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Contributing to dataframe

Thank you for your interest in contributing to hsky, a Haskell dataframe library built for speed, clarity, and expressive data analysis. Contributions are welcome and encouraged! This guide will walk you through how to get started.

Table of Contents

How to Contribute

You can contribute in many ways:

  • Filing bug reports
  • Suggesting new features or APIs
  • Improving documentation
  • Submitting code improvements or new functionality
  • Writing benchmarks or performance test cases
  • Helping triage issues or reviewing PRs

Getting Started

  1. Clone the repo:

    git clone https://github.com/mchav/dataframe.git
cd dataframe

  2. Install dependencies:

    We keep the library intentionally light on dependencies to make installation less painful. All you need to do is install Haskell via GHCup.

  3. Code standards

    • Prefer total functions and avoid partial ones unless necessary (with clear documentation).
    • Use strict folds when applicable to avoid space leaks.
    • Maintain performance discipline: avoid unnecessary allocations or intermediate structures.

  4. Testing We use HUnit and QuickCheck for unit and property-based tests. Add tests for new features under test/. If you're fixing a bug, add a test that fails without your fix.

Documentation

All public functions should have Haddock comments. If you're adding user-facing features, consider updating the README and examples/.

Filing Issues

Please include:

  • A short, clear title
  • A minimal reproducible example (if applicable)
  • What you expected to happen vs. what actually happened
  • Your environment (OS, GHC version, etc.)

Feature suggestions are welcome too! Bonus points for a motivating use case.

Submitting Pull Requests

  • Fork the repo.
  • Create a branch: git checkout -b feature/my-awesome-feature
  • Make your changes and commit with a clear message.
  • Push to your fork and open a PR.

Please include:

  • A description of the problem and solution.
  • Benchmarks or reasoning if the change impacts performance.
  • Tests and documentation where applicable.

All PRs should pass the CI and be reviewed before merging.

Community Expectations

We strive to maintain a respectful, inclusive, and collaborative environment. Be kind, curious, and constructive in your discussions.