Sprints<\/h3>
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.<\/p><\/div>\n <\/div>[\/vc_column_inner][vc_column_inner width=”1\/4″][\/vc_column_inner][\/vc_row_inner][vc_row_inner el_id=”Talks”][vc_column_inner width=”1\/12″][\/vc_column_inner][vc_column_inner width=”10\/12″][vc_column_text]<\/p>\n
Mini-Sprints<\/strong><\/span><\/h3>\nContribute 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.<\/span><\/p>\nYour Sprint Leads:<\/span><\/p>\n\n- conda\/conda-forge \u2014 Marius van Niekerk, conda-forge core developer<\/span><\/li>\n
- PyMC \u2014 Dr. <\/span>Christian Luhmann, PyMC core developer<\/span><\/li>\n
- NumPy\/SciPy \u2014 Ganesh Kathiresan, NumPy core developer & Juan Luis Cano Rodr\u00edguez, Prolific Open Source Contributor\u00a0<\/span><\/li>\n
- Matplotlib <\/span>\u2014 Hannah Aizenman, matplotlib core developer<\/span><\/li>\n<\/ul>\n
[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner width=”1\/5″][vc_column_text]![\"\"](\"https:\/\/pydata.org\/nyc2022\/wp-content\/uploads\/2022\/10\/scipy_logo300x300-150x150.png\")
[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/5″][vc_column_text]![\"\"](\"https:\/\/pydata.org\/nyc2022\/wp-content\/uploads\/2022\/10\/condaforge-150x150.png\")
[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/5″][vc_column_text]![\"\"](\"https:\/\/pydata.org\/nyc2022\/wp-content\/uploads\/2022\/10\/PyMC300x300-150x150.png\")
[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/5″][vc_column_text]![\"\"](\"https:\/\/pydata.org\/nyc2022\/wp-content\/uploads\/2022\/10\/NumPy-150x150.png\")
[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/5″][vc_column_text]![\"\"](\"https:\/\/pydata.org\/nyc2022\/wp-content\/uploads\/2022\/10\/844075_c6531fd41fea5eea6cdfde8f9564c1d3-150x150.png\")
[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][vc_column_inner width=”10\/12″][vc_column_text]Free to attend. All levels are welcome. Pizza lunch will be provided. This event will not be recorded.<\/span><\/p>\nRoom<\/b>: Radio City (6th Floor)<\/span><\/h3>\nDay<\/b>: Tuesday, November 8<\/span><\/h3>\nTime<\/b>: 10am-2pm<\/span><\/h3>\n[\/vc_column_text][vc_separator color=”turquoise”][\/vc_column_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][vc_column_inner width=”10\/12″][vc_column_text]<\/p>\n
NumPy<\/span><\/h1>\nHelpful resources*<\/span><\/h3>\n\n- Contributing to NumPy<\/span><\/a><\/li>\n
- Using gitpod for NumPy development<\/span><\/a><\/li>\n
- Building from source<\/span><\/a><\/li>\n
- Building the documentation<\/span><\/a><\/li>\n<\/ul>\n
*Please help us improve these resources by submitting your suggestions in a corresponding issue tracker.<\/span><\/p>\nChoosing an issue<\/span><\/h3>\nThe issues tagged\u00a0<\/span>sprint<\/code>\u00a0are relatively self-contained and a fairly good starting point for learning your way through NumPy building and bug fixing.<\/span>
\nClick here to see the list of all the issues.<\/span><\/a><\/p>\nLet\u2019s connect and keep the conversation going!<\/span><\/h3>\nJoin our\u00a0<\/span>Slack<\/span><\/strong> workspace: https:\/\/join.slack.com\/t\/numpy-team\/shared_invite\/zt-1j6z4sown-3aT1Nee_LT8OWL3EuKlRWg<\/a><\/span>
\nSign up to our\u00a0<\/span>mailing list<\/span><\/strong>:\u00a0<\/span>mail.python.org\/mailman\/listinfo\/numpy-discussion<\/span><\/a>
\nFollow us on\u00a0<\/span>Twitter<\/span><\/strong>:\u00a0<\/span>@numpy_team<\/span><\/a>
\nSubscribe to our\u00a0<\/span>YouTube<\/span><\/strong>\u00a0channel:\u00a0<\/span>https:\/\/www.youtube.com\/c\/NumPy_team<\/span><\/a><\/p>\n[\/vc_column_text][vc_separator color=”turquoise”][vc_column_text]<\/p>\n
SciPy<\/span><\/h1>\nIssues labelled\u00a0<\/span>sprint<\/code>\u00a0on the SciPy issue tracker<\/span><\/a><\/strong>\u00a0is a mix of code (mostly Python-level) and documentation issues selected for today\u2019s sprint.<\/span><\/p>\nIf 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.<\/span><\/p>\nGuides<\/span><\/h3>\nSciPy Contributing documentation<\/span><\/a><\/h4>\nThe following documentation will help you set up and build a local development environment:<\/span><\/p>\n\n- Fork and configuration of SciPy repository<\/span><\/a><\/li>\n
- Contributor quickstart guide<\/span><\/a><\/li>\n
- Complete build instructions<\/span><\/a><\/li>\n
- As an aternative, use the SciPy Gitpod setup\u00a0<\/span>(this works without having to install anything, get to coding straight away in the browser!)<\/span><\/em>. Find more information in\u00a0<\/span>the SciPy Gitpod docs<\/span><\/a>.<\/span><\/li>\n
- General guidelines for contributing:<\/span>\n
\n- Read through the recommended\u00a0<\/span>development workflow<\/span><\/a>.<\/span><\/li>\n
- Read through how-to\u00a0<\/span>guide to contributing to SciPy documentation<\/span><\/a>\u00a0(same as NumPy).<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
Choosing an issue<\/span><\/h3>\nThe issues tagged\u00a0<\/span>sprint<\/code>\u00a0are relatively self-contained and a fairly good starting point for learning your way through SciPy building and bug fixing.<\/span>
\nClick here to see the list of all the issues.<\/span><\/a><\/p>\n[\/vc_column_text][vc_separator color=”turquoise”][vc_column_text]<\/p>\n
Matplotlib<\/span><\/h1>\nIssues labelled\u00a0<\/span>Good first issue<\/code>\u00a0on the Matplotlib issue tracker<\/span><\/a><\/strong>\u00a0are suitable for participants in today\u2019s sprint.<\/span><\/p>\nIf you do not see a suitable issue or you are interested in a particular domain (such as\u00a0<\/span>GUI<\/code>,\u00a0<\/span>API<\/code>,\u00a0<\/span>backend<\/code>,\u00a0<\/span>Documentation<\/code>\u00a0or any other), please look at the relevant label on GitHub or ask a mentor for a suggestion.<\/span><\/p>\nGuides<\/span><\/h3>\nMatplotlib Contributing documentation<\/span><\/a><\/h4>\nThe following documentation will help you set up and build a local development environment:<\/span><\/p>\n\n- Setting up Matplotlib for development<\/span><\/a><\/li>\n
- Pull request guidelines<\/span><\/a><\/li>\n
- Coding guidelines<\/span><\/a><\/li>\n
- Testing<\/span><\/a><\/li>\n
- Writing documentation<\/span><\/a><\/li>\n<\/ul>\n
[\/vc_column_text][vc_separator color=”turquoise”][vc_column_text]<\/p>\n
Get sprinting (aka fixing issues and submitting PRs)!<\/span><\/h2>\nDevelopment 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.<\/p>\n
\n- \n
Please comment on the issue that you are working on to avoid multiple people taking on the same issue.<\/span><\/p>\n<\/li>\n- \n
There are\u00a0<\/span>a lot of<\/span><\/em>\u00a0issues\u00a0<\/span>![\":sweat:\"](\"https:\/\/assets.hackmd.io\/build\/emojify.js\/dist\/images\/basic\/sweat.png\")
! If you search for an issue not labelled\u00a0<\/span>sprint<\/code>\u00a0or\u00a0<\/span>good first issue<\/code>, it is recommended to pick one that is relatively new and doesn\u2019t have too many comments on it. (Old issues are often difficult to solve or unresolvable which is why they still remain.)<\/span><\/p>\n<\/li>\n- \n
The selections labelled\u00a0<\/span>sprint<\/code>\u00a0and\u00a0<\/span>good first issue<\/code>\u00a0are 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\u2019t have more than 5 comments – those are most likely to be actionable.<\/span><\/p>\n<\/li>\n<\/ul>\nDocumentation issues<\/span><\/h4>\nIf you are working on documentation, remember to check against\u00a0<\/span>https:\/\/scipy.github.io\/devdocs\/index.html<\/span><\/a>,\u00a0<\/span>https:\/\/numpy.org\/devdocs\/index.html<\/span><\/a>\u00a0or\u00a0<\/span>https:\/\/matplotlib.org\/devdocs\/<\/span><\/a>. These are the version of the documentation corresponding to the latest development version (aka what is merged on the main branch on GitHub).<\/span><\/p>\nHow to co-author commits<\/span><\/h4>\nIf you worked in a pair\/group and would like to acknowledge multiple authors on the PR, find some helpful documentation\u00a0<\/span>here.<\/span><\/a><\/p>\nLabelling commit messages<\/span><\/h4>\nCommit 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<\/span><\/p>\n\n- NumPy<\/span><\/a><\/li>\n
- SciPy<\/span><\/a><\/li>\n<\/ul>\n
There is no specific guidance for Matplotlib, but please try to write clear and concise commit messages that explain what you did.<\/span><\/p>\nRecommended resources about contributing to open source<\/span><\/h4>\n\n- Talk\u00a0<\/span>Sphinx for Python documentation<\/span><\/em>\u00a0by Melissa Weber Mendon\u00e7a:\u00a0<\/span>https:\/\/youtu.be\/tXWscUSYdBs<\/span><\/a><\/li>\n
- Talk\u00a0<\/span>Intro to git<\/span><\/em>\u00a0by Reshama Shaikh:\u00a0<\/span>
Your Sprint Leads:<\/span><\/p>\n [\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner width=”1\/5″][vc_column_text] [\/vc_column_text][vc_separator color=”turquoise”][\/vc_column_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][vc_column_inner width=”10\/12″][vc_column_text]<\/p>\n *Please help us improve these resources by submitting your suggestions in a corresponding issue tracker.<\/span><\/p>\n The issues tagged\u00a0<\/span> Join our\u00a0<\/span>Slack<\/span><\/strong> workspace: https:\/\/join.slack.com\/t\/numpy-team\/shared_invite\/zt-1j6z4sown-3aT1Nee_LT8OWL3EuKlRWg<\/a><\/span> [\/vc_column_text][vc_separator color=”turquoise”][vc_column_text]<\/p>\n Issues labelled\u00a0<\/span> 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.<\/span><\/p>\n The following documentation will help you set up and build a local development environment:<\/span><\/p>\n The issues tagged\u00a0<\/span> [\/vc_column_text][vc_separator color=”turquoise”][vc_column_text]<\/p>\n Issues labelled\u00a0<\/span> If you do not see a suitable issue or you are interested in a particular domain (such as\u00a0<\/span> The following documentation will help you set up and build a local development environment:<\/span><\/p>\n [\/vc_column_text][vc_separator color=”turquoise”][vc_column_text]<\/p>\n 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.<\/p>\n Please comment on the issue that you are working on to avoid multiple people taking on the same issue.<\/span><\/p>\n<\/li>\n There are\u00a0<\/span>a lot of<\/span><\/em>\u00a0issues\u00a0<\/span> The selections labelled\u00a0<\/span> If you are working on documentation, remember to check against\u00a0<\/span>https:\/\/scipy.github.io\/devdocs\/index.html<\/span><\/a>,\u00a0<\/span>https:\/\/numpy.org\/devdocs\/index.html<\/span><\/a>\u00a0or\u00a0<\/span>https:\/\/matplotlib.org\/devdocs\/<\/span><\/a>. These are the version of the documentation corresponding to the latest development version (aka what is merged on the main branch on GitHub).<\/span><\/p>\n If you worked in a pair\/group and would like to acknowledge multiple authors on the PR, find some helpful documentation\u00a0<\/span>here.<\/span><\/a><\/p>\n 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<\/span><\/p>\n There is no specific guidance for Matplotlib, but please try to write clear and concise commit messages that explain what you did.<\/span><\/p>\n\n
[\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/5″][vc_column_text][\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/5″][vc_column_text][\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/5″][vc_column_text][\/vc_column_text][\/vc_column_inner][vc_column_inner width=”1\/5″][vc_column_text][\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][vc_column_inner width=”10\/12″][vc_column_text]Free to attend. All levels are welcome. Pizza lunch will be provided. This event will not be recorded.<\/span><\/p>\nRoom<\/b>: Radio City (6th Floor)<\/span><\/h3>\n
Day<\/b>: Tuesday, November 8<\/span><\/h3>\n
Time<\/b>: 10am-2pm<\/span><\/h3>\n
NumPy<\/span><\/h1>\n
Helpful resources*<\/span><\/h3>\n
\n
Choosing an issue<\/span><\/h3>\n
sprint<\/code>\u00a0are relatively self-contained and a fairly good starting point for learning your way through NumPy building and bug fixing.<\/span>
\nClick here to see the list of all the issues.<\/span><\/a><\/p>\nLet\u2019s connect and keep the conversation going!<\/span><\/h3>\n
\nSign up to our\u00a0<\/span>mailing list<\/span><\/strong>:\u00a0<\/span>mail.python.org\/mailman\/listinfo\/numpy-discussion<\/span><\/a>
\nFollow us on\u00a0<\/span>Twitter<\/span><\/strong>:\u00a0<\/span>@numpy_team<\/span><\/a>
\nSubscribe to our\u00a0<\/span>YouTube<\/span><\/strong>\u00a0channel:\u00a0<\/span>https:\/\/www.youtube.com\/c\/NumPy_team<\/span><\/a><\/p>\nSciPy<\/span><\/h1>\n
sprint<\/code>\u00a0on the SciPy issue tracker<\/span><\/a><\/strong>\u00a0is a mix of code (mostly Python-level) and documentation issues selected for today\u2019s sprint.<\/span><\/p>\nGuides<\/span><\/h3>\n
SciPy Contributing documentation<\/span><\/a><\/h4>\n
\n
\n
Choosing an issue<\/span><\/h3>\n
sprint<\/code>\u00a0are relatively self-contained and a fairly good starting point for learning your way through SciPy building and bug fixing.<\/span>
\nClick here to see the list of all the issues.<\/span><\/a><\/p>\nMatplotlib<\/span><\/h1>\n
Good first issue<\/code>\u00a0on the Matplotlib issue tracker<\/span><\/a><\/strong>\u00a0are suitable for participants in today\u2019s sprint.<\/span><\/p>\nGUI<\/code>,\u00a0<\/span>API<\/code>,\u00a0<\/span>backend<\/code>,\u00a0<\/span>Documentation<\/code>\u00a0or any other), please look at the relevant label on GitHub or ask a mentor for a suggestion.<\/span><\/p>\nGuides<\/span><\/h3>\n
Matplotlib Contributing documentation<\/span><\/a><\/h4>\n
\n
Get sprinting (aka fixing issues and submitting PRs)!<\/span><\/h2>\n
\n
! If you search for an issue not labelled\u00a0<\/span>sprint<\/code>\u00a0or\u00a0<\/span>good first issue<\/code>, it is recommended to pick one that is relatively new and doesn\u2019t have too many comments on it. (Old issues are often difficult to solve or unresolvable which is why they still remain.)<\/span><\/p>\n<\/li>\nsprint<\/code>\u00a0and\u00a0<\/span>good first issue<\/code>\u00a0are 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\u2019t have more than 5 comments – those are most likely to be actionable.<\/span><\/p>\n<\/li>\n<\/ul>\nDocumentation issues<\/span><\/h4>\n
How to co-author commits<\/span><\/h4>\n
Labelling commit messages<\/span><\/h4>\n
\n
Recommended resources about contributing to open source<\/span><\/h4>\n
\n