VegaFusion provides serverside acceleration for the Vega visualization grammar. While not limited to Python, an initial application of VegaFusion is the acceleration of the Altair Python interface to Vega-Lite.

The core VegaFusion algorithms are implemented in Rust. Python integration is provided using PyO3 and JavaScript integration is provided using wasm-bindgen.

For more info on the future direction of the project, see the Roadmap and Licensing and Funding pages.

Quickstart: Accelerate Altair in Jupyter

VegaFusion can be used to provide serverside acceleration for Altair visualizations when displayed in Jupyter contexts (Classic notebook, JupyterLab, and Voila). First, install the vegafusion-jupyter package, along with vega-datasets for the example below.

$ pip install "vegafusion-jupyter[embed]" vega-datasets

Then, open a jupyter notebook (either the classic notebook, or a notebook inside JupyterLab), and run these two lines to import and enable the VegaFusion Altair renderer.

import vegafusion as vf

VegaFusion will now be used to accelerate any Altair chart. For example, here’s the interactive average Altair gallery example.

import altair as alt
from vega_datasets import data

source = data.seattle_weather()
brush = alt.selection(type='interval', encodings=['x'])

bars = alt.Chart().mark_bar().encode(
    opacity=alt.condition(brush, alt.OpacityValue(1), alt.OpacityValue(0.7)),

line = alt.Chart().mark_rule(color='firebrick').encode(

chart = alt.layer(bars, line, data=source)

Histogram binning, aggregation, selection filtering, and average calculations will now be evaluated in the Python kernel process with efficient parallelization, rather than in the single-threaded browser context.

You can see that VegaFusion acceleration is working by noticing that the Python kernel is running as the selection region is created or moved. You can also notice the VegaFusion logo in the dropdown menu button.

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