Exploring the NYC Airbnb Market

Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this notebook, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .csv, .tsv, and .xlsx.

Recall that CSV, TSV, and Excel files are three common formats for storing data. Three files containing data on 2019 Airbnb listings are available to you:

data/airbnb_price.csv

listing_id: unique identifier of listing
price: nightly listing price in USD
nbhood_full: name of borough and neighborhood where listing is located

data/airbnb_room_type.xlsx This is an Excel file containing data on Airbnb listing descriptions and room types.

listing_id: unique identifier of listing
description: listing description
room_type: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments

data/airbnb_last_review.tsv This is a TSV file containing data on Airbnb host names and review dates.

listing_id: unique identifier of listing
host_name: name of listing host
last_review: date when the listing was last reviewed

Our goals are to convert untidy data into appropriate formats to analyze, and answer key questions including:

What is the average price, per night, of an Airbnb listing in NYC?
How does the average price of an Airbnb listing, per month, compare to the private rental market?
How many adverts are for private rooms?
How do Airbnb listing prices compare across the five NYC boroughs?

# We've loaded your first package for you! You can add as many cells as you need.
import numpy as np

import pandas as pd
airbnb_analysis={}
#Import the data
prices = pd.read_csv('data/airbnb_price.csv')
types = pd.read_excel('data/airbnb_room_type.xlsx')
reviews = pd.read_csv('data/airbnb_last_review.tsv', sep='\t')

print(prices.head())
print(types.head())
print(reviews.head())


#Delete unnecessary tables
del types['description']
del reviews['host_name']

#Look For Duplicates
print(prices.isna().any())
print(types.isna().any())
print(reviews.isna().any())

prices['price'],y = prices['price'].str.split(' ').str
prices['price'] = prices['price'].astype(int)

import matplotlib.pyplot as plt
prices.boxplot(column='price') 
plt.yscale('log')
plt.show()

# Subset prices for listings costing $0, free_listings
free_listings = prices["price"] == 0

# Update prices by removing all free listings from prices
prices = prices.loc[~free_listings]

airbnb_analysis['avg_price'] = prices['price'].mean().round(2)
print(airbnb_analysis)

prices['price_per_month'] = prices['price'] * 365 /12
airbnb_analysis['average_price_per_month'] = prices['price_per_month'].mean().round(2)
airbnb_analysis['difference'] = (airbnb_analysis['average_price_per_month'] - 3100).round(2)
print(airbnb_analysis)

types['room_type'] = types['room_type'].str.lower().astype('category')
print(types['room_type'].unique())
airbnb_analysis['room_frequency'] = types["room_type"].value_counts()
print(airbnb_analysis)

reviews['last_review']=pd.to_datetime(reviews['last_review'])
airbnb_analysis['first_reviewed'] = reviews['last_review'].min()
airbnb_analysis['last_reviewed'] = reviews['last_review'].max()
print(airbnb_analysis)

rooms_and_prices = prices.merge(types, on='listing_id', how='outer')
airbnb_merged = rooms_and_prices.merge(reviews, on='listing_id', how='outer')
airbnb_merged.dropna(inplace=True)
print(airbnb_merged.duplicated().sum())

airbnb_merged['borough'] = airbnb_merged["nbhood_full"].str.partition(', ')[0]
boroughs = airbnb_merged.groupby('borough')['price'].agg([np.mean, np.median, sum, 'count']).round(2).sort_values('mean')
print(boroughs)

label_names = ["Budget", "Average", "Expensive", "Extravagant"]
ranges = [0, 69, 175, 350, np.inf]

airbnb_merged['price_range'] = pd.cut(airbnb_merged['price'], bins=ranges, labels=label_names)

# Group by borough and price_range
prices_by_borough = airbnb_merged.groupby(["borough", "price_range"])["price_range"].count()

airbnb_analysis['prices_by_borough'] = prices_by_borough

print(airbnb_analysis)