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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
airbnb_price_df = pd.read_csv("data/airbnb_price.csv")
# airbnb_price_df
from pathlib import Path
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def load_airbnb_data(data_path: str) -> dict:
    """
    Load Airbnb data from CSV, Excel, and TSV files into pandas DataFrames.
    
    Parameters:
    -----------
    data_path : str
        Path to the directory containing the data files
        
    Returns:
    --------
    dict
        Dictionary containing DataFrames with keys:
        - 'price': DataFrame from airbnb_price.csv
        - 'room_type': DataFrame from airbnb_room_type.xlsx
        - 'last_review': DataFrame from airbnb_last_review.tsv
        
    Raises:
    -------
    FileNotFoundError
        If any of the expected files are not found
    Exception
        For other unexpected errors during file loading
    """
    try:
        # Initialize dictionary to store DataFrames
        dataframes = {}
        
        # Define file paths
        data_path = Path(data_path)
        price_file = data_path / 'airbnb_price.csv'
        room_type_file = data_path / 'airbnb_room_type.xlsx'
        last_review_file = data_path / 'airbnb_last_review.tsv'
        
        # Load CSV file
        if price_file.exists():
            dataframes['price'] = pd.read_csv(price_file)
            logger.info(f"Successfully loaded {price_file}")
        else:
            raise FileNotFoundError(f"File not found: {price_file}")
            
        # Load Excel file
        if room_type_file.exists():
            dataframes['room_type'] = pd.read_excel(room_type_file)
            logger.info(f"Successfully loaded {room_type_file}")
        else:
            raise FileNotFoundError(f"File not found: {room_type_file}")
            
        # Load TSV file
        if last_review_file.exists():
            dataframes['last_review'] = pd.read_csv(last_review_file, sep='\t')
            logger.info(f"Successfully loaded {last_review_file}")
        else:
            raise FileNotFoundError(f"File not found: {last_review_file}")
            
        return dataframes
    
    except FileNotFoundError as e:
        logger.error(f"Error: {str(e)}")
        raise
    except Exception as e:
        logger.error(f"Unexpected error while loading files: {str(e)}")
        raise

# Example usage to load and display the DataFrames
if __name__ == "__main__":
    try:
        # Specify the path to your data folder
        data_folder = "./data"
        
        # Load the data
        airbnb_data = load_airbnb_data(data_folder)
        
        # Access and display the DataFrames
        price_df = airbnb_data['price']
        room_type_df = airbnb_data['room_type']
        last_review_df = airbnb_data['last_review']
        
        # Display basic information about each DataFrame
        print("\nPrice DataFrame Info:")
        print("-" * 50)
        print(price_df.info())
        print("\nFirst few rows:")
        print(price_df.head())
        
        print("\nRoom Type DataFrame Info:")
        print("-" * 50)
        print(room_type_df.info())
        print("\nFirst few rows:")
        print(room_type_df.head())
        
        print("\nLast Review DataFrame Info:")
        print("-" * 50)
        print(last_review_df.info())
        print("\nFirst few rows:")
        print(last_review_df.head())
        
    except Exception as e:
        print(f"Failed to load or display data: {str(e)}")
price_df.head()
room_type_df.head()
last_review_df.head()
def merge_airbnb_data(price_df: pd.DataFrame, room_type_df: pd.DataFrame, last_review_df: pd.DataFrame) -> pd.DataFrame:
    """
    Merge three Airbnb DataFrames (price, room_type, last_review) on the 'listing_id' column.
    
    Parameters:
    -----------
    price_df : pd.DataFrame
        DataFrame containing price data
    room_type_df : pd.DataFrame
        DataFrame containing room type data
    last_review_df : pd.DataFrame
        DataFrame containing last review data
        
    Returns:
    --------
    pd.DataFrame
        Merged DataFrame containing columns from all three input DataFrames
        
    Raises:
    -------
    ValueError
        If 'listing_id' is not present in any DataFrame or if merges result in empty DataFrames
    Exception
        For other unexpected errors during merging
    """
    try:
        # Validate that 'listing_id' exists in all DataFrames
        for df, name in [(price_df, 'price'), (room_type_df, 'room_type'), (last_review_df, 'last_review')]:
            if 'listing_id' not in df.columns:
                raise ValueError(f"'listing_id' column not found in {name} DataFrame")
        
        # Log initial shapes
        logger.info(f"Initial shapes - Price: {price_df.shape}, Room Type: {room_type_df.shape}, Last Review: {last_review_df.shape}")
        
        # First merge: price and room_type
        merged_df = pd.merge(
            price_df,
            room_type_df,
            on='listing_id',
            how='inner',
            suffixes=('_price', '_room')
        )
        
        if merged_df.empty:
            raise ValueError("First merge (price and room_type) resulted in an empty DataFrame")
        
        logger.info(f"Shape after price and room_type merge: {merged_df.shape}")
        
        # Second merge: result with last_review
        final_df = pd.merge(
            merged_df,
            last_review_df,
            on='listing_id',
            how='inner',
            suffixes=('_merged', '_last_review')
        )
        
        if final_df.empty:
            raise ValueError("Second merge (with last_review) resulted in an empty DataFrame")
        
        logger.info(f"Final merged DataFrame shape: {final_df.shape}")
        
        return final_df
    
    except ValueError as e:
        logger.error(f"ValueError during merge: {str(e)}")
        raise
    except Exception as e:
        logger.error(f"Unexpected error during merge: {str(e)}")
        raise

# Example usage with the previously loaded DataFrames
if __name__ == "__main__":
    try:
        # Assume DataFrames are loaded using the previous load_airbnb_data function
        # For demonstration, replace with your actual DataFrame loading
        data_folder = "./data"
        airbnb_data = load_airbnb_data(data_folder)  # From previous code
        
        price_df = airbnb_data['price']
        room_type_df = airbnb_data['room_type']
        last_review_df = airbnb_data['last_review']
        
        # Merge the DataFrames
        merged_df = merge_airbnb_data(price_df, room_type_df, last_review_df)
        
        # Display results
        print("\nMerged DataFrame Info:")
        print("-" * 50)
        print(merged_df.info())
        print("\nFirst few rows of merged DataFrame:")
        print(merged_df.head())
        
    except Exception as e:
        print(f"Failed to merge or display data: {str(e)}")
merged_df.head()
merged_df.info()
merged_df['last_review'] = pd.to_datetime(merged_df['last_review'], format='%B %d %Y')
merged_df.head()
earliest_review_date = merged_df['last_review'].min()
earliest_review_date 
most_recent_review_date = merged_df['last_review'].max()
most_recent_review_date
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