| Name | altair JSON |
| Version |
5.5.0
JSON |
| download |
| home_page | None |
| Summary | Vega-Altair: A declarative statistical visualization library for Python. |
| upload_time | 2024-11-23 23:39:58 |
| maintainer | None |
| docs_url | None |
| author | Vega-Altair Contributors |
| requires_python | >=3.9 |
| license | Copyright (c) 2015-2023, Vega-Altair Developers
All rights reserved.
Redistribution and use in source and binary forms, with or without
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* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of vega-altair nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
| keywords |
declarative
interactive
json
statistics
vega-lite
visualization
|
| VCS |
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# 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/vega/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.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
```
## 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
[](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
```bash
hatch test
```
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
[](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/vega/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.when(brush).then(\"Origin\").otherwise(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/vega/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```bash\nhatch test\n```\n\nFor information on how to contribute your developments back to the Vega-Altair repository, see\n[`CONTRIBUTING.md`](https://github.com/vega/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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