Table of Contents
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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 project, 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 This is a CSV file containing data on Airbnb listing prices and locations.
listing_id
: unique identifier of listingprice
: nightly listing price in USDnbhood_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 listingdescription
: listing descriptionroom_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 listinghost_name
: name of listing hostlast_review
: date when the listing was last reviewed
# 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
# Begin coding here ...
#Import CSV for prices
airbnb_price = pd.read_csv('data/airbnb_price.csv')
#Import Excel file for room types
airbnb_room_type = pd.read_excel('data/airbnb_room_type.xlsx')
#Import TSV for review dates
airbnb_last_review = pd.read_csv('data/airbnb_last_review.tsv', sep='\t' )
#Join the three data frames together into one
listings = pd.merge(airbnb_price, airbnb_room_type, on='listing_id')
listings = pd.merge(listings, airbnb_last_review, on='listing_id')
#What are the dates of the earliest and most recent reviews?
#To use a function like max()/min() on last_review date column, it needs to be converted to datetime type
listings['last_review_date'] = pd.to_datetime(listings['last_review'], format= '%B %d %Y')
first_reviewed = listings['last_review_date'].min()
last_reviewed = listings['last_review_date'].max()
#How many of the listngs are private rooms?
#setince ther are differences in capitalization, make capitalization consistent
listings['room_type'] = listings['room_type'].str.lower()
private_room_count = listings[listings['room_type'] == 'private room'].shape[0]
#What is the average listing price?
#To convert price to numeric, remove "dollars" from each value
listings['price_clean'] = listings['price'].str.replace(' dollars', '').astype(float)
avg_price = listings['price_clean'].mean()
review_dates = pd.DataFrame({
'first_reviewed': [first_reviewed],
'last_reviewed': [last_reviewed],
'nb_private_rooms': [private_room_count],
'avg_price': [round(avg_price, 2)]
})
print(review_dates)