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PyCon US'22 Mentored Sprints - NumPy & SciPy

The main purpose of this sprint is to help newcomers contribute to NumPy & SciPy.

To join Mentored Sprints Discord, follow the link: https://t.co/zTrfEb4Mzh.

NumPy

Helpful resources*

*Please help us improve these resources by submitting your suggestions in a corresponding issue tracker.

Choosing an issue

The issues tagged sprint are relatively self-contained and a fairly good starting point for learning your way through NumPy building and bug fixing.
Click here to see the list of all the issues.

Let's connect and keep the conversation going!

Join our Slack workspace: https://join.slack.com/t/numpy-team/shared_invite/zt-e2d24txg-w3Mq1OJZ2nEAAcGgQOoC0A
Sign up to our mailing list: mail.python.org/mailman/listinfo/numpy-discussion
Follow us on Twitter: @numpy_team
Subscribe to our YouTube channel: https://www.youtube.com/c/NumPy_team

SciPy

Issues labelled sprint on the SciPy issue tracker is a mix of code (mostly Python-level) and documentation issues selected for today's sprint.

If you do not see a suitable issue or you are interested in a particular SciPy submodule, please look at the relevant submodule label or ask a mentor for a suggestion.

Guides

SciPy Contributing documentation

The following documentation will help you set up and build a local development environment:

Choosing an issue

The issues tagged sprint are relatively self-contained and a fairly good starting point for learning your way through SciPy building and bug fixing.
Click here to see the list of all the issues.

Get sprinting (aka fixing issues and submitting PRs)!

  • Please comment on the issue that you are working on to avoid multiple people taking on the same issue.

  • There are a lot of issues

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    ! If you search for an issue not labelled sprint, it is recommended to pick one that is relatively new and doesn't have too many comments on it. (Old issues are often difficult to solve or unresolvable which is why they still remain.)

  • The selections labelled sprint are focused on beginner-to-intermediate-friendly issues that are actionable and should be doable today. If you want to take on something a little more challenging, there are a lot of issues in the main repos to choose from. Try to choose issues that (from the title or labels) seem to be bugs, and don't have more than 5 comments - those are most likely to be actionable.

Documentation issues

If you are working on documentation, remember to check against https://scipy.github.io/devdocs/index.html or https://numpy.org/devdocs/index.html. This is the version of the documentation corresponding to the latest development version (aka what is merged on the main branch on GitHub).

How to co-author commits

If you worked in a pair/group and would like to acknowledge multiple authors on the PR, find some helpful documentation here.

Labelling commit messages

Commit messages should be clear and follow a few basic rules. Please try to label your commits with an indicative label. Here are the guides for

Select a repo