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Feedback and Contribution

We welcome any input, feedback, bug reports, and contributions via Altair's GitHub Repository. In particular, we welcome companion efforts from other visualization libraries to render the Vega-Lite specifications output by Altair. We see this portion of the effort as much bigger than Altair itself: the Vega and Vega-Lite specifications are perhaps the best existing candidates for a principled lingua franca of data visualization.

We are also seeking contributions of additional Jupyter notebook-based examples in our separate GitHub repository: https://github.com/altair-viz/altair_notebooks.

All contributions, suggestions, and feedback you submitted are accepted under the Project's license. You represent that if you do not own copyright in the code that you have the authority to submit it under the Project's license. All feedback, suggestions, or contributions are not confidential. The Project abides by the Vega Organization's code of conduct and governance.

How To Contribute Code to Vega-Altair

Setting Up Your Environment

Fork the Altair repository on GitHub and then clone the fork to you local machine. For more details on forking see the GitHub Documentation.

git clone https://github.com/YOUR-USERNAME/altair.git

To keep your fork up to date with changes in this repo, you can use the fetch upstream button on GitHub.

Now you can install the latest version of Altair locally using pip. The -e flag indicates that your local changes will be reflected every time you open a new Python interpreter (instead of having to reinstall the package each time).

cd altair/ 
python -m pip install -e ".[all, dev]"

'[all, dev]' indicates that pip should also install the optional and development requirements which you can find in pyproject.toml ([project.optional-dependencies]/all and [project.optional-dependencies]/dev)

Creating a Branch

Once your local environment is up-to-date, you can create a new git branch which will contain your contribution (always create a new branch instead of making changes to the main branch):

git switch -c <your-branch-name>

With this branch checked-out, make the desired changes to the package.

A large part of Altair's code base is automatically generated. After you have made your manual changes, make sure to run the following to see if there are any changes to the automatically generated files:

hatch run generate-schema-wrapper

For information on how to update the Vega-Lite version that Altair uses, please read the maintainers' notes.

Testing your Changes

Before submitting your changes to the main Altair repository, it is recommended that you run the Altair test suite, which includes a number of tests to validate the correctness of your code:

hatch test

This also runs the ruff linter and formatter as well as mypy as type checker.

Study the output of any failed tests and try to fix the issues before proceeding to the next section.

Failures on specific python version(s)

By default, hatch test will run the test suite against the currently active python version. Two useful variants for debugging failures that only appear after you've submitted your PR:

# Test against all python version(s) in the matrix
hatch test --all
# Test against a specific python version
hatch test --python 3.8

See hatch test docs for other options.

Changes to __all__

If test_completeness_of__all__ fails, you may need to run:

hatch run update-init-file

However, this test usually indicates unintentional addition(s) to the top-level alt. namespace that will need resolving first.

Creating a Pull Request

When you are happy with your changes, you can commit them to your branch by running

git add <modified-file>
git commit -m "Some descriptive message about your change"
git push origin <your-branch-name>

You will then need to submit a pull request (PR) on GitHub asking to merge your example branch into the main Altair repository. For details on creating a PR see GitHub documentation Creating a pull request. You can add more details about your example in the PR such as motivation for the example or why you thought it would be a good addition. You will get feed back in the PR discussion if anything needs to be changed. To make changes continue to push commits made in your local example branch to origin and they will be automatically shown in the PR.

Hopefully your PR will be answered in a timely manner and your contribution will help others in the future.

How To Contribute Documentation to Vega-Altair

Altair documentation is written in reStructuredText and compiled into html pages using Sphinx. Contributing to the documentation requires some extra dependencies and we have some conventions and plugins that are used to help navigate the docs and generate great Altair visualizations.

Note that the Altair website is only updated when a new version is released so your contribution might not show up for a while.

Adding Examples

We are always interested in new examples contributed from the community. These could be everything from simple one-panel scatter and line plots, to more complicated layered or stacked plots, to more advanced interactive features. Before submitting a new example check the Altair Example Gallery to make sure that your idea has not already been implemented.

Once you have an example you would like to add there are a few guide lines to follow. Every example should:

  • have a arguments_syntax and methods_syntax implementation. Each implementation must be saved as a stand alone script in the tests/examples_arguments_syntax and tests/examples_methods_syntax directories.
  • have a descriptive docstring, which will eventually be extracted for the documentation website.
  • contain a category tag.
  • define a chart variable with the main chart object (This will be used both in the unit tests to confirm that the example executes properly, and also eventually used to display the visualization on the documentation website).
  • not make any external calls to download data within the script (i.e. don't use urllib). You can define your data directly within the example file, generate your data using pandas and numpy, or you can use data available in the vega_datasets package.

The easiest way to get started would be to adapt examples from the Vega-Lite example gallery which are missing in the Altair gallery. Or you can feel free to be creative and build your own visualizations.

Often it is convenient to draft an example outside of the main repository, such as Google Colab, to avoid difficulties when working with git. Once you have an example you would like to add, follow the same contribution procedure outlined above.

Some additional notes:

  • The format and style of new contributions should generally match that of existing examples.
  • The file docstring will be rendered into HTML via reStructuredText, so use that format for any hyperlinks or text styling. In particular, be sure you include a title in the docstring underlined with ---, and be sure that the size of the underline exactly matches the size of the title text.
  • If your example fits into a chart type but involves significant configuration it should be in the Case Studies category.
  • For consistency all data used for a visualization should be assigned to the variable source. Then source is passed to the alt.Chart object. If the example requires multiple dataframes then this does not apply. See other examples for guidance.
  • Example code should not require downloading external datasets. We suggest using the vega_datasets package if possible. If you are using the vega_datasets package there are multiple ways to refer to a data source. If the dataset you would like to use is included in local installation (vega_datasets.local_data.list_datasets()) then the data can be referenced directly, such as source = data.iris(). If the data is not included then it should be referenced by URL, such as source = data.movies.url. This is to ensure that Altair's automated test suite does not depend on availability of external HTTP resources.
  • If VlConvert does not support PNG export of the chart (e.g. in the case of emoji), then add the name of the example to the SVG_EXAMPLES set in tests/examples_arguments_syntax/__init__.py and tests/examples_methods_syntax/__init__.py

Building the Documentation Locally

The process to build the documentation locally consists of three steps:

  1. Clean any previously generated files to ensure a clean build.
  2. Generate the documentation in HTML format.
  3. View the generated documentation using a local Python testing server.

The specific commands for each step depend on your operating system. Make sure you execute the following commands from the root dir of altair and have hatch installed in your local environment.

  • For MacOS and Linux, run the following commands in your terminal:
hatch run doc:clean-all
hatch run doc:build-html
hatch run doc:serve
  • For Windows, use these commands instead:
hatch run doc:clean-all-win
hatch run doc:build-html-win
hatch run doc:serve

To view the documentation, open your browser and go to http://localhost:8000. To stop the server, use ^C (control+c) in the terminal.

Part of MVG-0.1-beta. Made with love by GitHub. Licensed under the CC-BY 4.0 License.