Scraping GitHub: Stars, Issues, and Developer Trends at Scale

GitHub is the world's largest developer platform, and its public data reveals technology trends, popular tools, and developer sentiment. Here's how to scrape GitHub effectively for trend analysis. What We'll Track Repository star counts and growth rates Issue volume and response times Language and topic trends Developer activity patterns Setup pip install requests pandas matplotlib GitHub API + Scraping Hybrid GitHub has a generous API, so we'll use it where possible and scrape for data the API doesn't expose: import requests import time from datetime import datetime class GitHubTracker : def __init__ ( self , token = None ): self . session = requests . Session () if token : self . session . headers [ " Authorization " ] = f " token { token } " self . session . headers [ " Accept " ] = " application/vnd.github.v3+json " def search_repos ( self , query , sort = " stars " , per_page = 100 ): """ Search repositories with the GitHub API. """ results = [] page = 1 while len ( results ) < pe