Contributing#

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions#

Report Bugs#

Report bugs here: https://github.com/ContextLab/data-wrangler/issues

If you are reporting a bug, please include:

  • Your operating system name and version.

  • Any details about your local setup that might be helpful in troubleshooting.

  • Detailed steps to reproduce the bug.

Fix Bugs#

Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.

Implement Features#

Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.

Write Documentation#

datawrangler could always use more documentation, whether as part of the official datawrangler docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback#

The best way to send feedback is to file an issue here: https://github.com/ContextLab/data-wrangler/issues

If you are proposing a feature:

  • Explain in detail how it would work.

  • Keep the scope as narrow as possible, to make it easier to implement.

  • Remember that this is a volunteer-driven project, and that contributions are welcome!

Get Started!#

Ready to contribute? Here’s how to set up datawrangler for local development.

  1. Fork the data-wrangler repo on GitHub: https://github.com/ContextLab/data-wrangler

  2. Clone your fork locally:

    $ git clone git@github.com:your_name_here/data-wrangler.git

  3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

    $ mkvirtualenv data-wrangler
$ cd data-wrangler/
$ python setup.py develop

  4. Create a branch for local development:

    $ git checkout -b name-of-your-bugfix-or-feature

    Now you can make your changes locally.

  5. When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:

    $ flake8 datawrangler tests
$ python setup.py test or pytest
$ tox

    To get flake8 and tox, just pip install them into your virtualenv.

  6. Commit your changes and push your branch to GitHub:

    $ git add .
$ git commit -m "Your detailed description of your changes."
$ git push origin name-of-your-bugfix-or-feature

  7. Submit a pull request through the GitHub website.

Pull Request Guidelines#

Before you submit a pull request, check that it meets these guidelines:

  1. The pull request should include tests.

  2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.

  3. The pull request should work for Python 3.6, 3.7, 3.8, 3.9, and for PyPy. Check https://github.com/ContextLab/data-wrangler/pulls and make sure that the tests pass for all supported Python versions.

Tips#

To run a subset of tests:

$ pytest tests.test_datawrangler

Deploying#

A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.rst). Then run:

$ bump2version patch # possible: major / minor / patch
$ git push
$ git push --tags

Travis will then deploy to PyPI if tests pass.