# Vega-Altair <a href="https://altair-viz.github.io/"><img align="right" src="https://altair-viz.github.io/_static/altair-logo-light.png" height="50"></img></a>
[](https://github.com/altair-viz/altair/actions?query=workflow%3Abuild)
[](https://www.mypy-lang.org)
[](https://joss.theoj.org/papers/10.21105/joss.01057)
[](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:
[](https://beta.mybinder.org/v2/gh/altair-viz/altair_notebooks/master)
[](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',
)
```
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.condition(brush, 'Origin', 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
```
## 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/altair-viz/altair/issues).
## Development
[](https://github.com/pypa/hatch)
[](https://github.com/astral-sh/ruff)
[](https://github.com/pytest-dev/pytest)
You can find the instructions on how to install the package for development in [the documentation](https://altair-viz.github.io/getting_started/installation.html).
To run the tests and linters, use
```
hatch run test
```
For information on how to contribute your developments back to the Vega-Altair repository, see
[`CONTRIBUTING.md`](https://github.com/altair-viz/altair/blob/main/CONTRIBUTING.md)
## Citing Vega-Altair
[](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}
}
```
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"description": "# Vega-Altair <a href=\"https://altair-viz.github.io/\"><img align=\"right\" src=\"https://altair-viz.github.io/_static/altair-logo-light.png\" height=\"50\"></img></a>\n\n[](https://github.com/altair-viz/altair/actions?query=workflow%3Abuild)\n[](https://www.mypy-lang.org)\n[](https://joss.theoj.org/papers/10.21105/joss.01057)\n[](https://pypi.org/project/altair)\n\n**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\nAPI is simple, friendly and consistent and built on top of the powerful\n[Vega-Lite](https://github.com/vega/vega-lite) JSON specification. This elegant\nsimplicity produces beautiful and effective visualizations with a minimal amount of code. \n\n*Vega-Altair was originally developed by [Jake Vanderplas](https://github.com/jakevdp) and [Brian\nGranger](https://github.com/ellisonbg) in close collaboration with the [UW\nInteractive Data Lab](https://idl.cs.washington.edu/).*\n*The Vega-Altair open source project is not affiliated with Altair Engineering, Inc.*\n\n## Documentation\n\nSee [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\nrun the notebooks directly in your browser by clicking on one of the following badges:\n\n[](https://beta.mybinder.org/v2/gh/altair-viz/altair_notebooks/master)\n[](https://colab.research.google.com/github/altair-viz/altair_notebooks/blob/master/notebooks/Index.ipynb)\n\n## Example\n\nHere is an example using Vega-Altair to quickly visualize and display a dataset with the native Vega-Lite renderer in the JupyterLab:\n\n```python\nimport altair as alt\n\n# load a simple dataset as a pandas DataFrame\nfrom vega_datasets import data\ncars = data.cars()\n\nalt.Chart(cars).mark_point().encode(\n x='Horsepower',\n y='Miles_per_Gallon',\n color='Origin',\n)\n```\n\n\n\nOne of the unique features of Vega-Altair, inherited from Vega-Lite, is a declarative grammar of not just visualization, but _interaction_. \nWith a few modifications to the example above we can create a linked histogram that is filtered based on a selection of the scatter plot.\n\n```python \nimport altair as alt\nfrom vega_datasets import data\n\nsource = data.cars()\n\nbrush = alt.selection_interval()\n\npoints = alt.Chart(source).mark_point().encode(\n x='Horsepower',\n y='Miles_per_Gallon',\n color=alt.condition(brush, 'Origin', alt.value('lightgray'))\n).add_params(\n brush\n)\n\nbars = alt.Chart(source).mark_bar().encode(\n y='Origin',\n color='Origin',\n x='count(Origin)'\n).transform_filter(\n brush\n)\n\npoints & bars\n```\n\n\n\n## Features\n\n* Carefully-designed, declarative Python API.\n* Auto-generated internal Python API that guarantees visualizations are type-checked and\n in full conformance with the [Vega-Lite](https://github.com/vega/vega-lite)\n specification.\n* Display visualizations in JupyterLab, Jupyter Notebook, Visual Studio Code, on GitHub and\n [nbviewer](https://nbviewer.jupyter.org/), and many more.\n* Export visualizations to various formats such as PNG/SVG images, stand-alone HTML pages and the\n[Online Vega-Lite Editor](https://vega.github.io/editor/#/).\n* Serialize visualizations as JSON files.\n\n## Installation\n\nVega-Altair can be installed with:\n```bash\npip install altair\n```\n\nIf you are using the conda package manager, the equivalent is:\n```bash\nconda install altair -c conda-forge\n```\n\nFor full installation instructions, please see [the documentation](https://altair-viz.github.io/getting_started/installation.html).\n\n## Getting Help\n\nIf you have a question that is not addressed in the documentation, \nyou can post it on [StackOverflow](https://stackoverflow.com/questions/tagged/altair) using the `altair` tag.\nFor bugs and feature requests, please open a [Github Issue](https://github.com/altair-viz/altair/issues).\n\n## Development\n\n[](https://github.com/pypa/hatch)\n[](https://github.com/astral-sh/ruff)\n[](https://github.com/pytest-dev/pytest)\n\nYou can find the instructions on how to install the package for development in [the documentation](https://altair-viz.github.io/getting_started/installation.html).\n\nTo run the tests and linters, use\n\n```\nhatch run test\n```\n\nFor information on how to contribute your developments back to the Vega-Altair repository, see\n[`CONTRIBUTING.md`](https://github.com/altair-viz/altair/blob/main/CONTRIBUTING.md)\n\n## Citing Vega-Altair\n\n[](https://joss.theoj.org/papers/10.21105/joss.01057)\n\nIf you use Vega-Altair in academic work, please consider citing https://joss.theoj.org/papers/10.21105/joss.01057 as\n\n```bib\n@article{VanderPlas2018,\n doi = {10.21105/joss.01057},\n url = {https://doi.org/10.21105/joss.01057},\n year = {2018},\n publisher = {The Open Journal},\n volume = {3},\n number = {32},\n pages = {1057},\n 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},\n title = {Altair: Interactive Statistical Visualizations for Python},\n journal = {Journal of Open Source Software}\n}\n```\nPlease 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\n\n```bib\n@article{Satyanarayan2017,\n author={Satyanarayan, Arvind and Moritz, Dominik and Wongsuphasawat, Kanit and Heer, Jeffrey},\n title={Vega-Lite: A Grammar of Interactive Graphics},\n journal={IEEE transactions on visualization and computer graphics},\n year={2017},\n volume={23},\n number={1},\n pages={341-350},\n publisher={IEEE}\n} \n```\n",
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