altair README

The project's README from github.com/vega/altair. As one markdown file: README.md.

Vega-Altair

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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:

Binder Colab

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",
)

Vega-Altair Visualization

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(
    x="count(Origin)",
    y="Origin",
    color="Origin",
).transform_filter(
    brush
)

points & bars

Vega-Altair Visualization Gif

Features

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

uv Ruff pytest

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

Citing Vega-Altair

JOSS Paper

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}
}

altair (vega/altair)

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10.5K ·GitHub
Altair
What is this?Vega-Altair is a declarative statistical visualization library for Python, letting you build charts by describing data mappings instead of drawing commands.
Is it a plotting library or a full app?It's a Python library that wraps the Vega-Lite JSON specification with a type-checked Python API, rendering charts inline rather than running as a standalone app.
How do I install it and make my first chart?Install via pip or conda, then import altair and pass a DataFrame to a Chart object with encodings to render it in Jupyter or VS Code.
Do I need Jupyter to use it?Not strictly required, but it's designed to display natively in JupyterLab, Jupyter Notebook, and VS Code, and can also export to standalone HTML or static images.
Does it generate the charts itself or hand off to another renderer?Rendering is delegated to the Vega-Lite JS engine, so the actual chart drawing happens via that underlying spec, not Altair's Python code directly.
Can I export my charts to other formats?Yes, visualizations can be exported to PNG/SVG images, stand-alone HTML pages, the Online Vega-Lite Editor, or serialized as JSON files.
Does it support interactive charts like linked selections?Yes, Altair inherits Vega-Lite's declarative grammar of interaction, so you can build things like a histogram that filters based on a scatter plot selection with only minor changes to a static chart.
What are similar projects?matplotlib, plotly.py, bokeh, fg-data-profiling, DeepAnalyze, notebook, prettymaps, pygwalker
Which topics is it in?charting-library, data, jupyter, jupyter-notebook, data-analysis, academic, visualization, data-visualization, data-science, json, bsd, dashboard
Creator
vega
Size
123 MB
Stars
10.5K
Updated
CommitOct 14Releasev6.3.0Sep 15
Age
11y
Commits · 90 Days