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's API is simple, friendly and consistent and built on top of the powerful Vega-Lite JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code.

Vega-Altair was originally developed by Jake Vanderplas and Brian Granger in close collaboration with the UW Interactive Data Lab. The Vega-Altair open source project is not affiliated with Altair Engineering, Inc.

See Vega-Altair's Documentation Site as well as the Tutorial Notebooks. You can run 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:

import altair as alt
# load a simple dataset as a pandas DataFrame
from vega_datasets import data
cars = 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, but interaction. 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.

import altair as alt
from vega_datasets import data
source = 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 and in full conformance with the Vega-Lite specification.
• Display visualizations in JupyterLab, Jupyter Notebook, Visual Studio Code, on GitHub and nbviewer, and many more.
• Export visualizations to various formats such as PNG/SVG images, stand-alone HTML pages and the Online 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 see the documentation.

If you have a question that is not addressed in the documentation, you can post it on StackOverflow using the altair tag. For bugs and feature requests, please open a Github Issue.

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

If you use Vega-Altair in academic work, please consider citing https://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 the Vega-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}
}