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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
# Import necessary packages
import pandas as pd
import numpy as np

# Begin coding here ...
# Use as many cells as you like
airbnb_last_review= pd.read_csv('data/airbnb_last_review.tsv', sep='\t')
# Convert the review_date column to datetime format
airbnb_last_review['last_review'] = pd.to_datetime(airbnb_last_review['last_review'], format='%B %d %Y', errors='coerce')
airbnb_last_review.info()
# Find the earliest and latest review dates
earliest_review_date = airbnb_last_review['last_review'].min()
latest_review_date = airbnb_last_review['last_review'].max()
# Print the results
print(f"\nEarliest review date: {earliest_review_date}")
print(f"Most recent review date: {latest_review_date}")

airbnb_room_type= pd.read_excel('data/airbnb_room_type.xlsx')
airbnb_room_type['room_type']=airbnb_room_type['room_type'].str.lower()
# Count the entries where room_type is "private room"
private_room_count = airbnb_room_type['room_type'].value_counts().get('private room', 0)
airbnb_price=pd.read_csv('data/airbnb_price.csv')
airbnb_price['price'] = airbnb_price['price'].str.replace('dollars', '', regex=False)
# Remove the word 'dollars' and strip any leading or trailing whitespace
airbnb_price['price'] = airbnb_price['price'].str.replace('dollars', '', regex=False).str.strip()

# Convert the cleaned listing_price column to numeric format
airbnb_price['price'] = pd.to_numeric(airbnb_price['price'] , errors='coerce')  
average_price = airbnb_price['price'].mean()

# Round to two decimal places
avg_price = round(average_price, 2)

# Create a DataFrame with one row
review_dates = pd.DataFrame({
    'first_reviewed': [earliest_review_date],
    'last_reviewed': [latest_review_date],
    'nb_private_rooms': [private_room_count],
    'avg_price': [avg_price]
})
print(review_dates)