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https://github.com/vega/altair

Declarative visualization library for Python
https://github.com/vega/altair

Last synced: 4 days ago
JSON representation

Declarative visualization library for Python

Host: GitHub
URL: https://github.com/vega/altair
Owner: vega
License: bsd-3-clause
Created: 2015-09-19T03:14:04.000Z (over 9 years ago)
Default Branch: main
Last Pushed: 2025-02-10T14:12:13.000Z (11 days ago)
Last Synced: 2025-02-10T18:53:02.520Z (11 days ago)
Language: Python
Homepage: https://altair-viz.github.io/
Size: 43.2 MB
Stars: 9,568
Watchers: 138
Forks: 794
Open Issues: 149
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

StarryDivineSky - vega/altair - Altair是一个用于 Python 的声明式统计可视化库，它基于强大的Vega-Lite JSON 规范，提供简单易用的 API，帮助你快速创建美观且有效的可视化图表。你可以在 JupyterLab、Jupyter Notebook、Visual Studio Code 等环境中使用它，并导出为 PNG/SVG 等格式。Vega-Altair 的独特之处在于它不仅支持可视化，还支持交互，例如使用刷选工具过滤散点图并联动更新直方图。 (其他_机器学习与深度学习)
awesome-production-machine-learning - Vega-Altair - Vega-Altair is a declarative statistical visualization library for Python. (Industry Strength Visualisation)

README

        # Vega-Altair 

[![github actions](https://github.com/vega/altair/workflows/build/badge.svg)](https://github.com/vega/altair/actions?query=workflow%3Abuild)

[![typedlib_mypy](https://www.mypy-lang.org/static/mypy_badge.svg)](https://www.mypy-lang.org)

[![JOSS Paper](https://joss.theoj.org/papers/10.21105/joss.01057/status.svg)](https://joss.theoj.org/papers/10.21105/joss.01057)

[![PyPI - Downloads](https://img.shields.io/pypi/dm/altair)](https://pypi.org/project/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](https://github.com/vega/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](https://github.com/jakevdp) and [Brian

Granger](https://github.com/ellisonbg) in close collaboration with the [UW

Interactive Data Lab](https://idl.cs.washington.edu/).*

*The Vega-Altair open source project is not affiliated with Altair Engineering, Inc.*

## Documentation

See [Vega-Altair's Documentation Site](https://altair-viz.github.io) as well as the [Tutorial Notebooks](https://github.com/altair-viz/altair_notebooks). You can

run the notebooks directly in your browser by clicking on one of the following badges:

[![Binder](https://beta.mybinder.org/badge.svg)](https://beta.mybinder.org/v2/gh/altair-viz/altair_notebooks/master)

[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/altair-viz/altair_notebooks/blob/master/notebooks/Index.ipynb)

## Example

Here is an example using Vega-Altair to quickly visualize and display a dataset with the native Vega-Lite renderer in the JupyterLab:

```python

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',

)

```

![Vega-Altair Visualization](https://raw.githubusercontent.com/altair-viz/altair/main/images/cars.png)

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.

```python 

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

```

![Vega-Altair Visualization Gif](https://raw.githubusercontent.com/altair-viz/altair/main/images/cars_scatter_bar.gif)

## 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](https://github.com/vega/vega-lite)

  specification.

* Display visualizations in JupyterLab, Jupyter Notebook, Visual Studio Code, on GitHub and

  [nbviewer](https://nbviewer.jupyter.org/), and many more.

* Export visualizations to various formats such as PNG/SVG images, stand-alone HTML pages and the

[Online Vega-Lite Editor](https://vega.github.io/editor/#/).

* Serialize visualizations as JSON files.

## Installation

Vega-Altair can be installed with:

```bash

pip install altair

```

If you are using the conda package manager, the equivalent is:

```bash

conda install altair -c conda-forge

```

For full installation instructions, please see [the documentation](https://altair-viz.github.io/getting_started/installation.html).

## Getting Help

If you have a question that is not addressed in the documentation, 

you can post it on [StackOverflow](https://stackoverflow.com/questions/tagged/altair) using the `altair` tag.

For bugs and feature requests, please open a [Github Issue](https://github.com/vega/altair/issues).

## Development

[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)

[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

[![pytest](https://img.shields.io/badge/logo-pytest-blue?logo=pytest&labelColor=5c5c5c&label=%20)](https://github.com/pytest-dev/pytest)

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

[`CONTRIBUTING.md`](https://github.com/vega/altair/blob/main/CONTRIBUTING.md)

## Citing Vega-Altair

[![JOSS Paper](https://joss.theoj.org/papers/10.21105/joss.01057/status.svg)](https://joss.theoj.org/papers/10.21105/joss.01057)

If you use Vega-Altair in academic work, please consider citing https://joss.theoj.org/papers/10.21105/joss.01057 as

```bib

@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](https://vega.github.io/vega-lite/) project, which Vega-Altair is based on: https://dl.acm.org/doi/10.1109/TVCG.2016.2599030

```bib

@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}

} 

```