Table of Contents
The outline of your notebook will show up here. You can include headings in any text cell by starting a line with # , ## , ### , etc., depending on the desired title hierarchy.
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 listingprice: nightly listing price in USDnbhood_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 listingdescription: listing descriptionroom_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 listinghost_name: name of listing hostlast_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 matplotlib.pyplot as plt
import seaborn as snsprices = pd.read_csv('data/airbnb_price.csv')
room_types = pd.read_excel('data/airbnb_room_type.xlsx')
reviews = pd.read_csv('data/airbnb_last_review.tsv', sep='\t')prices['price'] = prices['price'].str.replace(' dollars','')
prices['price'] = pd.to_numeric(prices['price'])
prices.dtypeszeros = prices.price == 0
prices = prices[~zeros]
avg_price = prices.price.mean()
avg_priceprices['price_per_month'] = prices['price'] * 365/12
average_price_per_month = prices['price_per_month'].mean()
difference = round(average_price_per_month - 3100, 2)
differenceroom_types['room_type'].value_counts()room_types['room_type'] = room_types['room_type'].str.lower()
room_frequencies = room_types['room_type'].value_counts()
room_frequenciesreviews.head()reviews.dtypesreviews['last_review'] = pd.to_datetime(reviews["last_review"])
first_reviewed = reviews['last_review'].dt.date.min()
last_reviewed = reviews['last_review'].dt.date.max()rooms_and_prices = prices.merge(room_types, on='listing_id', how='outer')
airbnb_merged = rooms_and_prices.merge(reviews, on='listing_id', how='outer')airbnb_merged.head()airbnb_merged.dropna(inplace=True)
airbnb_merged.duplicated().sum()airbnb_merged['borough'] = airbnb_merged['nbhood_full'].str.partition(',')[0]