Hey thanks for deciding to contribute!
The following is a set of guidelines for contributing to the imputations library, impyute, which is hosted here
- Check for open issues or create a new one to discuss new features or bugs.
- Fork the repo on github and make the changes. Make sure you follow the guidelines below.
- Write a unit test to show that the bug was fixed/feature works
- Submit your pull request and reference the issue (and add yourself to AUTHORS!)
To run unit tests, just run pytest from root. To test all other supported Python versions you will need Docker. The unit testing framework used is the built-in one, unittest. Put unit tests in the test directory in root. The testing environment works like this: 1) Build a docker image with multiple python versions 2) Run the container with pytest for each python version.
$ make test
Using Sphinx's autodoc module, docstrings are used as the documentation. Make sure that all docstrings are formatted according to the NumPy/SciPy Docstring Standard
Use .pylintrc to lint files in accordance (mostly) with PEP8. You will first need pylint installed. I recommend integrating it with your editor or you can call it from bash with:
$ pylint --rcfile=.pylintrc impyute/
$ pylint --rcfile=.pylintrc test/
Fix all warnings raised, if you feel that the warning isn't justified/serves no purpose, then feel free to disable the specific message for whatever blocks are causing it.
This project was created to cover everything required in the step of your pipeline where you move from data with missing values to data without missing values. Simple enough right? Any enhancements that brings value with that in mind are welcome.
This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code.