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
# Import data
airbnb_price_df = pd.read_csv('data/airbnb_price.csv')
print(airbnb_price_df.head())
airbnb_room_type_df = pd.read_excel('data/airbnb_room_type.xlsx')
print(airbnb_room_type_df.head())
airbnb_last_review_df = pd.read_csv('data/airbnb_last_review.tsv', sep='\t')
print(airbnb_last_review_df.head())
# Find earliest and most recent reviews in airbnb_last_review_df
airbnb_last_review_df['last_review'] = pd.to_datetime(airbnb_last_review_df['last_review'])
first_reviewed_df = airbnb_last_review_df.sort_values('last_review', ascending=True)
last_reviewed_df = airbnb_last_review_df.sort_values('last_review', ascending=False)
first_reviewed = first_reviewed_df['last_review'].iloc[0]
last_reviewed = last_reviewed_df['last_review'].iloc[0]
print(f"The earliest review was on {first_reviewed_df['last_review'].iloc[0]}.")
print(f"The most recent review has been on {last_reviewed_df['last_review'].iloc[0]}.")
# Find number of private rooms in airbnb_room_type_df
airbnb_room_type_df['room_type'] = airbnb_room_type_df['room_type'].str.lower()
nb_private_rooms = airbnb_room_type_df[airbnb_room_type_df['room_type'] == 'private room']['listing_id'].count()
print(f'There are {nb_private_rooms} private rooms in the list.')
# Find the mean price in airbnb_price_df
airbnb_price_df['dolar_price'] = airbnb_price_df['price'].str.replace('dollars','').astype('int')
avg_price = (airbnb_price_df['dolar_price'].mean().round(2))
print(f'The average listing price is {avg_price} dollars')
# New df with these variables
review_dates = pd.DataFrame({'first_reviewed': [first_reviewed],
'last_reviewed': [last_reviewed],
'nb_private_rooms': [nb_private_rooms],
'avg_price': [avg_price]})
print(review_dates)