Python data analysis example: Analyzing Airbnb listing prices and identifying popular neighborhoods
Accommodation sharing platforms like Airbnb generate a lot of data on a global scale. Using this data to analyze listing prices by region and identify popular areas can be a very useful Python data analysis example. In this post Explains how to use New York Airbnb listing data to analyze listing prices and number of reviewsby neighborhood. Learn how to effectively derive price points and popular neighborhoods for listings by region with this Python data analysis example.
The importance of analyzing Airbnb listing prices
Listing prices are the most important information for travelers and hosts alike. By analyzing listing prices, travelers can better plan their budgets, and hosts can maximize their profitability. In this Python data analysis example, you'll learn how to analyze Airbnb data for New York to derive listing prices and review counts by region.
Loading and exploring data
First, Inside AirbnbThis Python data analysis example imports and explores data from Airbnb listings in New York City provided by Airbnb Inc. The data contains information about the price, location, number of reviews, and more.
Loading data from Python
import pandas as pd
# Load Airbnb listing data
url = 'https://raw.githubusercontent.com/erkansirin78/datasets/refs/heads/master/AB_NYC_2019.csv'
airbnb_data = pd.read_csv(url)
Explore the # data
print(airbnb_data.head())
print(airbnb_data.describe())This code is the first step in our Python data analysis example, and shows you how to load and explore Airbnb data for New York. The data includes price, number of reviews, and location information for each listing.
Data preprocessing
Before analyzing your data, it's important to identify and handle data with missing values or errors. Python data analysis examplesfor preprocessing to handle missing values.
Check for # missing values
print(airbnb_data.isnull().sum())
Fill in the # review-related missingness with zeroes
airbnb_data['reviews_per_month'].fillna(0, inplace=True)
airbnb_data['last_review'].fillna('No Review', inplace=True)
# Check after handling missing values
print(airbnb_data.isnull().sum())This code describes how to handle review-related missingness. It's important to handle missingness appropriately. Python data analysis examplesfor reliable analytics results.
Analyze listing prices by region
Now you want to analyze the price of listings in each neighborhood in New York. Calculate the average listing price by region and visualize it with this Python data analysis example.
Calculate and visualize average prices by region
import seaborn as sns
import matplotlib.pyplot as plt
Calculate the average listing price by # neighborhood
avg_price_by_neighborhood = airbnb_data.groupby('neighbourhood_group')['price'].mean().sort_values(ascending=False)
Visualize #
plt.figure(figsize=(10, 6))
avg_price_by_neighbourhood.plot(kind='bar', color='orange')
plt.title('Average listing price by neighborhood in New York')
plt.xlabel('Neighborhood')
plt.ylabel('Average price (USD)')
plt.show()In this Python data analysis example, we calculate the average listing price for each neighborhood in New York City and visualize it in a bar graph. The analysis shows that Manhattan is the most expensive neighborhood.
Analyzing popular regions: Based on number of reviews
The number of reviews is an indirect indicator of a listing's popularity. In this Python data analysis example, we'll analyze the average number of reviews per region to identify popular regions.
Calculate and visualize the average number of reviews by region
# Calculate the average number of reviews by neighborhood
avg_reviews_by_neighborhood = airbnb_data.groupby('neighborhood_group')['number_of_reviews'].mean().sort_values(ascending=False)
Visualize #
plt.figure(figsize=(10, 6))
avg_reviews_by_neighbourhood.plot(kind='bar', color='blue')
plt.title('Average number of reviews by neighborhood in New York')
plt.xlabel('Neighborhood')
plt.ylabel('Average number of reviews')
plt.show()In this Python data analysis example, we analyze the average number of reviews for each region and visually see which regions have received more reviews. You can see that Staten Island and Queens are popular regions with a high number of reviews.
Analyze the relationship between listing price and number of reviews
In this Python data analysis example, we analyze the correlation between the price of a listing and the number of reviews. We want to see if higher-priced listings get more reviews, or if there is a pattern between price and number of reviews.
Correlating price and number of reviews
# Calculate the correlation between price and number of reviews
correlation = airbnb_data[['price', 'number_of_reviews']].corr()
print(correlation)
Visualize as a # scatterplot
plt.figure(figsize=(10, 6))
sns.scatterplot(data=airbnb_data, x='price', y='number_of_reviews')
plt.title('Relationship between listing price and number of reviews')
plt.xlabel('Price (USD)')
plt.ylabel('Number of reviews')
plt.show()Correlation analysis visually analyzes the relationship between price and number of reviews. This Python data analysis example shows that a higher price does not necessarily mean a higher number of reviews, and that lower-priced listings are more likely to receive more reviews.
Common mistakes in data analysis and how to fix them
In this article, we'll cover common mistakes in Python data analysis and how to fix them.
- Lack of missing value handling: If your data contains missing values, it can skew the results of your analysis. Make sure to handle missing values appropriately.
- Errors in interpreting average price: The average price alone doesn't accurately describe the price range by region, so you need to consider the price distribution as well.
- Limitations of review count interpretation: A high number of reviews doesn't always indicate a popular listing, so you should consider a variety of analytics factors.
FAQs
Q1: Where can I get Airbnb data?
A: Inside AirbnbYou can download Airbnb data by city from Airbnb.com, or you can find it on places like Github via a Google search.
Q2: How can I learn more about Python data analytics?
A: We recommend looking for hands-on Python tutorials on various data analytics platforms, or analyzing real-world data like Airbnb data. For an example using S&P 500 data, see Herefor more information.
Q3: Can I create a price prediction model for my listing?
A: Yes, you can. Based on this data, you can use a variety of machine learning techniques, including linear regression, random forests, and more, to create a predictive model for listing prices.
Organize
In this post, you learned how to analyze New York Airbnb listing data in Python and identify popular neighborhoods based on listing price and number of reviews per neighborhood. This will help you understand the price points and popularity of your listings, and lay the groundwork for creating a price prediction model.
Now you can get hands-on with the Python data analysis examples and start drawing your own insights!
# Glossary
- AirbnbA stay-sharing platform that connects travelers and hosts around the world.
- Missing values: A value that is missing from the data and should be handled appropriately for analysis.
- Correlation: An indicator of the relationship between two variables, where a higher correlation number means a stronger relationship between the two variables.
- Number of reviewsThe number of reviews left on each listing, one of the metrics that reflects the popularity of a listing.
- Mean squared error (MSE)The squared and averaged difference between the model's predicted and actual values, used to evaluate the model's performance.
- Data preprocessing: The process of properly preparing data for data analysis, including handling missing values, data transformation, etc.
- Visualization: A way to represent data in a graph or chart to make it easier to see patterns or trends.
- Regional Groups: A variable available in Airbnb data that refers to a categorized neighborhood within the city the listing is in.
- Property type: A type of listing offered by Airbnb, which can be an entire home, a private room, and more.
