altair

library6.2.2pypypi✓ verified 25d ago

Altair is a declarative statistical visualization library for Python, built on top of the powerful Vega-Lite JSON specification. It offers a simple, friendly, and consistent API for creating a wide range of interactive and aesthetically pleasing statistical charts with a minimal amount of code. The library is actively developed, with a current stable version of 6.0.0 and weekly pre-release builds.

data serialization

pip install altair

github ↗package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · ALTAIR

altair

datapythonv6.2.2

Install

10.4s avg

Import

1761ms

Disk

561MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v6.2.2 · 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

glibc

py 3.10

1/2 runs

✓ 10.65s

py 3.11

1/2 runs

✓ 10.15s

py 3.12

1/2 runs

✓ 9.45s

py 3.13

1/2 runs

✓ 9.75s

py 3.9

1/2 runs

✓ 11.9s

561MB installed

● package 561MB

Code

Verified usage

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

Chart

✓ import altair as alt

Standard convention for Altair imports.

data

✓ from altair.datasets import data

✗ from vega_datasets import data

As of Altair v6.0.0, the recommended way to access built-in datasets is via `altair.datasets`. The `vega_datasets` package is still functional but less integrated for new examples.

This quickstart demonstrates how to create a basic bar chart using Altair with a pandas DataFrame. It defines a DataFrame, then uses `alt.Chart()` to specify the data, `mark_bar()` to choose the chart type, and `encode()` to map data columns to visual properties (x and y axes). The `.show()` method attempts to render the chart in a compatible environment.

import altair as alt import pandas as pd # Sample data data = pd.DataFrame({ 'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'], 'b': [28, 55, 43, 91, 81, 53, 19, 87, 52] }) # Create a simple bar chart chart = alt.Chart(data).mark_bar().encode( x='a', y='b' ) chart.show()

Debug

Known issues

breakingAltair 6.0.0 officially removes support for Python 3.8. Users should upgrade to Python 3.9 or newer.

fix

Upgrade your Python environment to 3.9 or higher. Review `pyproject.toml` or `setup.cfg` for `requires_python` constraints.

affects: 6.0.0 and above

breakingAltair 6.0.0 is compiled against Vega-Lite version 6. While largely backward compatible, some advanced or undocumented Vega-Lite features might behave differently or require updates if you were directly manipulating the underlying JSON spec.

fix

Consult the Vega-Lite 6 release notes for any specific breaking changes relevant to your custom specifications. Test existing complex charts after upgrading.

affects: 6.0.0 and above

breakingThe recommended way to access built-in example datasets changed from `from vega_datasets import data` to `from altair.datasets import data` in Altair 6.0.0. Some column names in these datasets might also have changed (e.g., spaces instead of underscores).

fix

Update import statements to `from altair.datasets import data`. If encountering `KeyError` for column names, check the dataset documentation or inspect the DataFrame for updated column names.

affects: 6.0.0 and above

gotchaAltair charts are primarily designed for display in Jupyter environments (Notebook, Lab, VS Code with Jupyter extension). Outside of these, you may need to configure a specific renderer (e.g., 'mimetype', 'html', 'json', 'altair_saver') to display or save charts.

fix

For non-Jupyter environments, ensure you explicitly call `chart.save('chart.html')`, `chart.to_json()`, or configure a renderer like `alt.renderers.enable('altair_saver')` (requires `vl-convert-python`).

affects: All versions

breakingBuilding `vegafusion` (a dependency of Altair) on Alpine Linux environments may fail due to missing `glibc` or `libgcc` components required by Rust/Cargo build tools. This typically manifests as 'Error loading shared library libgcc_s.so.1: No such file or directory' errors during metadata generation.

fix

To resolve this, install `libgcc` and `libstdc++` in your Alpine environment or Dockerfile before installing Python packages that rely on Rust extensions. For example, using `apk add libgcc libstdc++`.

affects: 6.0.0 and above

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'altair'

The Altair library is not installed in the Python environment.

fix

Install Altair using pip: `pip install altair`.

AttributeError: module 'altair' has no attribute 'selection_point'

The 'selection_point' attribute is not available in the installed version of Altair.

fix

Upgrade Altair to the latest version: `pip install --upgrade altair`.

AttributeError: module 'altair' has no attribute 'Chart'

A local file named 'altair.py' is shadowing the Altair library.

fix

Rename or remove the local 'altair.py' file to avoid the naming conflict.

AttributeError: module 'jsonschema' has no attribute 'Draft7Validator'

An outdated version of the 'jsonschema' library is installed.

fix

Upgrade 'jsonschema' to a compatible version: `pip install 'jsonschema>=3.0,<4.0'`.

ModuleNotFoundError: No module named 'altair.vegalite.v4'

The installed version of Altair does not include the 'vegalite.v4' module.

fix

Ensure compatibility between Altair and dependent libraries by installing compatible versions or upgrading dependent libraries.

Upgrade

Version history

6.2.2latest on PyPI · released Jun 23, 2026

Audit

Dependencies

pythonrequiredRuntime environment

entrypointsrequiredRequired for package entry points

jsonschemarequiredRequired for schema validation

numpyrequiredTypically used for data handling; implicitly installed with pandas

pandasrequiredCommonly used for data structures (DataFrames)

toolzrequiredFunctional utilities

vega_datasetsoptionalProvides sample datasets for examples and tutorials

ipythonoptionalEnhanced interactive Python shell, often used with Jupyter

jupyteroptionalFor rendering charts in Jupyter notebooks/Lab

jupyterlaboptionalFor rendering charts in Jupyter notebooks/Lab

polarsoptionalAlternative DataFrame library for data handling

pyarrowoptionalData serialization and interoperability, sometimes used with Polars

vl-convert-pythonoptionalEnables PNG and SVG export of Altair charts

Agent activity

66 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/vega/altair ↗

packagehttps://pypi.org/project/altair/ ↗

homepagehttps://altair-viz.github.io ↗

docshttps://altair-viz.github.io ↗