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Vega-Altair is a declarative statistical visualization library for Python. With Vega-Altair, you can spend more time understanding your data and its meaning. Vega-Altair'sAPI is simple, friendly and consistent and built on top of the powerfulVega-Lite JSON specification. This elegantsimplicity produces beautiful and effective visualizations with a minimal amount of code.

Vega-Altair was originally developed byJake Vanderplas andBrianGranger in close collaboration with theUWInteractive Data Lab.The Vega-Altair open source project is not affiliated with Altair Engineering, Inc.

SeeVega-Altair's Documentation Site as well as theTutorial Notebooks. You canrun the notebooks directly in your browser by clicking on one of the following badges:

Here is an example using Vega-Altair to quickly visualize and display a dataset with the native Vega-Lite renderer in the JupyterLab:

importaltairasalt# load a simple dataset as a pandas DataFramefromvega_datasetsimportdatacars=data.cars()alt.Chart(cars).mark_point().encode(x='Horsepower',y='Miles_per_Gallon',color='Origin',)

One of the unique features of Vega-Altair, inherited from Vega-Lite, is a declarative grammar of not just visualization, butinteraction.With a few modifications to the example above we can create a linked histogram that is filtered based on a selection of the scatter plot.

importaltairasaltfromvega_datasetsimportdatasource=data.cars()brush=alt.selection_interval()points=alt.Chart(source).mark_point().encode(x='Horsepower',y='Miles_per_Gallon',color=alt.when(brush).then("Origin").otherwise(alt.value("lightgray"))).add_params(brush)bars=alt.Chart(source).mark_bar().encode(y='Origin',color='Origin',x='count(Origin)').transform_filter(brush)points&bars

• Carefully-designed, declarative Python API.
• Auto-generated internal Python API that guarantees visualizations are type-checked andin full conformance with theVega-Litespecification.
• Display visualizations in JupyterLab, Jupyter Notebook, Visual Studio Code, on GitHub andnbviewer, and many more.
• Export visualizations to various formats such as PNG/SVG images, stand-alone HTML pages and theOnline Vega-Lite Editor.
• Serialize visualizations as JSON files.

Vega-Altair can be installed with:

pip install altair

If you are using the conda package manager, the equivalent is:

conda install altair -c conda-forge

For full installation instructions, please seethe documentation.

If you have a question that is not addressed in the documentation,you can post it onStackOverflow using thealtair tag.For bugs and feature requests, please open aGithub Issue.

For information on how to contribute your developments back to the Vega-Altair repository, seeCONTRIBUTING.md

If you use Vega-Altair in academic work, please consider citinghttps://joss.theoj.org/papers/10.21105/joss.01057 as

@article{VanderPlas2018,doi ={10.21105/joss.01057},url ={https://doi.org/10.21105/joss.01057},year ={2018},publisher ={The Open Journal},volume ={3},number ={32},pages ={1057},author ={Jacob VanderPlas and Brian Granger and Jeffrey Heer and Dominik Moritz and Kanit Wongsuphasawat and Arvind Satyanarayan and Eitan Lees and Ilia Timofeev and Ben Welsh and Scott Sievert},title ={Altair: Interactive Statistical Visualizations for Python},journal ={Journal of Open Source Software}}

Please additionally consider citing theVega-Lite project, which Vega-Altair is based on:https://dl.acm.org/doi/10.1109/TVCG.2016.2599030

@article{Satyanarayan2017,author={Satyanarayan, Arvind and Moritz, Dominik and Wongsuphasawat, Kanit and Heer, Jeffrey},title={Vega-Lite: A Grammar of Interactive Graphics},journal={IEEE transactions on visualization and computer graphics},year={2017},volume={23},number={1},pages={341-350},publisher={IEEE}}