vegafusion

library2.0.3pypypiunverified

VegaFusion provides server-side acceleration for the Vega visualization grammar, primarily designed to enhance Python libraries like Altair. It enables scaling interactive charts to large datasets by offloading data transformations from the browser to an efficient, multi-threaded Rust-based Python kernel. The library is currently at version 2.0.3 and maintains an active development and release cadence.

data serialization

pip install vegafusion altair

github ↗package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · VEGAFUSION

vegafusion

datapythonv2.0.3

Install

4.2s avg

Import

96ms

Disk

112MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.0.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

build_error

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 4.2s · import 0.096s · 113MB

112MB installed

● package 112MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

vegafusion

✓ import vegafusion as vf

Standard import for the VegaFusion library.

enable/save/transformed_data (for Altair)

✓ import altair as alt alt.data_transformers.enable('vegafusion')

✗ import vegafusion as vf; vf.enable()

As of VegaFusion 2.0, Altair integration logic has been upstreamed. Use Altair's data transformers directly. `vf.save()` and `vf.transformed_data()` are also removed in favor of Altair's native methods when the transformer is enabled.

This quickstart demonstrates how to enable VegaFusion for Altair visualizations. As of VegaFusion 2.0, the integration is handled by enabling Altair's built-in 'vegafusion' data transformer. This allows Altair charts to automatically offload data-intensive operations to the VegaFusion backend.

import altair as alt from vega_datasets import data # Enable the VegaFusion data transformer within Altair alt.data_transformers.enable('vegafusion') # Example Altair chart that benefits from VegaFusion's server-side processing source = data.flights.url chart = alt.Chart(source).mark_bar().encode( x=alt.X('distance:Q', bin=True), y='count()' ).properties(title='Flight Distance Histogram (Accelerated by VegaFusion)') # To display the chart in a Jupyter environment: chart # To save the chart with transformed data (using Altair's native save method): # chart.save('flight_histogram.json')

vegafusion --version

Debug

Known issues

breakingVegaFusion 2.0 removed the `vegafusion-jupyter` package and direct Altair integration methods (like `vf.enable()`, `vf.save()`, `vf.transformed_data()`) from the `vegafusion` package. These functionalities are now handled directly by Altair's built-in 'vegafusion' data transformer.

fix

Instead of `import vegafusion as vf; vf.enable()`, use `import altair as alt; alt.data_transformers.enable('vegafusion')`. For saving or extracting transformed data, use Altair's native `chart.save()` or `chart.to_dict()` methods after enabling the transformer.

affects: >=2.0.0

breakingVegaFusion 2.0 requires Altair version 5.5.0 or higher for full compatibility. Older Altair versions may not work or exhibit unexpected behavior.

fix

Ensure Altair is updated to `altair>=5.5.0` using `pip install altair>=5.5.0`.

affects: >=2.0.0

breakingVegaFusion 2.0 significantly reduced its Python dependencies. `pyarrow` and `pandas` are no longer required, instead relying on `narwhals` for DataFrame compatibility and `arrow3-core` for Arrow data. Existing code heavily dependent on `pyarrow` or `pandas` might need adjustments if not explicitly installed alongside.

fix

Review your data handling. If you require `pandas` or `pyarrow` for other parts of your workflow, ensure they are still installed. VegaFusion 2.0 supports any Narwhals-compatible DataFrame type or objects supporting the Arrow PyCapsule interface for zero-copy data transfer.

affects: >=2.0.0

gotchaWhen working in cloud notebook environments, VegaFusion's local timezone might differ from the browser's. This can lead to discrepancies in date/time-based visualizations or transformations.

fix

Customize VegaFusion's local timezone using `vegafusion.set_local_tz("Your/Timezone")` at the beginning of your session to ensure consistent time interpretation.

affects: All

Upgrade

Version history

2.0.3latest on PyPI · released Sep 29, 2025

Audit

Dependencies

pythonrequiredRequired for the Python package.

altairrequiredCommonly used with VegaFusion for visualization; requires specific version for compatibility with VegaFusion 2.x.

narwhalsrequiredVegaFusion 2.0+ uses Narwhals as a general interface to DataFrames, reducing direct dependencies on pandas/pyarrow.

arrow3-corerequiredCore dependency for Arrow data handling in VegaFusion 2.0+.

packagingrequiredDependency for packaging utilities in VegaFusion 2.0+.

Agent activity