Scraping GitHub with Python: Effective Proxy Solutions

Nathan Reynolds

Last edited on May 3, 2025
Last edited on May 3, 2025

Scraping Techniques

Extracting Insights from GitHub with Python and Proxies

As the cornerstone of collaborative software development and version control, GitHub holds a wealth of publicly accessible data. Tapping into this data through web scraping can yield fascinating insights into repository details, language popularity, and emerging project trends.

This guide explores how to effectively scrape GitHub using the power duo of Python and the Beautiful Soup library, with a focus on using proxies for smoother, faster data collection.

Getting Started with GitHub Scraping

Fortunately, much of GitHub presents itself as a standard, static website. Core information isn't typically loaded dynamically via JavaScript, which simplifies our scraping approach. We don't necessarily need complex browser automation tools like Selenium for many common tasks.

The basic process involves two main steps:

  1. Fetch the raw HTML content of the target GitHub page using an HTTP client library.

  2. Parse this downloaded HTML to locate and extract the specific data points you need.

While we're focusing on Python here, similar libraries exist for many other programming languages. It's also worth noting that GitHub offers a comprehensive REST API, which can be a more structured and sometimes preferable alternative to scraping, especially for smaller-scale or hobbyist projects.

Choosing Your Python Toolkit

For scraping GitHub with Python, the go-to libraries are typically Requests and Beautiful Soup.

Requests handles the task of retrieving the webpage's HTML source code, essentially acting like a simplified web browser. Beautiful Soup then steps in to navigate this HTML structure, making it straightforward to pinpoint and pull out the desired information.

Scraping GitHub using Beautiful Soup: A Practical Example

Let's walk through a practical example: scraping information about currently trending Python repositories on GitHub using Requests and Beautiful Soup.

This tutorial assumes you have a basic understanding of these libraries. If you're new to web scraping with Python, you might find our beginner's guide to Python web scraping helpful before proceeding.

Setting Up Your Environment

First things first, ensure you have Python installed on your system. You can download it and find installation instructions on the official Python website if needed.

Next, install the necessary libraries, Requests and Beautiful Soup (bs4), using pip, Python's package installer. Open your terminal or command prompt and run:

Fetching the HTML Content

The first step in our script is to download the HTML of the target page. The Requests library makes this simple:

import requests

# Define the target URL
target_url = 'https://github.com/trending/python'  # Send an HTTP GET request
page_response = requests.get(target_url)

# Check if the request was successful (Status code 200)
if page_response.status_code == 200:
    html_content = page_response.text
    # Proceed with parsing...
else:
    print(f"Failed to retrieve page. Status code: {page_response.status_code}")
    # Handle error appropriately

If the request is successful, the entire HTML source of the page is stored in the html_content variable (accessible via the .text attribute of the response object).

Parsing with Beautiful Soup

With the HTML obtained, we use Beautiful Soup to parse it and extract the data.

First, create a Beautiful Soup object from the HTML content:

from bs4 import BeautifulSoup

# Assuming html_content holds the fetched HTML
soup_parser = BeautifulSoup(html_content, 'html.parser')

Now, we can use methods on the soup_parser object to find specific elements. We'll start by selecting the main container for each repository listed on the trending page. Inspecting the GitHub page reveals these are often within <article> tags.

Inspecting GitHub trending page structure
# Find all article elements, likely containing repo info
repository_elements = soup_parser.find_all('article', class_='Box-row')

Next, we loop through these elements to extract the details we want: the repository name, its star count, and a direct link.

Inspecting elements for name, stars, and link
trending_repos_data = []
for repo_element in repository_elements:
    try:
        # Extract Name (often in an h2 tag)
        name_element = repo_element.find('h2', class_='h3')
        # The name structure might be 'user / repo_name', so we split and clean
        full_name = name_element.a.text.strip().replace('\n', '').replace(' ', '')
        repo_name = full_name.split('/')[-1]

        # Extract Star Count (look for the star icon and its sibling/parent text)
        star_link = repo_element.find('a', href=lambda href: href and '/stargazers' in href)
        star_count_text = star_link.text.strip() if star_link else 'N/A'

        # Extract Link (get the href from the link in the h2)
        link_path = name_element.a['href']
        repo_link = 'https://github.com' + link_path

        repo_info = {
            'name': repo_name,
            'stars': star_count_text,
            'link': repo_link
        }
        trending_repos_data.append(repo_info)
    except AttributeError:
        # Handle cases where an element might be missing (e.g., sponsored repo without stars)
        print("Skipping an element due to missing attributes.")
        continue

Here’s a breakdown of the extraction logic:

  • name: Finds the h2 element containing the repo name, extracts the text from the link within it, cleans up whitespace/newlines, and splits to get just the repository name.

  • stars: Locates the link usually associated with stargazers (containing '/stargazers' in its href) and extracts its text content. Includes error handling for cases where it might not be found.

  • link: Selects the anchor (a) tag within the h2 element and extracts its href attribute, prefixing it with the base GitHub URL.

Finally, let's print the collected data:

import json

# Pretty print the JSON output
print(json.dumps(trending_repos_data, indent=2))

The output should resemble a list of dictionaries like this (specific repos will vary):

[
  {
    "name": "some-cool-project",
    "stars": "12.3k",
    "link": "https://github.com/user/some-cool-project"
  },
  {
    "name": "another-trending-repo",
    "stars": "5,870",
    "link": "https://github.com/another-user/another-trending-repo"
  },
  ...
]

For clarity, here is the complete basic scraper script:

import requests
from bs4 import BeautifulSoup
import json

# Define the target URL
target_url = 'https://github.com/trending/python'

# Send an HTTP GET request
page_response = requests.get(target_url)
trending_repos_data = []

# Check if the request was successful
if page_response.status_code == 200:
    html_content = page_response.text
    # Parse the HTML
    soup_parser = BeautifulSoup(html_content, 'html.parser')
    # Find all article elements containing repo info
    repository_elements = soup_parser.find_all('article', class_='Box-row')

    for repo_element in repository_elements:
        try:
            # Extract Name
            name_element = repo_element.find('h2', class_='h3')
            full_name = name_element.a.text.strip().replace('\n', '').replace(' ', '')
            repo_name = full_name.split('/')[-1]

            # Extract Star Count
            star_link = repo_element.find('a', href=lambda href: href and '/stargazers' in href)
            star_count_text = star_link.text.strip() if star_link else 'N/A'

            # Extract Link
            link_path = name_element.a['href']
            repo_link = 'https://github.com' + link_path

            repo_info = {
                'name': repo_name,
                'stars': star_count_text,
                'link': repo_link
            }
            trending_repos_data.append(repo_info)
        except AttributeError:
            print("Skipping an element due to missing attributes.")
            continue
else:
    print(f"Failed to retrieve page. Status code: {page_response.status_code}")

# Print the collected data
print(json.dumps(trending_repos_data, indent=2))

Going Deeper: Crawling Repository READMEs

The list of trending repositories is just the start. Since we've collected links to individual repositories, we can "crawl" further by visiting each link and extracting more detailed information, such as the content of their README files.

Fetching Data from Multiple Pages

To crawl the READMEs, we need to iterate through our list of repository links, visit each page, and scrape the README content. However, making many requests in rapid succession from the same IP address can trigger GitHub's rate limiting or anti-bot measures.

A naive approach involves adding pauses between requests using Python's built-in time library. Let's import it first:

import time

Then, we can modify our loop to fetch and parse each repository page:

# Assuming 'trending_repos_data' contains the list of dicts with 'link' keys
for repo in trending_repos_data:
    repo_url = repo['link']
    try:
        print(f"Fetching README for: {repo_url}")
        repo_page_response = requests.get(repo_url)

        if repo_page_response.status_code == 200:
            repo_html = repo_page_response.text
            repo_soup = BeautifulSoup(repo_html, 'html.parser')

            # Find the README section (often an element with id="readme")
            readme_element = repo_soup.find('div', id='readme')

            if readme_element:
                readme_text = readme_element.get_text(separator='\n', strip=True) # Extract text content
                # Store or process the readme_text
                print(f"--- README Start ({repo['name']}) ---")
                print(readme_text[:500] + "...") # Print first 500 chars
                print(f"--- README End ({repo['name']}) ---\n")
            else:
                print(f"README section not found for {repo['name']}.")
        else:
            print(f"Failed to fetch {repo_url}. Status: {repo_page_response.status_code}")

        # IMPORTANT: Pause to avoid overwhelming the server
        time.sleep(3) # Wait for 3 seconds before the next request
    except requests.exceptions.RequestException as e:
        print(f"Request error for {repo_url}: {e}")
        time.sleep(5) # Longer pause on error
    except Exception as e:
        print(f"An error occurred while processing {repo['name']}: {e}")
        continue # Move to the next repo if parsing fails

This code iterates through our scraped list, visits each repository's URL, finds the element typically containing the README (often a <div id="readme">), extracts its text content, and prints a snippet.

GitHub repository page showing the README section

The crucial part here is time.sleep(3). This pause is essential without proxies to avoid hitting rate limits. However, it significantly slows down the scraping process. To scrape efficiently and reliably at scale, proxies are the way to go.

Accelerating Scraping with Evomi Proxies

Using proxies, particularly rotating residential proxies, is key to improving scraping speed and reliability. A proxy acts as an intermediary, forwarding your requests to GitHub through a different IP address. With a rotating proxy pool, each request (or small batches of requests) can originate from a unique IP.

Why residential proxies? These proxies use IP addresses assigned by Internet Service Providers (ISPs) to real devices worldwide. Compared to datacenter proxies (which originate from servers), residential IPs appear more like genuine user traffic, significantly reducing the likelihood of detection and blocking by sites like GitHub. At Evomi, we pride ourselves on ethically sourced residential proxies, ensuring quality and reliability, backed by our Swiss commitment to standards.

Integrating Evomi proxies into your Requests script is straightforward. You'll need your proxy credentials and the endpoint details. For Evomi's residential proxies, the format typically looks like this:

# Replace with your actual Evomi username, password, and desired settings
# Example using HTTP endpoint for residential proxies
proxy_user = 'your_evomi_username'
proxy_pass = 'your_evomi_password'
proxy_host = 'rp.evomi.com'
proxy_port_http = 1000
proxy_port_https = 1001

evomi_proxies = {
    'http': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_http}',
    'https': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_https}',
}

# Optional: If you need SOCKS5 (check your plan/needs)
# proxy_port_socks5 = 1002
# evomi_proxies = {
#    'http': f'socks5h://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_socks5}',
#    'https': f'socks5h://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_socks5}',
# }

Now, simply pass this evomi_proxies dictionary to your requests.get calls within the loop. Because each request can now potentially use a different IP, we can remove the artificial delay (time.sleep()):

# Loop through repos, fetching READMEs using proxies
for repo in trending_repos_data:
    repo_url = repo['link']
    try:
        print(f"Fetching README for: {repo_url} via proxy")
        # Pass the proxies dictionary to the get request
        repo_page_response = requests.get(repo_url, proxies=evomi_proxies, timeout=15) # Added timeout

        # ... (rest of the parsing logic remains the same) ...
        if repo_page_response.status_code == 200:
            repo_html = repo_page_response.text
            repo_soup = BeautifulSoup(repo_html, 'html.parser')
            readme_element = repo_soup.find('div', id='readme')
            if readme_element:
                readme_text = readme_element.get_text(separator='\n', strip=True)
                print(f"--- README Start ({repo['name']}) ---")
                print(readme_text[:500] + "...")
                print(f"--- README End ({repo['name']}) ---\n")
            else:
                print(f"README section not found for {repo['name']}.")
        else:
            print(f"Failed to fetch {repo_url}. Status: {repo_page_response.status_code}")

        # NO time.sleep() needed here thanks to rotating proxies!
    except requests.exceptions.ProxyError as e:
        print(f"Proxy error for {repo_url}: {e}")
    except requests.exceptions.Timeout:
        print(f"Request timed out for {repo_url}")
    except requests.exceptions.RequestException as e:
        print(f"Request error for {repo_url}: {e}")
    except Exception as e:
        print(f"An error occurred while processing {repo['name']}: {e}")
        continue

Using reliable proxies like those from Evomi allows for much faster and more robust scraping, essential for larger projects. With competitive pricing (Residential proxies start at just $0.49/GB) and a commitment to ethical sourcing and support, it's a solid choice. You can even explore our plans or potentially test the waters with a free trial if available for your use case.

Here's the consolidated code for the advanced crawler using Evomi proxies:

import requests
from bs4 import BeautifulSoup
import json
# import time # Not needed for delays when using proxies

# --- Initial Scrape of Trending Page (as before) ---
target_url = 'https://github.com/trending/python'
page_response = requests.get(target_url)  # Initial fetch can be direct or via proxy
trending_repos_data = []

if page_response.status_code == 200:
    html_content = page_response.text
    soup_parser = BeautifulSoup(html_content, 'html.parser')
    repository_elements = soup_parser.find_all('article', class_='Box-row')

    for repo_element in repository_elements:
        try:
            name_element = repo_element.find('h2', class_='h3')
            full_name = name_element.a.text.strip().replace('\n', '').replace(' ', '')
            repo_name = full_name.split('/')[-1]
            star_link = repo_element.find('a', href=lambda href: href and '/stargazers' in href)
            star_count_text = star_link.text.strip() if star_link else 'N/A'
            link_path = name_element.a['href']
            repo_link = 'https://github.com' + link_path
            repo_info = {
                'name': repo_name,
                'stars': star_count_text,
                'link': repo_link
            }
            trending_repos_data.append(repo_info)
        except AttributeError:
            print("Skipping an element during initial scrape.")
            continue
else:
    print(f"Failed to retrieve trending page. Status code: {page_response.status_code}")
    exit()  # Exit if the initial scrape failed

# --- Configure Evomi Proxies ---
proxy_user = 'your_evomi_username'  # Replace with your details
proxy_pass = 'your_evomi_password'  # Replace with your details
proxy_host = 'rp.evomi.com'
proxy_port_http = 1000
proxy_port_https = 1001

evomi_proxies = {
   'http': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_http}',
   'https': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_https}',
}

# --- Crawl Individual Repo READMEs using Proxies ---
all_readme_data = {}  # Dictionary to store READMEs by repo name

print(f"\n--- Starting README Crawl for {len(trending_repos_data)} repositories ---")

for repo in trending_repos_data:
    repo_url = repo['link']
    repo_name = repo['name']
    try:
        print(f"Fetching README for: {repo_name} ({repo_url})")
        # Use proxies for the individual repo requests
        repo_page_response = requests.get(repo_url, proxies=evomi_proxies, timeout=20)  # Increased timeout slightly

        if repo_page_response.status_code == 200:
            repo_html = repo_page_response.text
            repo_soup = BeautifulSoup(repo_html, 'html.parser')
            readme_element = repo_soup.find('div', id='readme')

            if readme_element:
                readme_text = readme_element.get_text(separator='\n', strip=True)
                all_readme_data[repo_name] = readme_text
                print(f"Successfully fetched README for {repo_name}. Length: {len(readme_text)} chars.")
            else:
                print(f"README section not found for {repo_name}.")
                all_readme_data[repo_name] = None  # Indicate README wasn't found
        else:
            print(f"Failed to fetch {repo_url}. Status: {repo_page_response.status_code}")
            all_readme_data[repo_name] = f"Error: Status {repo_page_response.status_code}"

    except requests.exceptions.ProxyError as e:
        print(f"Proxy error for {repo_url}: {e}")
        all_readme_data[repo_name] = "Error: Proxy Issue"
    except requests.exceptions.Timeout:
        print(f"Request timed out for {repo_url}")
        all_readme_data[repo_name] = "Error: Timeout"
    except requests.exceptions.RequestException as e:
        print(f"Request error for {repo_url}: {e}")
        all_readme_data[repo_name] = f"Error: Request Failed ({e})"
    except Exception as e:
        print(f"An error occurred while processing {repo_name}: {e}")
        all_readme_data[repo_name] = f"Error: Parsing Failed ({e})"
        continue

print("\n--- README Crawl Complete ---")

# Now 'all_readme_data' dictionary contains the extracted READMEs (or errors)
# Example: Accessing a specific README
# print(all_readme_data.get('some-cool-project', 'Not Found'))

Important Considerations for GitHub Scraping

When scraping any website, especially one as widely used as GitHub, it's crucial to proceed responsibly and ethically. While GitHub's data is largely public, adhering to best practices ensures you don't disrupt the service and stay compliant.

Keep these points in mind:

  • Mind the Rate Limits: GitHub implements rate limits to prevent abuse. While proxies help distribute requests, excessively aggressive scraping can still lead to temporary blocks. Scrape at a reasonable pace, especially if not using a large, diverse proxy pool.

  • Cache Your Results: Avoid repeatedly scraping the same page if the data hasn't changed. Store (cache) the data you've already collected locally. This reduces load on GitHub's servers and speeds up your subsequent analyses.

  • Check the GitHub API First: Before committing to scraping, investigate if the official GitHub API provides the data you need. The API offers structured data access, is generally more stable than scraping HTML, and is the officially supported method for data retrieval. Scraping is better suited for data not readily available via the API or for specific exploratory analysis.

  • Review Terms of Service: Familiarize yourself with GitHub's Terms of Service regarding data access and automated retrieval. While scraping public data isn't explicitly forbidden for logged-out users, excessive scraping could be flagged.

Potential Use Cases for Scraped GitHub Data

The data gathered from scraping GitHub can fuel various insightful applications:

  1. Software Development Trend Analysis: Track the rise and fall of programming languages, frameworks, and libraries based on repository popularity, star counts, and activity.

  2. Academic and Market Research: Analyze code structures, development practices, collaboration patterns, or the evolution of open-source projects.

  3. Competitive Intelligence: Monitor competitors' public repositories to understand their technology choices, development velocity, and open-source strategies.

  4. User Feedback Aggregation: Systematically gather insights from issue trackers and pull request comments across multiple relevant repositories.

  5. Feature Request Identification: Spot emerging trends in user demands and feature requests within specific software ecosystems.

Conclusion

Scraping GitHub with Python, Requests, and Beautiful Soup opens up a rich source of data for understanding the software development landscape. From tracking trending projects to analyzing specific repository details like READMEs, the possibilities are vast.

While simple scraping is feasible for basic tasks, incorporating reliable, ethically sourced residential proxies—like those offered by Evomi—is crucial for scaling your efforts, improving speed, and avoiding IP blocks. By combining smart scraping techniques with robust proxy infrastructure, you can effectively gather valuable insights for research, analysis, or market intelligence.

Extracting Insights from GitHub with Python and Proxies

As the cornerstone of collaborative software development and version control, GitHub holds a wealth of publicly accessible data. Tapping into this data through web scraping can yield fascinating insights into repository details, language popularity, and emerging project trends.

This guide explores how to effectively scrape GitHub using the power duo of Python and the Beautiful Soup library, with a focus on using proxies for smoother, faster data collection.

Getting Started with GitHub Scraping

Fortunately, much of GitHub presents itself as a standard, static website. Core information isn't typically loaded dynamically via JavaScript, which simplifies our scraping approach. We don't necessarily need complex browser automation tools like Selenium for many common tasks.

The basic process involves two main steps:

  1. Fetch the raw HTML content of the target GitHub page using an HTTP client library.

  2. Parse this downloaded HTML to locate and extract the specific data points you need.

While we're focusing on Python here, similar libraries exist for many other programming languages. It's also worth noting that GitHub offers a comprehensive REST API, which can be a more structured and sometimes preferable alternative to scraping, especially for smaller-scale or hobbyist projects.

Choosing Your Python Toolkit

For scraping GitHub with Python, the go-to libraries are typically Requests and Beautiful Soup.

Requests handles the task of retrieving the webpage's HTML source code, essentially acting like a simplified web browser. Beautiful Soup then steps in to navigate this HTML structure, making it straightforward to pinpoint and pull out the desired information.

Scraping GitHub using Beautiful Soup: A Practical Example

Let's walk through a practical example: scraping information about currently trending Python repositories on GitHub using Requests and Beautiful Soup.

This tutorial assumes you have a basic understanding of these libraries. If you're new to web scraping with Python, you might find our beginner's guide to Python web scraping helpful before proceeding.

Setting Up Your Environment

First things first, ensure you have Python installed on your system. You can download it and find installation instructions on the official Python website if needed.

Next, install the necessary libraries, Requests and Beautiful Soup (bs4), using pip, Python's package installer. Open your terminal or command prompt and run:

Fetching the HTML Content

The first step in our script is to download the HTML of the target page. The Requests library makes this simple:

import requests

# Define the target URL
target_url = 'https://github.com/trending/python'  # Send an HTTP GET request
page_response = requests.get(target_url)

# Check if the request was successful (Status code 200)
if page_response.status_code == 200:
    html_content = page_response.text
    # Proceed with parsing...
else:
    print(f"Failed to retrieve page. Status code: {page_response.status_code}")
    # Handle error appropriately

If the request is successful, the entire HTML source of the page is stored in the html_content variable (accessible via the .text attribute of the response object).

Parsing with Beautiful Soup

With the HTML obtained, we use Beautiful Soup to parse it and extract the data.

First, create a Beautiful Soup object from the HTML content:

from bs4 import BeautifulSoup

# Assuming html_content holds the fetched HTML
soup_parser = BeautifulSoup(html_content, 'html.parser')

Now, we can use methods on the soup_parser object to find specific elements. We'll start by selecting the main container for each repository listed on the trending page. Inspecting the GitHub page reveals these are often within <article> tags.

Inspecting GitHub trending page structure
# Find all article elements, likely containing repo info
repository_elements = soup_parser.find_all('article', class_='Box-row')

Next, we loop through these elements to extract the details we want: the repository name, its star count, and a direct link.

Inspecting elements for name, stars, and link
trending_repos_data = []
for repo_element in repository_elements:
    try:
        # Extract Name (often in an h2 tag)
        name_element = repo_element.find('h2', class_='h3')
        # The name structure might be 'user / repo_name', so we split and clean
        full_name = name_element.a.text.strip().replace('\n', '').replace(' ', '')
        repo_name = full_name.split('/')[-1]

        # Extract Star Count (look for the star icon and its sibling/parent text)
        star_link = repo_element.find('a', href=lambda href: href and '/stargazers' in href)
        star_count_text = star_link.text.strip() if star_link else 'N/A'

        # Extract Link (get the href from the link in the h2)
        link_path = name_element.a['href']
        repo_link = 'https://github.com' + link_path

        repo_info = {
            'name': repo_name,
            'stars': star_count_text,
            'link': repo_link
        }
        trending_repos_data.append(repo_info)
    except AttributeError:
        # Handle cases where an element might be missing (e.g., sponsored repo without stars)
        print("Skipping an element due to missing attributes.")
        continue

Here’s a breakdown of the extraction logic:

  • name: Finds the h2 element containing the repo name, extracts the text from the link within it, cleans up whitespace/newlines, and splits to get just the repository name.

  • stars: Locates the link usually associated with stargazers (containing '/stargazers' in its href) and extracts its text content. Includes error handling for cases where it might not be found.

  • link: Selects the anchor (a) tag within the h2 element and extracts its href attribute, prefixing it with the base GitHub URL.

Finally, let's print the collected data:

import json

# Pretty print the JSON output
print(json.dumps(trending_repos_data, indent=2))

The output should resemble a list of dictionaries like this (specific repos will vary):

[
  {
    "name": "some-cool-project",
    "stars": "12.3k",
    "link": "https://github.com/user/some-cool-project"
  },
  {
    "name": "another-trending-repo",
    "stars": "5,870",
    "link": "https://github.com/another-user/another-trending-repo"
  },
  ...
]

For clarity, here is the complete basic scraper script:

import requests
from bs4 import BeautifulSoup
import json

# Define the target URL
target_url = 'https://github.com/trending/python'

# Send an HTTP GET request
page_response = requests.get(target_url)
trending_repos_data = []

# Check if the request was successful
if page_response.status_code == 200:
    html_content = page_response.text
    # Parse the HTML
    soup_parser = BeautifulSoup(html_content, 'html.parser')
    # Find all article elements containing repo info
    repository_elements = soup_parser.find_all('article', class_='Box-row')

    for repo_element in repository_elements:
        try:
            # Extract Name
            name_element = repo_element.find('h2', class_='h3')
            full_name = name_element.a.text.strip().replace('\n', '').replace(' ', '')
            repo_name = full_name.split('/')[-1]

            # Extract Star Count
            star_link = repo_element.find('a', href=lambda href: href and '/stargazers' in href)
            star_count_text = star_link.text.strip() if star_link else 'N/A'

            # Extract Link
            link_path = name_element.a['href']
            repo_link = 'https://github.com' + link_path

            repo_info = {
                'name': repo_name,
                'stars': star_count_text,
                'link': repo_link
            }
            trending_repos_data.append(repo_info)
        except AttributeError:
            print("Skipping an element due to missing attributes.")
            continue
else:
    print(f"Failed to retrieve page. Status code: {page_response.status_code}")

# Print the collected data
print(json.dumps(trending_repos_data, indent=2))

Going Deeper: Crawling Repository READMEs

The list of trending repositories is just the start. Since we've collected links to individual repositories, we can "crawl" further by visiting each link and extracting more detailed information, such as the content of their README files.

Fetching Data from Multiple Pages

To crawl the READMEs, we need to iterate through our list of repository links, visit each page, and scrape the README content. However, making many requests in rapid succession from the same IP address can trigger GitHub's rate limiting or anti-bot measures.

A naive approach involves adding pauses between requests using Python's built-in time library. Let's import it first:

import time

Then, we can modify our loop to fetch and parse each repository page:

# Assuming 'trending_repos_data' contains the list of dicts with 'link' keys
for repo in trending_repos_data:
    repo_url = repo['link']
    try:
        print(f"Fetching README for: {repo_url}")
        repo_page_response = requests.get(repo_url)

        if repo_page_response.status_code == 200:
            repo_html = repo_page_response.text
            repo_soup = BeautifulSoup(repo_html, 'html.parser')

            # Find the README section (often an element with id="readme")
            readme_element = repo_soup.find('div', id='readme')

            if readme_element:
                readme_text = readme_element.get_text(separator='\n', strip=True) # Extract text content
                # Store or process the readme_text
                print(f"--- README Start ({repo['name']}) ---")
                print(readme_text[:500] + "...") # Print first 500 chars
                print(f"--- README End ({repo['name']}) ---\n")
            else:
                print(f"README section not found for {repo['name']}.")
        else:
            print(f"Failed to fetch {repo_url}. Status: {repo_page_response.status_code}")

        # IMPORTANT: Pause to avoid overwhelming the server
        time.sleep(3) # Wait for 3 seconds before the next request
    except requests.exceptions.RequestException as e:
        print(f"Request error for {repo_url}: {e}")
        time.sleep(5) # Longer pause on error
    except Exception as e:
        print(f"An error occurred while processing {repo['name']}: {e}")
        continue # Move to the next repo if parsing fails

This code iterates through our scraped list, visits each repository's URL, finds the element typically containing the README (often a <div id="readme">), extracts its text content, and prints a snippet.

GitHub repository page showing the README section

The crucial part here is time.sleep(3). This pause is essential without proxies to avoid hitting rate limits. However, it significantly slows down the scraping process. To scrape efficiently and reliably at scale, proxies are the way to go.

Accelerating Scraping with Evomi Proxies

Using proxies, particularly rotating residential proxies, is key to improving scraping speed and reliability. A proxy acts as an intermediary, forwarding your requests to GitHub through a different IP address. With a rotating proxy pool, each request (or small batches of requests) can originate from a unique IP.

Why residential proxies? These proxies use IP addresses assigned by Internet Service Providers (ISPs) to real devices worldwide. Compared to datacenter proxies (which originate from servers), residential IPs appear more like genuine user traffic, significantly reducing the likelihood of detection and blocking by sites like GitHub. At Evomi, we pride ourselves on ethically sourced residential proxies, ensuring quality and reliability, backed by our Swiss commitment to standards.

Integrating Evomi proxies into your Requests script is straightforward. You'll need your proxy credentials and the endpoint details. For Evomi's residential proxies, the format typically looks like this:

# Replace with your actual Evomi username, password, and desired settings
# Example using HTTP endpoint for residential proxies
proxy_user = 'your_evomi_username'
proxy_pass = 'your_evomi_password'
proxy_host = 'rp.evomi.com'
proxy_port_http = 1000
proxy_port_https = 1001

evomi_proxies = {
    'http': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_http}',
    'https': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_https}',
}

# Optional: If you need SOCKS5 (check your plan/needs)
# proxy_port_socks5 = 1002
# evomi_proxies = {
#    'http': f'socks5h://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_socks5}',
#    'https': f'socks5h://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_socks5}',
# }

Now, simply pass this evomi_proxies dictionary to your requests.get calls within the loop. Because each request can now potentially use a different IP, we can remove the artificial delay (time.sleep()):

# Loop through repos, fetching READMEs using proxies
for repo in trending_repos_data:
    repo_url = repo['link']
    try:
        print(f"Fetching README for: {repo_url} via proxy")
        # Pass the proxies dictionary to the get request
        repo_page_response = requests.get(repo_url, proxies=evomi_proxies, timeout=15) # Added timeout

        # ... (rest of the parsing logic remains the same) ...
        if repo_page_response.status_code == 200:
            repo_html = repo_page_response.text
            repo_soup = BeautifulSoup(repo_html, 'html.parser')
            readme_element = repo_soup.find('div', id='readme')
            if readme_element:
                readme_text = readme_element.get_text(separator='\n', strip=True)
                print(f"--- README Start ({repo['name']}) ---")
                print(readme_text[:500] + "...")
                print(f"--- README End ({repo['name']}) ---\n")
            else:
                print(f"README section not found for {repo['name']}.")
        else:
            print(f"Failed to fetch {repo_url}. Status: {repo_page_response.status_code}")

        # NO time.sleep() needed here thanks to rotating proxies!
    except requests.exceptions.ProxyError as e:
        print(f"Proxy error for {repo_url}: {e}")
    except requests.exceptions.Timeout:
        print(f"Request timed out for {repo_url}")
    except requests.exceptions.RequestException as e:
        print(f"Request error for {repo_url}: {e}")
    except Exception as e:
        print(f"An error occurred while processing {repo['name']}: {e}")
        continue

Using reliable proxies like those from Evomi allows for much faster and more robust scraping, essential for larger projects. With competitive pricing (Residential proxies start at just $0.49/GB) and a commitment to ethical sourcing and support, it's a solid choice. You can even explore our plans or potentially test the waters with a free trial if available for your use case.

Here's the consolidated code for the advanced crawler using Evomi proxies:

import requests
from bs4 import BeautifulSoup
import json
# import time # Not needed for delays when using proxies

# --- Initial Scrape of Trending Page (as before) ---
target_url = 'https://github.com/trending/python'
page_response = requests.get(target_url)  # Initial fetch can be direct or via proxy
trending_repos_data = []

if page_response.status_code == 200:
    html_content = page_response.text
    soup_parser = BeautifulSoup(html_content, 'html.parser')
    repository_elements = soup_parser.find_all('article', class_='Box-row')

    for repo_element in repository_elements:
        try:
            name_element = repo_element.find('h2', class_='h3')
            full_name = name_element.a.text.strip().replace('\n', '').replace(' ', '')
            repo_name = full_name.split('/')[-1]
            star_link = repo_element.find('a', href=lambda href: href and '/stargazers' in href)
            star_count_text = star_link.text.strip() if star_link else 'N/A'
            link_path = name_element.a['href']
            repo_link = 'https://github.com' + link_path
            repo_info = {
                'name': repo_name,
                'stars': star_count_text,
                'link': repo_link
            }
            trending_repos_data.append(repo_info)
        except AttributeError:
            print("Skipping an element during initial scrape.")
            continue
else:
    print(f"Failed to retrieve trending page. Status code: {page_response.status_code}")
    exit()  # Exit if the initial scrape failed

# --- Configure Evomi Proxies ---
proxy_user = 'your_evomi_username'  # Replace with your details
proxy_pass = 'your_evomi_password'  # Replace with your details
proxy_host = 'rp.evomi.com'
proxy_port_http = 1000
proxy_port_https = 1001

evomi_proxies = {
   'http': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_http}',
   'https': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_https}',
}

# --- Crawl Individual Repo READMEs using Proxies ---
all_readme_data = {}  # Dictionary to store READMEs by repo name

print(f"\n--- Starting README Crawl for {len(trending_repos_data)} repositories ---")

for repo in trending_repos_data:
    repo_url = repo['link']
    repo_name = repo['name']
    try:
        print(f"Fetching README for: {repo_name} ({repo_url})")
        # Use proxies for the individual repo requests
        repo_page_response = requests.get(repo_url, proxies=evomi_proxies, timeout=20)  # Increased timeout slightly

        if repo_page_response.status_code == 200:
            repo_html = repo_page_response.text
            repo_soup = BeautifulSoup(repo_html, 'html.parser')
            readme_element = repo_soup.find('div', id='readme')

            if readme_element:
                readme_text = readme_element.get_text(separator='\n', strip=True)
                all_readme_data[repo_name] = readme_text
                print(f"Successfully fetched README for {repo_name}. Length: {len(readme_text)} chars.")
            else:
                print(f"README section not found for {repo_name}.")
                all_readme_data[repo_name] = None  # Indicate README wasn't found
        else:
            print(f"Failed to fetch {repo_url}. Status: {repo_page_response.status_code}")
            all_readme_data[repo_name] = f"Error: Status {repo_page_response.status_code}"

    except requests.exceptions.ProxyError as e:
        print(f"Proxy error for {repo_url}: {e}")
        all_readme_data[repo_name] = "Error: Proxy Issue"
    except requests.exceptions.Timeout:
        print(f"Request timed out for {repo_url}")
        all_readme_data[repo_name] = "Error: Timeout"
    except requests.exceptions.RequestException as e:
        print(f"Request error for {repo_url}: {e}")
        all_readme_data[repo_name] = f"Error: Request Failed ({e})"
    except Exception as e:
        print(f"An error occurred while processing {repo_name}: {e}")
        all_readme_data[repo_name] = f"Error: Parsing Failed ({e})"
        continue

print("\n--- README Crawl Complete ---")

# Now 'all_readme_data' dictionary contains the extracted READMEs (or errors)
# Example: Accessing a specific README
# print(all_readme_data.get('some-cool-project', 'Not Found'))

Important Considerations for GitHub Scraping

When scraping any website, especially one as widely used as GitHub, it's crucial to proceed responsibly and ethically. While GitHub's data is largely public, adhering to best practices ensures you don't disrupt the service and stay compliant.

Keep these points in mind:

  • Mind the Rate Limits: GitHub implements rate limits to prevent abuse. While proxies help distribute requests, excessively aggressive scraping can still lead to temporary blocks. Scrape at a reasonable pace, especially if not using a large, diverse proxy pool.

  • Cache Your Results: Avoid repeatedly scraping the same page if the data hasn't changed. Store (cache) the data you've already collected locally. This reduces load on GitHub's servers and speeds up your subsequent analyses.

  • Check the GitHub API First: Before committing to scraping, investigate if the official GitHub API provides the data you need. The API offers structured data access, is generally more stable than scraping HTML, and is the officially supported method for data retrieval. Scraping is better suited for data not readily available via the API or for specific exploratory analysis.

  • Review Terms of Service: Familiarize yourself with GitHub's Terms of Service regarding data access and automated retrieval. While scraping public data isn't explicitly forbidden for logged-out users, excessive scraping could be flagged.

Potential Use Cases for Scraped GitHub Data

The data gathered from scraping GitHub can fuel various insightful applications:

  1. Software Development Trend Analysis: Track the rise and fall of programming languages, frameworks, and libraries based on repository popularity, star counts, and activity.

  2. Academic and Market Research: Analyze code structures, development practices, collaboration patterns, or the evolution of open-source projects.

  3. Competitive Intelligence: Monitor competitors' public repositories to understand their technology choices, development velocity, and open-source strategies.

  4. User Feedback Aggregation: Systematically gather insights from issue trackers and pull request comments across multiple relevant repositories.

  5. Feature Request Identification: Spot emerging trends in user demands and feature requests within specific software ecosystems.

Conclusion

Scraping GitHub with Python, Requests, and Beautiful Soup opens up a rich source of data for understanding the software development landscape. From tracking trending projects to analyzing specific repository details like READMEs, the possibilities are vast.

While simple scraping is feasible for basic tasks, incorporating reliable, ethically sourced residential proxies—like those offered by Evomi—is crucial for scaling your efforts, improving speed, and avoiding IP blocks. By combining smart scraping techniques with robust proxy infrastructure, you can effectively gather valuable insights for research, analysis, or market intelligence.

Extracting Insights from GitHub with Python and Proxies

As the cornerstone of collaborative software development and version control, GitHub holds a wealth of publicly accessible data. Tapping into this data through web scraping can yield fascinating insights into repository details, language popularity, and emerging project trends.

This guide explores how to effectively scrape GitHub using the power duo of Python and the Beautiful Soup library, with a focus on using proxies for smoother, faster data collection.

Getting Started with GitHub Scraping

Fortunately, much of GitHub presents itself as a standard, static website. Core information isn't typically loaded dynamically via JavaScript, which simplifies our scraping approach. We don't necessarily need complex browser automation tools like Selenium for many common tasks.

The basic process involves two main steps:

  1. Fetch the raw HTML content of the target GitHub page using an HTTP client library.

  2. Parse this downloaded HTML to locate and extract the specific data points you need.

While we're focusing on Python here, similar libraries exist for many other programming languages. It's also worth noting that GitHub offers a comprehensive REST API, which can be a more structured and sometimes preferable alternative to scraping, especially for smaller-scale or hobbyist projects.

Choosing Your Python Toolkit

For scraping GitHub with Python, the go-to libraries are typically Requests and Beautiful Soup.

Requests handles the task of retrieving the webpage's HTML source code, essentially acting like a simplified web browser. Beautiful Soup then steps in to navigate this HTML structure, making it straightforward to pinpoint and pull out the desired information.

Scraping GitHub using Beautiful Soup: A Practical Example

Let's walk through a practical example: scraping information about currently trending Python repositories on GitHub using Requests and Beautiful Soup.

This tutorial assumes you have a basic understanding of these libraries. If you're new to web scraping with Python, you might find our beginner's guide to Python web scraping helpful before proceeding.

Setting Up Your Environment

First things first, ensure you have Python installed on your system. You can download it and find installation instructions on the official Python website if needed.

Next, install the necessary libraries, Requests and Beautiful Soup (bs4), using pip, Python's package installer. Open your terminal or command prompt and run:

Fetching the HTML Content

The first step in our script is to download the HTML of the target page. The Requests library makes this simple:

import requests

# Define the target URL
target_url = 'https://github.com/trending/python'  # Send an HTTP GET request
page_response = requests.get(target_url)

# Check if the request was successful (Status code 200)
if page_response.status_code == 200:
    html_content = page_response.text
    # Proceed with parsing...
else:
    print(f"Failed to retrieve page. Status code: {page_response.status_code}")
    # Handle error appropriately

If the request is successful, the entire HTML source of the page is stored in the html_content variable (accessible via the .text attribute of the response object).

Parsing with Beautiful Soup

With the HTML obtained, we use Beautiful Soup to parse it and extract the data.

First, create a Beautiful Soup object from the HTML content:

from bs4 import BeautifulSoup

# Assuming html_content holds the fetched HTML
soup_parser = BeautifulSoup(html_content, 'html.parser')

Now, we can use methods on the soup_parser object to find specific elements. We'll start by selecting the main container for each repository listed on the trending page. Inspecting the GitHub page reveals these are often within <article> tags.

Inspecting GitHub trending page structure
# Find all article elements, likely containing repo info
repository_elements = soup_parser.find_all('article', class_='Box-row')

Next, we loop through these elements to extract the details we want: the repository name, its star count, and a direct link.

Inspecting elements for name, stars, and link
trending_repos_data = []
for repo_element in repository_elements:
    try:
        # Extract Name (often in an h2 tag)
        name_element = repo_element.find('h2', class_='h3')
        # The name structure might be 'user / repo_name', so we split and clean
        full_name = name_element.a.text.strip().replace('\n', '').replace(' ', '')
        repo_name = full_name.split('/')[-1]

        # Extract Star Count (look for the star icon and its sibling/parent text)
        star_link = repo_element.find('a', href=lambda href: href and '/stargazers' in href)
        star_count_text = star_link.text.strip() if star_link else 'N/A'

        # Extract Link (get the href from the link in the h2)
        link_path = name_element.a['href']
        repo_link = 'https://github.com' + link_path

        repo_info = {
            'name': repo_name,
            'stars': star_count_text,
            'link': repo_link
        }
        trending_repos_data.append(repo_info)
    except AttributeError:
        # Handle cases where an element might be missing (e.g., sponsored repo without stars)
        print("Skipping an element due to missing attributes.")
        continue

Here’s a breakdown of the extraction logic:

  • name: Finds the h2 element containing the repo name, extracts the text from the link within it, cleans up whitespace/newlines, and splits to get just the repository name.

  • stars: Locates the link usually associated with stargazers (containing '/stargazers' in its href) and extracts its text content. Includes error handling for cases where it might not be found.

  • link: Selects the anchor (a) tag within the h2 element and extracts its href attribute, prefixing it with the base GitHub URL.

Finally, let's print the collected data:

import json

# Pretty print the JSON output
print(json.dumps(trending_repos_data, indent=2))

The output should resemble a list of dictionaries like this (specific repos will vary):

[
  {
    "name": "some-cool-project",
    "stars": "12.3k",
    "link": "https://github.com/user/some-cool-project"
  },
  {
    "name": "another-trending-repo",
    "stars": "5,870",
    "link": "https://github.com/another-user/another-trending-repo"
  },
  ...
]

For clarity, here is the complete basic scraper script:

import requests
from bs4 import BeautifulSoup
import json

# Define the target URL
target_url = 'https://github.com/trending/python'

# Send an HTTP GET request
page_response = requests.get(target_url)
trending_repos_data = []

# Check if the request was successful
if page_response.status_code == 200:
    html_content = page_response.text
    # Parse the HTML
    soup_parser = BeautifulSoup(html_content, 'html.parser')
    # Find all article elements containing repo info
    repository_elements = soup_parser.find_all('article', class_='Box-row')

    for repo_element in repository_elements:
        try:
            # Extract Name
            name_element = repo_element.find('h2', class_='h3')
            full_name = name_element.a.text.strip().replace('\n', '').replace(' ', '')
            repo_name = full_name.split('/')[-1]

            # Extract Star Count
            star_link = repo_element.find('a', href=lambda href: href and '/stargazers' in href)
            star_count_text = star_link.text.strip() if star_link else 'N/A'

            # Extract Link
            link_path = name_element.a['href']
            repo_link = 'https://github.com' + link_path

            repo_info = {
                'name': repo_name,
                'stars': star_count_text,
                'link': repo_link
            }
            trending_repos_data.append(repo_info)
        except AttributeError:
            print("Skipping an element due to missing attributes.")
            continue
else:
    print(f"Failed to retrieve page. Status code: {page_response.status_code}")

# Print the collected data
print(json.dumps(trending_repos_data, indent=2))

Going Deeper: Crawling Repository READMEs

The list of trending repositories is just the start. Since we've collected links to individual repositories, we can "crawl" further by visiting each link and extracting more detailed information, such as the content of their README files.

Fetching Data from Multiple Pages

To crawl the READMEs, we need to iterate through our list of repository links, visit each page, and scrape the README content. However, making many requests in rapid succession from the same IP address can trigger GitHub's rate limiting or anti-bot measures.

A naive approach involves adding pauses between requests using Python's built-in time library. Let's import it first:

import time

Then, we can modify our loop to fetch and parse each repository page:

# Assuming 'trending_repos_data' contains the list of dicts with 'link' keys
for repo in trending_repos_data:
    repo_url = repo['link']
    try:
        print(f"Fetching README for: {repo_url}")
        repo_page_response = requests.get(repo_url)

        if repo_page_response.status_code == 200:
            repo_html = repo_page_response.text
            repo_soup = BeautifulSoup(repo_html, 'html.parser')

            # Find the README section (often an element with id="readme")
            readme_element = repo_soup.find('div', id='readme')

            if readme_element:
                readme_text = readme_element.get_text(separator='\n', strip=True) # Extract text content
                # Store or process the readme_text
                print(f"--- README Start ({repo['name']}) ---")
                print(readme_text[:500] + "...") # Print first 500 chars
                print(f"--- README End ({repo['name']}) ---\n")
            else:
                print(f"README section not found for {repo['name']}.")
        else:
            print(f"Failed to fetch {repo_url}. Status: {repo_page_response.status_code}")

        # IMPORTANT: Pause to avoid overwhelming the server
        time.sleep(3) # Wait for 3 seconds before the next request
    except requests.exceptions.RequestException as e:
        print(f"Request error for {repo_url}: {e}")
        time.sleep(5) # Longer pause on error
    except Exception as e:
        print(f"An error occurred while processing {repo['name']}: {e}")
        continue # Move to the next repo if parsing fails

This code iterates through our scraped list, visits each repository's URL, finds the element typically containing the README (often a <div id="readme">), extracts its text content, and prints a snippet.

GitHub repository page showing the README section

The crucial part here is time.sleep(3). This pause is essential without proxies to avoid hitting rate limits. However, it significantly slows down the scraping process. To scrape efficiently and reliably at scale, proxies are the way to go.

Accelerating Scraping with Evomi Proxies

Using proxies, particularly rotating residential proxies, is key to improving scraping speed and reliability. A proxy acts as an intermediary, forwarding your requests to GitHub through a different IP address. With a rotating proxy pool, each request (or small batches of requests) can originate from a unique IP.

Why residential proxies? These proxies use IP addresses assigned by Internet Service Providers (ISPs) to real devices worldwide. Compared to datacenter proxies (which originate from servers), residential IPs appear more like genuine user traffic, significantly reducing the likelihood of detection and blocking by sites like GitHub. At Evomi, we pride ourselves on ethically sourced residential proxies, ensuring quality and reliability, backed by our Swiss commitment to standards.

Integrating Evomi proxies into your Requests script is straightforward. You'll need your proxy credentials and the endpoint details. For Evomi's residential proxies, the format typically looks like this:

# Replace with your actual Evomi username, password, and desired settings
# Example using HTTP endpoint for residential proxies
proxy_user = 'your_evomi_username'
proxy_pass = 'your_evomi_password'
proxy_host = 'rp.evomi.com'
proxy_port_http = 1000
proxy_port_https = 1001

evomi_proxies = {
    'http': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_http}',
    'https': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_https}',
}

# Optional: If you need SOCKS5 (check your plan/needs)
# proxy_port_socks5 = 1002
# evomi_proxies = {
#    'http': f'socks5h://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_socks5}',
#    'https': f'socks5h://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_socks5}',
# }

Now, simply pass this evomi_proxies dictionary to your requests.get calls within the loop. Because each request can now potentially use a different IP, we can remove the artificial delay (time.sleep()):

# Loop through repos, fetching READMEs using proxies
for repo in trending_repos_data:
    repo_url = repo['link']
    try:
        print(f"Fetching README for: {repo_url} via proxy")
        # Pass the proxies dictionary to the get request
        repo_page_response = requests.get(repo_url, proxies=evomi_proxies, timeout=15) # Added timeout

        # ... (rest of the parsing logic remains the same) ...
        if repo_page_response.status_code == 200:
            repo_html = repo_page_response.text
            repo_soup = BeautifulSoup(repo_html, 'html.parser')
            readme_element = repo_soup.find('div', id='readme')
            if readme_element:
                readme_text = readme_element.get_text(separator='\n', strip=True)
                print(f"--- README Start ({repo['name']}) ---")
                print(readme_text[:500] + "...")
                print(f"--- README End ({repo['name']}) ---\n")
            else:
                print(f"README section not found for {repo['name']}.")
        else:
            print(f"Failed to fetch {repo_url}. Status: {repo_page_response.status_code}")

        # NO time.sleep() needed here thanks to rotating proxies!
    except requests.exceptions.ProxyError as e:
        print(f"Proxy error for {repo_url}: {e}")
    except requests.exceptions.Timeout:
        print(f"Request timed out for {repo_url}")
    except requests.exceptions.RequestException as e:
        print(f"Request error for {repo_url}: {e}")
    except Exception as e:
        print(f"An error occurred while processing {repo['name']}: {e}")
        continue

Using reliable proxies like those from Evomi allows for much faster and more robust scraping, essential for larger projects. With competitive pricing (Residential proxies start at just $0.49/GB) and a commitment to ethical sourcing and support, it's a solid choice. You can even explore our plans or potentially test the waters with a free trial if available for your use case.

Here's the consolidated code for the advanced crawler using Evomi proxies:

import requests
from bs4 import BeautifulSoup
import json
# import time # Not needed for delays when using proxies

# --- Initial Scrape of Trending Page (as before) ---
target_url = 'https://github.com/trending/python'
page_response = requests.get(target_url)  # Initial fetch can be direct or via proxy
trending_repos_data = []

if page_response.status_code == 200:
    html_content = page_response.text
    soup_parser = BeautifulSoup(html_content, 'html.parser')
    repository_elements = soup_parser.find_all('article', class_='Box-row')

    for repo_element in repository_elements:
        try:
            name_element = repo_element.find('h2', class_='h3')
            full_name = name_element.a.text.strip().replace('\n', '').replace(' ', '')
            repo_name = full_name.split('/')[-1]
            star_link = repo_element.find('a', href=lambda href: href and '/stargazers' in href)
            star_count_text = star_link.text.strip() if star_link else 'N/A'
            link_path = name_element.a['href']
            repo_link = 'https://github.com' + link_path
            repo_info = {
                'name': repo_name,
                'stars': star_count_text,
                'link': repo_link
            }
            trending_repos_data.append(repo_info)
        except AttributeError:
            print("Skipping an element during initial scrape.")
            continue
else:
    print(f"Failed to retrieve trending page. Status code: {page_response.status_code}")
    exit()  # Exit if the initial scrape failed

# --- Configure Evomi Proxies ---
proxy_user = 'your_evomi_username'  # Replace with your details
proxy_pass = 'your_evomi_password'  # Replace with your details
proxy_host = 'rp.evomi.com'
proxy_port_http = 1000
proxy_port_https = 1001

evomi_proxies = {
   'http': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_http}',
   'https': f'http://{proxy_user}:{proxy_pass}@{proxy_host}:{proxy_port_https}',
}

# --- Crawl Individual Repo READMEs using Proxies ---
all_readme_data = {}  # Dictionary to store READMEs by repo name

print(f"\n--- Starting README Crawl for {len(trending_repos_data)} repositories ---")

for repo in trending_repos_data:
    repo_url = repo['link']
    repo_name = repo['name']
    try:
        print(f"Fetching README for: {repo_name} ({repo_url})")
        # Use proxies for the individual repo requests
        repo_page_response = requests.get(repo_url, proxies=evomi_proxies, timeout=20)  # Increased timeout slightly

        if repo_page_response.status_code == 200:
            repo_html = repo_page_response.text
            repo_soup = BeautifulSoup(repo_html, 'html.parser')
            readme_element = repo_soup.find('div', id='readme')

            if readme_element:
                readme_text = readme_element.get_text(separator='\n', strip=True)
                all_readme_data[repo_name] = readme_text
                print(f"Successfully fetched README for {repo_name}. Length: {len(readme_text)} chars.")
            else:
                print(f"README section not found for {repo_name}.")
                all_readme_data[repo_name] = None  # Indicate README wasn't found
        else:
            print(f"Failed to fetch {repo_url}. Status: {repo_page_response.status_code}")
            all_readme_data[repo_name] = f"Error: Status {repo_page_response.status_code}"

    except requests.exceptions.ProxyError as e:
        print(f"Proxy error for {repo_url}: {e}")
        all_readme_data[repo_name] = "Error: Proxy Issue"
    except requests.exceptions.Timeout:
        print(f"Request timed out for {repo_url}")
        all_readme_data[repo_name] = "Error: Timeout"
    except requests.exceptions.RequestException as e:
        print(f"Request error for {repo_url}: {e}")
        all_readme_data[repo_name] = f"Error: Request Failed ({e})"
    except Exception as e:
        print(f"An error occurred while processing {repo_name}: {e}")
        all_readme_data[repo_name] = f"Error: Parsing Failed ({e})"
        continue

print("\n--- README Crawl Complete ---")

# Now 'all_readme_data' dictionary contains the extracted READMEs (or errors)
# Example: Accessing a specific README
# print(all_readme_data.get('some-cool-project', 'Not Found'))

Important Considerations for GitHub Scraping

When scraping any website, especially one as widely used as GitHub, it's crucial to proceed responsibly and ethically. While GitHub's data is largely public, adhering to best practices ensures you don't disrupt the service and stay compliant.

Keep these points in mind:

  • Mind the Rate Limits: GitHub implements rate limits to prevent abuse. While proxies help distribute requests, excessively aggressive scraping can still lead to temporary blocks. Scrape at a reasonable pace, especially if not using a large, diverse proxy pool.

  • Cache Your Results: Avoid repeatedly scraping the same page if the data hasn't changed. Store (cache) the data you've already collected locally. This reduces load on GitHub's servers and speeds up your subsequent analyses.

  • Check the GitHub API First: Before committing to scraping, investigate if the official GitHub API provides the data you need. The API offers structured data access, is generally more stable than scraping HTML, and is the officially supported method for data retrieval. Scraping is better suited for data not readily available via the API or for specific exploratory analysis.

  • Review Terms of Service: Familiarize yourself with GitHub's Terms of Service regarding data access and automated retrieval. While scraping public data isn't explicitly forbidden for logged-out users, excessive scraping could be flagged.

Potential Use Cases for Scraped GitHub Data

The data gathered from scraping GitHub can fuel various insightful applications:

  1. Software Development Trend Analysis: Track the rise and fall of programming languages, frameworks, and libraries based on repository popularity, star counts, and activity.

  2. Academic and Market Research: Analyze code structures, development practices, collaboration patterns, or the evolution of open-source projects.

  3. Competitive Intelligence: Monitor competitors' public repositories to understand their technology choices, development velocity, and open-source strategies.

  4. User Feedback Aggregation: Systematically gather insights from issue trackers and pull request comments across multiple relevant repositories.

  5. Feature Request Identification: Spot emerging trends in user demands and feature requests within specific software ecosystems.

Conclusion

Scraping GitHub with Python, Requests, and Beautiful Soup opens up a rich source of data for understanding the software development landscape. From tracking trending projects to analyzing specific repository details like READMEs, the possibilities are vast.

While simple scraping is feasible for basic tasks, incorporating reliable, ethically sourced residential proxies—like those offered by Evomi—is crucial for scaling your efforts, improving speed, and avoiding IP blocks. By combining smart scraping techniques with robust proxy infrastructure, you can effectively gather valuable insights for research, analysis, or market intelligence.

Author

Nathan Reynolds

Web Scraping & Automation Specialist

About Author

Nathan specializes in web scraping techniques, automation tools, and data-driven decision-making. He helps businesses extract valuable insights from the web using ethical and efficient scraping methods powered by advanced proxies. His expertise covers overcoming anti-bot mechanisms, optimizing proxy rotation, and ensuring compliance with data privacy regulations.

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You asked, we answer - Users questions:
When should I choose web scraping over the official GitHub API for data collection?+
What if the specific GitHub data I need is loaded dynamically using JavaScript?+
Besides repository names and READMEs, what other specific data can be scraped from GitHub, and are there unique challenges?+
Can I use proxies if I need to scrape GitHub data that requires me to be logged in?+
The article recommends residential proxies. Are datacenter or mobile proxies ever suitable for scraping GitHub?+

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