Project: Exploring Airbnb Market Trends

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

# 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 the files needed
last_review = pd.read_csv("./data/airbnb_last_review.tsv", sep = "\t")
price = pd.read_csv("./data/airbnb_price.csv")
room_type = pd.read_excel('./data/airbnb_room_type.xlsx')

last_review.dtypes

price.dtypes

room_type["description"].unique()

# Convert the last_review column to datetime
last_review['last_review'] = pd.to_datetime(last_review['last_review'])

# Find the earliest and most recent reviews
earliest_review = last_review['last_review'].min().date()
most_recent_review = last_review['last_review'].max().date()

earliest_review

# Convert 'private room', 'Private room', 'PRIVATE ROOM' to 'Private Bathroom'
room_type['room_type'] = room_type['room_type'].str.lower().replace('private room', 'Private Room')

# Count the number of private rooms
private_rooms = len(room_type[room_type['room_type'] == 'Private Room'])

private_rooms

price

# Calculate the average listing price
price['price'] = price['price'].str.replace(' dollars', '').astype(float)
avg_price = round(price['price'].mean(), 2)

avg_price

# Create the review_dates DataFrame
review_dates = pd.DataFrame({
    'first_reviewed': [earliest_review],
    'last_reviewed': [most_recent_review],
    'nb_private_rooms': [private_rooms],
    'avg_price': [avg_price]
})

‌
‌
‌