The Tool Desk
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How do I contribute to Matplotlib?
Matplotlib’s preferred route for code contributions is to fork the main repository on GitHub and submit a pull request (PR). The same workflow can support documentation changes. You can also help by triaging issues or supporting the community.
Start by reading the project’s contributing guide and looking through discussions around the part of the project you want to improve. You do not need to understand the entire codebase first; Matplotlib says that learning it is a long-term project.
Choose a contribution that fits
- Code: Fix a bug, add a feature, or help maintain existing functionality.
- Documentation: Fix a typo, clarify a docstring, improve an example, or work on a tutorial.
- Issue triage or community support: Help clarify reports and questions, or assist other contributors.
If you are unsure which area to choose, explore existing issues and pull requests, or ask for guidance in the project’s community channels.
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How do I find a good first issue?
Use the Matplotlib issue tracker to look for tasks labeled “Difficulty: Easy” or “Good first issue.” Those filters are optional starting points, not a guarantee that a task is suitable for every newcomer. The guide describes easy work as appropriate for someone with beginner scientific Python experience: comfort with Python syntax and some experience with a library such as NumPy, pandas, or xarray.
- Open the issue and read its full description and discussion so you understand the expected change.
- Check the repository’s pull requests for work already addressing the issue.
- If another contributor is working on it, contact them in the issue or PR discussion to ask whether collaboration would help.
- If the scope or difficulty is unclear, ask the community before investing heavily in the change.
Matplotlib generally does not assign issues; opening a PR is how work is claimed. Check the relevant issue and PR threads before starting to avoid duplicating work.
Match the task to your experience
Medium- and hard-difficulty tasks may require more advanced Python, changes that cross several parts of the codebase, work in legacy areas, or substantial algorithmic or architectural decisions. For a first contribution, prefer something you can handle independently in a reasonable time. A small, well-understood documentation correction can be a better start than a large feature.
Set up your development environment
You can work locally or use GitHub Codespaces. Matplotlib describes Codespaces as convenient for a relatively simple, one-off change because much of the environment is already prepared. A local setup may be preferable for frequent or extensive work and avoids Codespaces monthly usage limits.
| Option | Best suited to | Setup considerations |
|---|---|---|
| GitHub Codespaces | A relatively simple, one-off contribution | Much of the setup is prepared; the guide says local external dependencies are not needed. |
| Local environment | Frequent or extensive development | Requires a dedicated Python environment. Building Matplotlib or its documentation can require compilers and other external tools. |
For local development, Matplotlib’s development setup guide covers forking the repository, cloning your fork, adding the main repository as the upstream remote, and creating an environment. It documents both venv and conda-based setups. Its current Python dependency options include pip install --group dev for a virtual environment or creating the mpl-dev conda environment from environment.yml. Check the guide’s separate dependency information for local compilers and external tools.
Install the working tree in editable mode
From the repository directory, the current setup guide gives this editable-install command:
python -m pip install --verbose --no-build-isolation --group dev --editable .An editable installation makes the development source importable from your working tree, so you can test changes without reinstalling after every edit. Setup instructions can change as Matplotlib’s development tooling evolves; confirm the command and dependencies in the current setup guide before using them.
Make a focused change and verify it
Follow the project’s development workflow for the area you are changing. Before asking maintainers to review a PR, check that the change addresses the problem and that the relevant behavior still works.
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For code changes
- Run the relevant tests.
- If the issue includes a reproducible code example, try it with your changed branch.
- Consider adapting that example into a test so the reported behavior remains covered.
- For plotting-related features, include examples where appropriate.
For documentation changes
- Build the documentation locally.
- Review the rendered page rather than relying only on source text.
- Check that links work and that examples display as intended.
The PR checklist also calls for an expressive title, tests for new or changed code, release notes for new features or API changes, and compliance with documentation guidance when relevant. Apply the items that fit your change rather than adding unrelated material.
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Start a pull request
Once your change is ready for review, push it to your fork and open a PR against matplotlib/matplotlib, generally targeting the main branch. Use the PR template to explain the change and why it is needed in your own words. If you want feedback before the work is finished, open a draft PR and state clearly what you would like reviewers to examine.
Matplotlib’s guide advises following up with maintainers if a submitted PR has received no feedback for more than a few days. Keep the conversation in the relevant PR so reviewers can see the context and progress.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Get help and handle review
If you are new to Git, GitHub, the review process, or the code itself, Matplotlib’s public Discourse contributor incubator is moderated by core developers and is a place to ask for help with technical questions, writing, and pre-review. The project also holds a monthly new-contributors meeting; its calendar is linked through the development documentation index.
For a first PR, the guide encourages completing review comments and waiting for that PR to be merged or closed before opening another. This gives you a chance to learn from the review and helps maintainers focus their attention.
Can I contribute to Matplotlib without being an expert?
Yes. The project explicitly welcomes newcomers, and useful contributions range from a small documentation correction to code changes and community help. You need enough context to understand the task you choose and to test or review your own change; you do not need to master the whole codebase before beginning.
Can I use AI when contributing?
Matplotlib’s current guide says the human contributor remains responsible for AI-assisted work and expects contributors to understand the resulting change. It identifies supportive uses such as helping you understand existing code, develop solution ideas, or proofread or translate your own wording. It also says external AI tools must not interact directly with project channels—for example, by creating issues or PRs or commenting on GitHub or Discourse—and warns that AI-generated PRs to good-first issues will be closed. Read the current policy before using AI, since project policies can change.
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