pypackaging-native is a collection of content about key Python packaging topics and issues for projects using native code - with a focus in particular scientific, data science and ML/AI projects in the PyData ecosystem.
The purpose of the content in this repository and the website built from it is to explain what these key issues are, why they're important, and (briefly) potential solution directions. This in order to get others - in particular maintainers of Python packaging tools and others heavily involved in Python packaging - on the same page when we are discussing solutions. These topics are complex enough that in the past, discussions have often gone around in circles because people weren't on the same page. Our hope is that this content will serve to avoid that problem, so we can either make headway with solving these problems or decide that a problem is too complex to solve or potential solutions are blocked by other use cases and needs.
All contributions are very welcome and appreciated! Ways to contribute include:
- Improving existing content on the website: extending or clarifying descriptions, adding relevant references, diagrams, etc.
- Providing feedback on existing content
- Proposing new topics for inclusion on the website, and writing the content for them
- ... and anything else you consider useful!
Everyone interacting in the pypackaging-native project's codebases, issue trackers, and communication channels is expected to adhere to the NumFOCUS Code of Conduct.