Case Overview
In this case study, we walk through the process of cleaning a real-world Airbnb dataset for listings in Barcelona, Spain. The raw data comes from Inside Airbnb, a source that provides detailed listing information scraped from Airbnb’s public website.
Figure 1:Barcelona, Spain - Photo by Kaspars Upmanis on Unsplash
The goal is to transform the messy, real-world data into a clean and usable dataset that can be analyzed or used for modeling. Raw listing data often contains:
Missing values
Inconsistent data types
Redundant or irrelevant columns
Cleaning helps ensure the dataset is reliable and ready for analysis.
🗂️ Key Steps in the Cleaning Workflow¶
Import Libraries Use pandas and NumPy for data handling and transformation.
Load the Dataset The dataset is compressed (gzip) and contains information on thousands of Airbnb listings in Barcelona.
Inspect the Data The dataset initially includes many columns (~79) with various data types and missing values.
Select Relevant Features Only a subset of columns important for analysis (e.g., price, room type, number of reviews, location) is retained.
Handle Missing Values Columns with missing or incomplete data are identified. Rows with missing values in critical fields (e.g., bedrooms, bathrooms, review scores) are dropped to ensure quality.
Convert Data Types Some fields, like price, are stored as strings and require conversion to numeric types after removing symbols like “$” and commas.
Standardize Binary Flags Categorical indicators such as whether a host is a superhost are converted into binary numeric formats (0 or 1).
Finalize Clean Data The cleaned dataset contains fewer columns but more reliable information, making it suitable for downstream analysis or modeling. It is also saved as an output file for future use.