Scrape open-source project activity

Scraping open-source project activity typically involves collecting data from platforms like GitHub, GitLab, or Bitbucket. This can include metrics like commit frequency, pull requests, issues, contributors, release timelines, and more. Below is a guide to scraping such data using GitHub as an example, since it’s the most popular platform.

Tools You Can Use

1. GitHub REST API v3

2. GitHub GraphQL API v4

3. Python Libraries:

   • requests

   • PyGithub

   • pandas (for data handling)

   • BeautifulSoup (if scraping HTML, not recommended due to API availability)

Steps to Scrape Open-Source Project Activity from GitHub

1. Get a GitHub Personal Access Token

• Navigate to: https://github.com/settings/tokens

• Generate a token with at least public_repo scope.

2. Use PyGithub (Python) to Access the API

python
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from github import Github
import pandas as pd

# Authentication
g = Github("your_github_token_here")

# Access a repository
repo = g.get_repo("tensorflow/tensorflow")

# Get commits
commits = repo.get_commits()
commit_data = [{"sha": c.sha, "author": c.commit.author.name, "date": c.commit.author.date} for c in commits[:100]]

# Get issues
issues = repo.get_issues(state='all')
issue_data = [{"title": i.title, "state": i.state, "created_at": i.created_at} for i in issues[:100]]

# Get pull requests
pulls = repo.get_pulls(state='all')
pull_data = [{"title": pr.title, "state": pr.state, "created_at": pr.created_at} for pr in pulls[:100]]

# Convert to DataFrames
df_commits = pd.DataFrame(commit_data)
df_issues = pd.DataFrame(issue_data)
df_pulls = pd.DataFrame(pull_data)

3. GitHub GraphQL for Efficient Queries

python
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import requests

headers = {"Authorization": "Bearer your_github_token_here"}
query = """
{
  repository(owner:"tensorflow", name:"tensorflow") {
    pullRequests(last: 10) {
      nodes {
        title
        state
        createdAt
      }
    }
  }
}
"""
response = requests.post('https://api.github.com/graphql', json={'query': query}, headers=headers)
data = response.json()

Data Points You Can Scrape

Storing and Visualizing Data

• Store results in CSV, JSON, or a SQLite database.

• Use tools like:

  • Matplotlib or Plotly for charts

  • Pandas Profiling for exploratory data analysis

  • Dash or Streamlit for web dashboards

Rate Limiting & Best Practices

• Rate Limits: GitHub API has limits:

  • Unauthenticated: 60 requests/hour

  • Authenticated: 5000 requests/hour

• Respect Terms: Avoid scraping HTML, use APIs.

• Pagination: Always handle paginated responses (API returns limited results per call).

• Cache Results: Store locally to avoid hitting rate limits unnecessarily.

Example Use Cases

• Monitor activity trends (e.g., most active weeks)

• Compare project vitality across multiple repos

• Track contributor churn

• Visualize open/closed issue ratios

• Analyze codebase changes by module or file

Let me know if you want a working script to scrape and plot activity trends across multiple GitHub repositories.