This release is a pre-release and may not be stable for production use.
Vega-Altair 
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.
Documentation
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:
Example
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 altair.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 altair.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
Features
- 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.
Installation
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.
Getting Help
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.
Development
For information on how to contribute your developments back to the Vega-Altair repository, see
CONTRIBUTING.md
Citing Vega-Altair
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}
}
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