number of Reviews vs. Price for Each Room Type', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are more reviews for less expensive listings regardless of the room types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of Data\n",
    "\n",
    "#### Availability\n",
    "- There are 48858 listings in total.\n",
    "- The majority of the listings are Entire home/apts or Private rooms.\n",
    "\n",
    "- On average, any given listing is available <b>113 days</b> in a year.\n",
    "    - Listings in <b>Staten Island</b> have the greatest availability and receive the most reviews per listing. \n",
    "    - However, Staten Island also receives the least number of reviews overall.\n",
    "    - Listings in <b>Manhattan</b> have the least availability and receive the least reviews per listing. \n",
    "    - However, Manhattan receives the second highest number of reviews overall.\n",
    "    \n",
    "- Average duration of stay for all listings is 7 days.\n",
    "    - Listings in the <b>Spuyten Duyvil</b> neighbourhood offer the longest average duration of stay at approximately <b>48 days.</b>\n",
    "    - Listings in the <b>Manhattan</b> neighbourhood group offer the longest average duration of stay at approximately <b>9 days.</b>\n",
    "\n",
    "#### Location\n",
    " \n",
    "- There are <b>221</b> neighbourhoods with <b>Williamsburg</b> having the most listings (3917). \n",
    "- The top 10 neighbourhoods represent about <b>47.95%</b> of all listings.\n",
    "- There are <b>5</b> neighbourhood groups with <b>Manhattan</b> having the most listings (21643). \n",
    "\n",
    "- On average, any given listing is <b>3.1 miles</b> from the closest major attraction.\n",
    "    - Listings in the <b>Bronx</b> are the closest a major attraction in the city. \n",
    "    - However, the Bronx also has the second lowest number of listings.\n",
    "    - Listings in <b>Queens</b> have the least availability and receive the least reviews per listing. \n",
    "    - <b>Manhattan</b> has the greatest number of listings and they are second closest to  a major attraction in the city. \n",
    "\n",
    "#### Price \n",
    "\n",
    "- Average Price Across all listings: 152.74\n",
    "- There are <b>55</b> neighbourhoods with average listing price above the average for all listings.\n",
    "- There are <b>166</b> neighbourhoods with average listing price below the average for all listings.\n",
    "- Largest standard deviation in price is in <b>Manhattan</b>.\n",
    "- The spread of prices is greatest in <b>Manhattan.</b>\n",
    "- As expected, listings with Entire home/apt are the most expensive.\n",
    "\n",
    "#### Number of Reviews\n",
    "\n",
    "- Across all categories (Room Type, Neighbourhood etc.), less expensive Listings receive more reviews. \n",
    "- The majority of reviews are left in the month of <b>June</b> which indicates that the majority of customers used a rental in June. Meanwhile, the least reviews are left in <b>February</b>, which indicates that the  fewest customers used a rental in February.\n",
    "- Staten Island has the largest number of reviews as compared to the actual number of listings, which indicates that reviews were left more frequently for stays in listings that were within the Staten island neighbourhood group.\n",
    "- Manhattan has the second largest number of listings but has the least number of reviews compared to the actual number of listings, which indicates that reviews are left less frequently for stays in the Manhattan neighbourhood group. The possible reasons for this are as follows:\n",
    "\n",
    "    - The average listing price is also the highest of all neighbourhood groups.\n",
    "    - Manhattan's average listing price is also above the average for all listings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Additional Data necessary</b><br>\n",
    "The data only tells us if a review was left or not for any given listing. It would be beneficial to know what score each listing received when they were reviewed. We can only go off the number of reviews listings receive and assume listings (and by extension neighbourhoods and neighbourhood groups) with more reviews are preferable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exporting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting the dataset (without the index values)\n",
    "#nyc_df.to_csv('Airbnb.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}