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Project: Exploring Airbnb Market Trends
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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 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
    # Import necessary packages
    import pandas as pd
    import numpy as np
    
    # 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 listings are private rooms?
    # Since there 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)