Sprints

PYDATA NYC IS HOSTING MULTIPLE SPRINTS FOR OPEN SOURCE PROJECTS. EACH SPRINT WILL HAVE A PROJECT MAINTAINER LEADING THE SPRINT TO HELP GUIDE THE CONTRIBUTORS THROUGHOUT THE SESSION.

Mini-Sprints

Contribute code or documentation to open source libraries! Project Maintainers and experienced contributors to open source will be on hand to help you choose a good first issue, create a pull request, and get your changes merged into the next candidate release.

Your Sprint Leads:

  • conda/conda-forge — Marius van Niekerk, conda-forge core developer
  • PyMC — Dr. Christian Luhmann, PyMC core developer
  • NumPy/SciPy — Ganesh Kathiresan, NumPy core developer & Juan Luis Cano Rodríguez, Prolific Open Source Contributor 
  • Matplotlib — Hannah Aizenman, matplotlib core developer

Free to attend. All levels are welcome. Pizza lunch will be provided. This event will not be recorded.

Room: Radio City (6th Floor)

Day: Tuesday, November 8

Time: 10am-2pm

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-1j6z4sown-3aT1Nee_LT8OWL3EuKlRWg
Sign up to our mailing listmail.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.

Matplotlib

Issues labelled Good first issue on the Matplotlib issue tracker are suitable for participants in today’s sprint.

If you do not see a suitable issue or you are interested in a particular domain (such as GUIAPIbackendDocumentation or any other), please look at the relevant label on GitHub or ask a mentor for a suggestion.

Guides

Matplotlib Contributing documentation

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

Get sprinting (aka fixing issues and submitting PRs)!

Development sprints offer an opportunity to enhance and contribute to open source projects in a focused session with the project maintainers. It is a fun exercise that helps open source projects to improve with the help of the open source community.

  • 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 :sweat:! If you search for an issue not labelled sprint or good first issue, 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 and good first issue 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.htmlhttps://numpy.org/devdocs/index.html or https://matplotlib.org/devdocs/. These are 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

There is no specific guidance for Matplotlib, but please try to write clear and concise commit messages that explain what you did.

Who can participate?

ALL experience levels are welcome to participate. Contribution guides and environment setup instructions are provided with each sprint.