You can contribute to Matplotlib without being a project expert: choose a manageable code, documentation, triage, or community task; check its issue and pull-request history; then submit work from a GitHub fork and verify it before asking for review. The official Matplotlib contributing guide welcomes first-time contributors and says nobody is expected to understand the entire codebase at the outset.
What can you contribute to Matplotlib?
Code is only one route. Matplotlib also welcomes documentation improvements, issue triage, and community support. A small typo fix or clearer docstring can be a useful documentation contribution; examples and tutorials are larger options. For code, contributions include bug fixes, features, and maintenance.
If you are new, first learn the context around the area you want to change. Read related issue and pull-request discussions, explore the code, or ask the community for guidance. The guide recommends starting with its pull-request instructions for code contributions.
How do I find a good first issue?
- Open the contributing guide and follow its issue-tracker guidance. The optional “Difficulty: Easy” and “Good first issue” filters can help narrow the list.
- Read the issue and its discussion, then search the pull-request tracker to see whether someone has already started work.
- If a contributor is already working on it, contact them about collaborating rather than duplicating the change.
- Choose work you can handle independently in a reasonable time. Ask for help judging complexity if the scope is unclear.
Matplotlib generally does not assign issues; opening a pull request is how work is claimed. Check the issue and related PR threads before starting. The project describes an easy issue as suitable for someone with beginner scientific Python experience—comfortable with Python syntax and some experience using a library such as NumPy, pandas, or xarray. Medium and hard tasks may involve advanced Python, dependencies across the codebase, legacy areas, or substantial algorithmic and architectural changes.
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Choose a development environment
You can work locally or in GitHub Codespaces. The official development setup guide presents Codespaces as convenient for a relatively simple, one-off change because much of the environment is prepared. Local setup can suit frequent or extensive work and avoids Codespaces monthly usage limits.
For local development, the guide covers both venv and conda approaches. It currently documents pip install --group dev for a virtual environment or creating the mpl-dev conda environment from environment.yml. Local development also needs compilers and external tools for building Matplotlib or its documentation; the setup guide links to the full dependency list. Codespaces does not require installing those local external dependencies.
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Set up a local editable checkout
- Fork
matplotlib/matplotlibon GitHub, then clone your fork. - Add the main Matplotlib repository as the
upstreamremote. - Create a dedicated development environment using the current instructions for
venvor conda on the setup page. - From the repository directory, install the checkout in editable mode with the command currently documented by Matplotlib:
python -m pip install --verbose --no-build-isolation --group dev --editable .
An editable install makes the working tree importable in the environment, so you can test edits without reinstalling after each change. The setup and install commands are development-documentation details that may change; confirm them on the linked official page when you set up.
Make a focused change and verify it
Follow the project’s development workflow while editing. Before submitting, check that the change addresses the issue rather than only appearing plausible.
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- For code: run the relevant tests. If the issue includes a reproducible example, try it against your changed branch; adapting that example into a test can help prevent regressions.
- For documentation: build the documentation locally, then inspect the rendered pages and check links.
- For plotting-related features: include examples where appropriate, as called out in the pull-request checklist.
- For new features or API changes: add a release note, as requested by the project checklist.
Use an expressive title and follow the project’s documentation guidance when it applies. Match the verification effort to the change: a code fix needs behavioral checks, while a documentation edit needs a rendered build and link review.
How do I start a pull request?
- Push your branch to your fork of
matplotlib/matplotlib. - Open a pull request from your fork to the main repository. The base branch is generally
main. - Write a clear summary in your own words: explain what changed and why. Follow the pull-request template, including its disclosure of whether and how you used AI.
- If the change is not ready to merge but you want early feedback, mark the PR as a draft and explain what you want reviewers to examine.
- Respond to review comments and make follow-up commits as needed. Matplotlib’s guide encourages first-time contributors to complete review on their first PR and wait for it to be merged or closed before opening another.
If a submitted PR has received no feedback for more than a few days, the guide advises following up with maintainers. The official contributing guide and workflow page provide the project’s current process and checklist.
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Can I contribute without being an expert?
Yes. The project explicitly encourages newcomers to start with a task they can reasonably handle, learn from the surrounding discussions, and ask questions when needed. You do not need to understand the whole codebase before making a useful contribution. A small documentation correction, a well-scoped beginner issue, or helpful issue triage can all be legitimate ways to participate.
If you need help with Git, GitHub, technical questions, writing, or the review process, Matplotlib’s public Discourse contributor incubator is moderated by core developers. The project also holds a monthly new-contributors meeting; its calendar is linked through the Scientific Python website. See the community pointers in the contributing guide.
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Can I use AI when contributing?
Matplotlib’s current guide says the human contributor remains responsible for AI-assisted work. It describes support such as understanding existing code, developing solution ideas, or proofreading and translating the contributor’s own wording as acceptable. It says external AI tools must not interact directly with project channels—for example, by creating issues or pull requests, or commenting on GitHub or Discourse—and contributions should reflect authentic engagement and work the contributor understands. The guide warns that AI-generated pull requests to good-first issues will be closed. Read the live AI guidance before using AI, since project policy can change.
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