Contributing guide#

Template

Template for further usage, template belong to matplotlib.

You’ve discovered a bug or something else you want to change in scikit-plots — excellent!

You’ve worked out a way to fix it — even better!

You want to tell us about it — best of all!

Below, you can find a number of ways to contribute, and how to connect with the scikit-plots community.

Ways to contribute#

Do I really have something to contribute to scikit-plots?#

100% yes! There are so many ways to contribute to our community. Take a look at the following sections to learn more.

There are a few typical new contributor profiles:

You are a scikit-plots user, and you see a bug, a potential improvement, or something that annoys you, and you can fix it.

You can search our issue tracker for an existing issue that describes your problem or open a new issue to inform us of the problem you observed and discuss the best approach to fix it. If your contributions would not be captured on GitHub (social media, communication, educational content), you can also reach out to us on gitter, Discourse or attend any of our community meetings.
You are not a regular scikit-plots user but a domain expert: you know about visualization, 3D plotting, design, technical writing, statistics, or some other field where scikit-plots could be improved.

Awesome — you have a focus on a specific application and domain and can start there. In this case, maintainers can help you figure out the best implementation; open an issue or pull request with a starting point, and we’ll be happy to discuss technical approaches.

If you prefer, you can use the GitHub functionality for “draft” pull requests and request early feedback on whatever you are working on, but you should be aware that maintainers may not review your contribution unless it has the “Ready to review” state on GitHub.
You are new to scikit-plots, both as a user and contributor, and want to start contributing but have yet to develop a particular interest.

Having some previous experience or relationship with the library can be very helpful when making open-source contributions. It helps you understand why things are the way they are and how they should be. Having first-hand experience and context is valuable both for what you can bring to the conversation (and given the breadth of scikit-plots’s usage, there is a good chance it is a unique context in any given conversation) and make it easier to understand where other people are coming from.

Understanding the entire codebase is a long-term project, and nobody expects you to do this right away. If you are determined to get started with scikit-plots and want to learn, going through the basic functionality, choosing something to focus on (3d, testing, documentation, animations, etc.) and gaining context on this area by reading the issues and pull requests touching these subjects is a reasonable approach.

Code#

You want to implement a feature or fix a bug or help with maintenance - much appreciated! Our library source code is found in:

Python library code: lib/
C-extension code: src/
Tests: lib/matplotlib/tests/

Because many people use and work on Matplotlib, we have guidelines for keeping our code consistent and mitigating the impact of changes.

Code is contributed through pull requests, so we recommend that you start at Start a pull request If you get stuck, please reach out on the Contributor incubator

Documentation#

You, as an end-user of Matplotlib can make a valuable contribution because you can more clearly see the potential for improvement than a core developer. For example, you can:

Fix a typo
Clarify a docstring
Write or update an example plot
Write or update a comprehensive tutorial

Our code is documented inline in the source code files in matplotlib/lib. Our website structure mirrors our folder structure, meaning that a narrative document’s URL roughly corresponds to its location in our folder structure:

using the library

galleries/plot_types/
users/getting_started/
galleries/user_explain/
galleries/tutorials/
galleries/examples/
doc/api/

information about the library

doc/install/
doc/project/
doc/devel/
doc/users/resources/index.rst
doc/users/faq.rst

Other documentation is generated from the following external sources:

matplotlib.org homepage: matplotlib/mpl-brochure-site
cheat sheets: matplotlib/cheatsheets
third party packages: matplotlib/mpl-third-party

Instructions and guidelines for contributing documentation are found in:

Documentation is contributed through pull requests, so we recommend that you start at Start a pull request. If that feels intimidating, we encourage you to open an issue describing what improvements you would make. If you get stuck, please reach out on the Contributor incubator

Triage#

We appreciate your help keeping the issue tracker organized because it is our centralized location for feature requests, bug reports, tracking major projects, and discussing priorities. Some examples of what we mean by triage are:

labeling issues and pull requests
verifying bug reports
debugging and resolving issues
linking to related issues, discussion, and external work

Our triage process is discussed in detail in Bug triaging and issue curation.

If you have any questions about the process, please reach out on the Contributor incubator

Community#

Matplotlib’s community is built by its members, if you would like to help out see our Community management guide.

It helps us if you spread the word: reference the project from your blog and articles or link to it from your website!

If Matplotlib contributes to a project that leads to a scientific publication, please cite us following the Citing scikit-plots guidelines.

If you have developed an extension to Matplotlib, please consider adding it to our third party package list.

Restrictions on Generative AI Usage#

We expect authentic engagement in our community. Be wary of posting output from Large Language Models or similar generative AI as comments on GitHub or our discourse server, as such comments tend to be formulaic and low content. If you use generative AI tools as an aid in developing code or documentation changes, ensure that you fully understand the proposed changes and can explain why they are the correct approach and an improvement to the current state.

New contributors#

Everyone comes to the project from a different place — in terms of experience and interest — so there is no one-size-fits-all path to getting involved. We recommend looking at existing issue or pull request discussions, and following the conversations during pull request reviews to get context. Or you can deep-dive into a subset of the code-base to understand what is going on.

New contributors meeting#

Once a month, we host a meeting to discuss topics that interest new contributors. Anyone can attend, present, or sit in and listen to the call. Among our attendees are fellow new contributors, as well as maintainers, and veteran contributors, who are keen to support onboarding of new folks and share their experience. You can find our community calendar link at the Scientific Python website, and you can browse previous meeting notes on GitHub. We recommend joining the meeting to clarify any doubts, or lingering questions you might have, and to get to know a few of the people behind the GitHub handles 😉. You can reach out to us on gitter for any clarifications or suggestions. We ❤ feedback!

Contributor incubator#

The incubator is our non-public communication channel for new contributors. It is a private gitter (chat) room moderated by core Matplotlib developers where you can get guidance and support for your first few PRs. It’s a place where you can ask questions about anything: how to use git, GitHub, how our PR review process works, technical questions about the code, what makes for good documentation or a blog post, how to get involved in community work, or get a “pre-review” on your PR.

To join, please go to our public community channel, and ask to be added to #incubator. One of our core developers will see your message and will add you.

Good first issues#

While any contributions are welcome, we have marked some issues as particularly suited for new contributors by the label good first issue. These are well documented issues, that do not require a deep understanding of the internals of Matplotlib. The issues may additionally be tagged with a difficulty. Difficulty: Easy is suited for people with little Python experience. Difficulty: Medium and Difficulty: Hard require more programming experience. This could be for a variety of reasons, among them, though not necessarily all at the same time:

The issue is in areas of the code base which have more interdependencies, or legacy code.
It has less clearly defined tasks, which require some independent exploration, making suggestions, or follow-up discussions to clarify a good path to resolve the issue.
It involves Python features such as decorators and context managers, which have subtleties due to our implementation decisions.

First contributions#

If this is your first open source contribution, or your first time contributing to Matplotlib, and you need help or guidance finding a good first issue, look no further. This section will guide you through each step:

Navigate to the issues page.
Filter labels with “Difficulty: Easy” & “Good first Issue” (optional).
Click on an issue you would like to work on, and check to see if the issue has a pull request opened to resolve it.
- A good way to judge if you chose a suitable issue is by asking yourself, “Can I independently submit a PR in 1-2 weeks?”
Check existing pull requests (e.g., PR #28476) and filter by the issue number to make sure the issue is not in progress:
- If the issue has a pull request (is in progress), tag the user working on the issue, and ask to collaborate (optional).
- If a pull request does not exist, create a draft pull request and follow the pull request guidelines.
Please familiarize yourself with the pull request template (see below), and ensure you understand/are able to complete the template when you open your pull request. Additional information can be found in the pull request guidelines.

Get connected#

When in doubt, we recommend going together! Get connected with our community of active contributors, many of whom felt just like you when they started out and are happy to welcome you and support you as you get to know how we work, and where things are. You can reach out on any of our Official communication channels. For development questions we recommend reaching out on our development gitter chat room and for community questions reach out at community.

Choose an issue#

In general, the Matplotlib project does not assign issues. Issues are “assigned” or “claimed” by opening a PR; there is no other assignment mechanism. If you have opened such a PR, please comment on the issue thread to avoid duplication of work. Please check if there is an existing PR for the issue you are addressing. If there is, try to work with the author by submitting reviews of their code or commenting on the PR rather than opening a new PR; duplicate PRs are subject to being closed. However, if the existing PR is an outline, unlikely to work, or stalled, and the original author is unresponsive, feel free to open a new PR referencing the old one.

Start a pull request#

The preferred way to contribute to Matplotlib is to fork the main repository on GitHub, then submit a “pull request” (PR). To work on a a pull request:

First set up a development environment, either by cloning a copy of the Matplotlib repository to your own computer or by using Github codespaces, by following the instructions in Setting up scikit-plots for development
Then start solving the issue, following the guidance in development workflow
As part of verifying your changes check that your contribution meets the pull request guidelines and then open a pull request.
Finally follow up with maintainers on the PR if waiting more than a few days for feedback. Update the pull request as needed.

If you have questions of any sort, reach out on the Contributor incubator and join the New contributors meeting.