Contributing

Thank you for your interest in contributing to edaflow! This project aims to provide comprehensive, educational EDA tools for data scientists and analysts.

🛠️ Development Setup

1. Fork the Repository:

   git clone https://github.com/YOUR_USERNAME/edaflow.git
   cd edaflow
   

2. Create Virtual Environment:

   python -m venv edaflow_env
   source edaflow_env/bin/activate  # On Windows: edaflow_env\Scripts\activate
   

3. Install in Development Mode:

   pip install -e .
   pip install -r requirements.txt
   

4. Install Development Dependencies:

   pip install pytest pytest-cov
   

🧪 Testing

Run the test suite before submitting contributions:

pytest tests/

Run tests with coverage:

pytest --cov=edaflow tests/

🎯 Contribution Areas

New EDA Functions

• Statistical analysis functions

• Visualization enhancements

• Data quality assessment tools

• Advanced pattern detection

Documentation Improvements

• Function docstrings

• Tutorial examples

• Best practices guides

• Educational content

Code Quality

• Performance optimizations

• Error handling improvements

• Type hints

• Code refactoring

Testing

• Unit test coverage

• Edge case testing

• Integration tests

• Performance benchmarks

📝 Coding Standards

Function Design

• Follow the established naming pattern: verb_object()

• Include comprehensive docstrings with parameters and examples

• Provide educational context in function descriptions

• Return both processed data and visualizations where appropriate

Code Style

• Follow PEP 8 Python style guidelines

• Use descriptive variable names

• Include inline comments for complex logic

• Maintain consistency with existing code patterns

Documentation

• Write clear, educational docstrings

• Include practical examples in docstrings

• Update README.md for new features

• Add changelog entries for all changes

🔄 Pull Request Process

1. Create Feature Branch:

   git checkout -b feature/your-feature-name
   

2. Make Your Changes * Write clean, well-documented code * Add appropriate tests * Update documentation

3. Test Your Changes:

   pytest tests/
   # Ensure all tests pass
   

4. Commit Changes:

   git add .
   git commit -m "Add: Brief description of your changes"
   

5. Push Branch:

   git push origin feature/your-feature-name
   

6. Submit Pull Request * Provide clear description of changes * Reference any related issues * Include screenshots for visualizations

📋 Pull Request Checklist

• [ ] Code follows project style guidelines

• [ ] Tests pass locally

• [ ] New tests added for new functionality

• [ ] Documentation updated

• [ ] Changelog entry added

• [ ] Function includes educational docstring

• [ ] No breaking changes (or clearly documented)

🐛 Bug Reports

When reporting bugs, please include:

Environment Information

• Python version

• edaflow version

• Operating system

• Package versions (pandas, matplotlib, etc.)

Bug Description

• Clear description of the issue

• Steps to reproduce

• Expected vs actual behavior

• Error messages/tracebacks

Example Code:

# Minimal code example that reproduces the bug
import edaflow
import pandas as pd

# Your code here...

💡 Feature Requests

We welcome feature requests! Please include:

• Use Case: Describe the EDA scenario this would help with

• Proposed Solution: How you envision the feature working

• Educational Value: How this would help users learn EDA concepts

• Examples: Provide examples of when this would be useful

📚 Documentation Contributions

Documentation improvements are highly valued:

• API Documentation: Enhance function docstrings

• Tutorials: Create educational examples

• Best Practices: Share EDA insights and techniques

• README Updates: Improve project description and examples

🎓 Educational Focus

edaflow has an educational mission. When contributing:

• Explain the Why: Help users understand EDA concepts

• Provide Context: Explain when and why to use functions

• Include Examples: Show practical applications

• Reference Theory: Link to statistical concepts when relevant

🤝 Code of Conduct

This project follows a simple code of conduct:

• Be Respectful: Treat all contributors with respect

• Be Collaborative: Work together to improve the project

• Be Educational: Help others learn and grow

• Be Patient: Remember that contributors have different skill levels

🆘 Getting Help

Need help with your contribution?

• GitHub Issues: Ask questions or discuss ideas

• Documentation: Check existing docs and examples

• Code Review: Maintainers will provide feedback on pull requests

📧 Contact

• GitHub Issues: Primary communication channel

• Email: For private inquiries (check GitHub profile)

• Discussions: Use GitHub Discussions for general questions

Thank you for helping make edaflow better for the data science community! 🚀

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Last updated: January 2024