github-data-extractor 0.0.11

A package to extract GitHub repository insights

Introduction

A Python package to extract GitHub repository insights including commit history, pull request analysis, contributor trends, and overall repository health. Designed to simplify engineering reporting and performance tracking.

Requirements

• Python 3.5 or later
• Google Maps API Key

Installation

pip install github-data-extractor

Usage and Documentation

This example shows how to use the geocentroid package.

from github_data_extractor import dataExtraction
from dotenv import load_dotenv
import os

load_dotenv()
GITHUB_TOKEN = os.getenv('GITHUB_TOKEN')

def main():
    repo_name = ['translate_lib']
    repo_owners = ['aadityayadav']
    repo_tokens = [GITHUB_TOKEN]

    extraction = dataExtraction(repo_name, repo_owners, repo_tokens)

    # method 1    
    extraction.extract_general_overview()
    # method 2
    extraction.extract_aggregate_metrics()
    # method 3
    extraction.extract_data_commit_contributor()
    # method 4
    extraction.extract_data_pr()

if __name__ == "__main__":
    main()

All functions take no parameters directly.
You must provide repo_name, repo_owners, and repo_tokens as lists, so you can extract data from multiple repositories at once.

1) extract_general_overview()

Fetches a high-level snapshot of the repository:

• Branch information (total branches, last updated)
• Linked vs unlinked issues
• File data associated with each pull request

2) extract_aggregate_metrics()

Provides an overview of project health using aggregated statistics:

• Commit activity over time
• File modification frequency
• Pull request volume and lifecycle
• Pull request quality: reviews, size, and merge times

3) extract_data_commit_contributor()

Gathers contributor and commit behavior:

• Commit counts by contributor
• Time-based commit activity
• New vs returning contributor patterns

4) extract_data_pr()

Detailed pull request analytics:

• PR open/merge/close timestamps
• Review histories and discussions
• Issue linkages, milestone tagging, and contributor-level PR trends

Returns:

• Automatically saves a CSV under a folder ExtractedData containing repo metrics.

Building the Package and Installing Locally Clone the repository then build the packages using:

pip3 install wheel
python3 setup.py bdist_wheel sdist
pip3 install .