Project: Exploring Airbnb Market Trends

Beta

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 notebook, 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

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

Our goals are to convert untidy data into appropriate formats to analyze, and answer key questions including:

What is the average price, per night, of an Airbnb listing in NYC?
How does the average price of an Airbnb listing, per month, compare to the private rental market?
How many adverts are for private rooms?
How do Airbnb listing prices compare across the five NYC boroughs?

# 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 datetime as dt

# Read in airbnb_price.csv
airbnb_price = pd.read_csv('data/airbnb_price.csv')
# print(airbnb_price.info())
# print(airbnb_price.head())

# Read in airbnb_room_type.xlsx
airbnb_room_type = pd.read_excel('data/airbnb_room_type.xlsx', )
# print(airbnb_room_type.head())

# Read in airbnb_last_review.tsv
airbnb_last_review = pd.read_csv('data/airbnb_last_review.tsv', sep='\t')
# print(airbnb_last_review.head())

# Remove the string 'dollars' from price column 
airbnb_price['price'] = airbnb_price['price'].str.rstrip(' dollars')

# Convert price column datatype into float
airbnb_price['price'] = airbnb_price['price'].astype('float')

# Filter price of free of charge
filtered_price = airbnb_price['price'] == 0

# Exclude the free price
airbnb_price = airbnb_price[~filtered_price]
# print(price.describe())

# Calculating the average price
avg_price = airbnb_price['price'].mean().round(2)
# print(avg_price)

# Add a new column price per month
airbnb_price['price_per_month'] = airbnb_price['price']*365/12

# Calculating the average price per month
average_price_per_month = airbnb_price['price_per_month'].mean().round(2)
# print(average_price_per_month)

# Calculating the differences price between private rental market
difference = (average_price_per_month - 3100).round(2)
# print(difference)

# Clean the room type column
airbnb_room_type['room_type'] = airbnb_room_type['room_type'].str.lower()

# Convert the room type column data into category
airbnb_room_type['room_type'] = airbnb_room_type['room_type'].astype('category')

# Calculating the room frequecny of the room type
room_frequencies = airbnb_room_type['room_type'].value_counts()
# print(room_frequencies)

# Convert the last_review column data into datetime
airbnb_last_review['last_review'] = pd.to_datetime(airbnb_last_review['last_review'])
# print(airbnb_last_review.info())

# Calculating the first review
first_reviewed = airbnb_last_review['last_review'].dt.date.min()
# print(first_reviewed)

# Calculating the last review
last_reviewed = airbnb_last_review['last_review'].dt.date.max()
# print(last_reviewed)

# Merge dataframe of price and room
price_roomtype = airbnb_price.merge(airbnb_room_type, how='outer', on='listing_id')
# print(price_roomtype.columns)

# Merge dataframe of price_roomtype with last_review
airbnb_merged = price_roomtype.merge(airbnb_last_review, on='listing_id', how='outer')
# print(airbnb_merged.columns)

# Drop missing value in the merged table
airbnb_merged.dropna(inplace=True)
# print(airbnb_merged.nbhood_full.value_counts())
# print(airbnb_merged.isnull().sum())
# print(airbnb_merged.duplicated().sum())

# Extract borough from nbhood_full column
airbnb_merged['borough'] = airbnb_merged['nbhood_full'].str.partition(',')[0]
# print(airbnb_merged.borough.value_counts())

# Group by borough to calculate the summary statistics: sum, mean, median, count
summary_statistics = airbnb_merged.groupby('borough')['price'].agg(['sum', 'mean', 'median', 'count']).round(2)
# print(summary_statistics.sort_values('mean', ascending=False))

# Create a list of label names
label_names = ["Budget", "Average", "Expensive", "Extravagant"]

# Create a list of price range
ranges = [0, 69, 175, 350, np.inf]

# Create a new column of price range
airbnb_merged['price_range'] = pd.cut(airbnb_merged['price'], bins=ranges, labels=label_names)
# print(airbnb_merged['price_range'].value_counts())

# Calculating prices by borough
prices_by_borough = airbnb_merged.groupby(['borough', 'price_range'])['price_range'].count()
# print(prices_by_borough)

# Create a dictionary called airbnb_analysis
airbnb_analysis ={'avg_price': avg_price, 'average_price_per_month': average_price_per_month, 'difference': difference, 'room_frequencies': room_frequencies, 'first_reviewed': first_reviewed, 'last_reviewed': last_reviewed, 'prices_by_borough': prices_by_borough}

print(airbnb_analysis)