How to Scrape GitHub User Profiles with Python and Beautiful Soup

Web scraping is a powerful technique to extract data from websites, and GitHub profiles contain valuable information that can be collected programmatically. This article demonstrates how to build a simple web scraping script that fetches GitHub user profile details and saves them to an Excel file.

Required Libraries

To get started with this project, you’ll need to install the following Python libraries:

• Beautiful Soup 4 – A screen scraping library for extracting data from HTML files
• Requests – A built-in module for making HTTP requests
• Pandas – For data manipulation and exporting to Excel

How the Script Works

The script operates by defining a function that accepts a GitHub username as input and returns the user’s profile details. Here’s a breakdown of the process:

1. Setting Up the Request

The script constructs a URL to the GitHub profile page by appending the username to the GitHub domain. It also includes a user agent in the headers to mimic a browser request, which is essential for web scraping to avoid being blocked.

2. Parsing the HTML

After retrieving the HTML content with a GET request, the script initializes Beautiful Soup to parse the HTML and extract relevant information.

3. Targeting Specific Elements

The script locates specific HTML elements using their CSS class properties:

• Name: Targets a span tag with the class ‘p-name’
• Biography: Finds a div element with a specific class
• Company: Extracts from appropriate HTML elements
• Location: Finds user’s location information
• Repository count: Gets the number of public repositories
• Avatar URL: Extracts the profile picture URL

4. Returning the Data

All extracted details are returned as a single object containing the username, biography, name, company, location, public repository count, and avatar URL.

5. Saving to Excel

Finally, the script uses pandas to create a DataFrame from the extracted data and saves it to an Excel file for easy reference.

Sample Implementation

The implementation is straightforward. You simply need to replace the username in the script and run it. The script then scrapes all the details and saves them to an Excel file that includes the user’s description, name, company, location, public repository count, and avatar URL.

Benefits of This Approach

This web scraping approach offers several advantages:

• No rate limits – Unlike API queries, this scraping method doesn’t have strict limitations
• Comprehensive data – Collects a wide range of profile information
• Easy to modify – The script can be adapted to extract different information
• Structured output – Data is organized in an Excel file for further analysis

Ethical Considerations

When performing web scraping, it’s important to respect website terms of service and avoid excessive requests that might burden servers. Always use web scraping responsibly and consider using official APIs when available.

This Python script provides a practical example of web scraping in action, demonstrating how to extract structured data from web pages using Beautiful Soup and convert it into a usable format with pandas.