How to Use Proxies to Scrape GitHub with Python

in #web-scraping • 4 days ago

Every day, millions of developers converge on GitHub. It’s more than just a code repository—it’s a living, breathing ecosystem where projects grow, ideas spread, and innovation thrives. While GitHub offers an official, robust API, there are times when the data you need isn’t neatly packaged. That’s where web scraping becomes a powerful tool in your toolkit.
This guide will walk you through scraping GitHub, understanding its core elements, and pulling trending repository data with Python—safely and efficiently.

Understanding GitHub

GitHub has transformed the way developers work. At its core, it’s built on Git, a version control system that tracks every change in a project. This eliminates the need for juggling multiple versions of the same file. Every commit tells a story about who made it, when it happened, and what changed.
Repositories hold your project and its history. Branches allow parallel work without overwriting others’ changes. Merges bring finished work together seamlessly. Stars and forks measure popularity and collaboration. Forks, repos, and branches may sound intimidating at first, but once you dive in, the logic clicks.

Preparing Your Python Environment

Before scraping, make sure your environment is ready:

  • Python 3 (latest version) – verify with:

    python3 --version

  • VS Code or another code editor.

  • Proxy credentials.

  • Libraries: requests, BeautifulSoup, sys, typing.

Install pip and required libraries:

python3 -m ensurepip
pip3 install requests beautifulsoup4

Step-by-Step GitHub Scraping

  • Pick your target page: e.g., trending repositories or user profiles.
  • Inspect HTML: locate the elements containing your data.
  • Send HTTP requests: requests library handles this.
  • Parse HTML: BeautifulSoup extracts the content.
  • Extract data: grab repository names, stars, descriptions, etc.
  • Handle pagination: loop through pages to get everything.
  • Store results: CSV, JSON, or database.

Proxy-Enabled Scraping Example

Set your proxy and scraping parameters:

import requests
from bs4 import BeautifulSoup

LANGUAGE = "python"
SINCE = "daily"
PROXY = {
    "host": "PRPXOY HOST",
    "port": "PRPXOY PORT",
    "user": "PRPXOY LOGIN",
    "pass": "PRPXOY PASSWORD",
}

proxy_url = f"http://{PROXY['user']}:{PROXY['pass']}@{PROXY['host']}:{PROXY['port']}"
proxies = {"http": proxy_url, "https": proxy_url}

ip = requests.get("https://api.ipify.org", proxies=proxies).text
print("Your IP:", ip)

url = "https://github.com/trending"
params = {"since": SINCE}
if LANGUAGE:
    params["language"] = LANGUAGE

headers = {"User-Agent": "Mozilla/5.0"}

r = requests.get(url, headers=headers, params=params, proxies=proxies, timeout=20)
soup = BeautifulSoup(r.text, "html.parser")

print("\n GitHub Trending:\n")
for count, repo in enumerate(soup.select("article.Box-row"), start=1):
    name = repo.h2.text.strip().replace("\n", "").replace(" ", "")
    link = "https://github.com" + repo.h2.a["href"]
    desc = repo.p.text.strip() if repo.p else "No description"
    stars_today = repo.select_one("span.d-inline-block.float-sm-right")
    stars_today = stars_today.text.strip() if stars_today else None

    print(f"{count}. {name}")
    print(f"Link: {link}")
    print(f"Stars Today: {stars_today}")
    print(f"Description: {desc}")
    print("-" * 40)

Legal and Ethical Considerations

Scraping GitHub is generally legal if you respect their Terms of Service and avoid sensitive data. Public repositories, user profiles, and metadata like stars or forks are fair game. Private repos and personal user data are off-limits.
GitHub enforces rate limits—roughly 5,000 requests per hour per account—to prevent abuse. Exceed these limits repeatedly, and your account may be suspended. Ethical scraping involves respecting these limits, protecting user privacy, and ensuring you don’t overload servers.

Final Thoughts

Scraping GitHub is useful but needs ethics and planning. Beginners can start with no-code tools like Gemini Bot to pull structured data with simple prompts. Advanced users can add proxies, filters, and logging, tracking trends daily or weekly. With careful setup and responsible practices, GitHub trending data becomes a reliable source for analysis.

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