You've discovered a bug or something else you want to change in Matplotlib — excellent!

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

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

This project is a community effort, and everyone is welcome to contribute. Everyone within the community is expected to abide by our code of conduct.

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

Get started#

There is no pre-defined pathway for new contributors -- we recommend looking at existing issue and 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.

Do I really have something to contribute to Matplotlib?

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 Matplotlib 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 Matplotlib user but a domain expert: you know about visualization, 3D plotting, design, technical writing, statistics, or some other field where Matplotlib 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 Matplotlib, 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 Matplotlib'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 Matplotlib 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.


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


You as an end-user of Matplotlib can make a valuable contribution because you 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:

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


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 Matplotlib guidelines.

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

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.

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.

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!

Work on 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.

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.

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). You can do this by cloning a copy of the Maplotlib repository to your own computer, or alternatively using GitHub Codespaces, a cloud-based in-browser development environment that comes with the appropriated setup to contribute to Matplotlib.

Workflow overview#

A brief overview of the workflow is as follows.

  1. Create an account on GitHub if you do not already have one.

  2. Fork the project repository by clicking on the Fork button near the top of the page. This creates a copy of the code under your account on the GitHub server.

  3. Set up a development environment:

    Clone this copy to your local disk:

    git clone<YOUR GITHUB USERNAME>/matplotlib.git

    Check out the Matplotlib repository and activate your development environment:

    1. Open codespaces on your fork by clicking on the green "Code" button on the GitHub web interface and selecting the "Codespaces" tab.

    2. Next, click on "Open codespaces on <your branch name>". You will be able to change branches later, so you can select the default main branch.

    3. After the codespace is created, you will be taken to a new browser tab where you can use the terminal to activate a pre-defined conda environment called mpl-dev:

      conda activate mpl-dev
  4. Install the local version of Matplotlib with:

    python -m pip install --no-build-isolation --editable .[dev]

    See Setting up Matplotlib for development for detailed instructions.

  5. Create a branch to hold your changes:

    git checkout -b my-feature origin/main

    and start making changes. Never work in the main branch!

  6. Work on this task using Git to do the version control. Codespaces persist for some time (check the documentation for details) and can be managed on codespaces. When you're done editing e.g., lib/matplotlib/, do:

    git add lib/matplotlib/
    git commit

    to record your changes in Git, then push them to your GitHub fork with:

    git push -u origin my-feature

GitHub Codespaces workflows#

If you need to open a GUI window with Matplotlib output on Codespaces, our configuration includes a light-weight Fluxbox-based desktop. You can use it by connecting to this desktop via your web browser. To do this:

  1. Press F1 or Ctrl/Cmd+Shift+P and select

    Ports: Focus on Ports View in the VSCode session to bring it into focus. Open the ports view in your tool, select the noVNC port, and click the Globe icon.

  2. In the browser that appears, click the Connect button and enter the desktop

    password (vscode by default).

Check the GitHub instructions for more details on connecting to the desktop.

View documentation#

If you also built the documentation pages, you can view them using Codespaces. Use the "Extensions" icon in the activity bar to install the "Live Server" extension. Locate the doc/build/html folder in the Explorer, right click the file you want to open and select "Open with Live Server."

Open a pull request on Matplotlib#

Finally, go to the web page of your fork of the Matplotlib repo, and click Compare & pull request to send your changes to the maintainers for review. The base repository is matplotlib/matplotlib and the base branch is generally main. For more guidance, see GitHub's pull request tutorial.

For more detailed instructions on how to set up Matplotlib for development and best practices for contribution, see Setting up Matplotlib for development and Development workflow.