<commit_before># Altair

[![build status](http://img.shields.io/travis/altair-viz/altair/master.svg?style=flat)](https://travis-ci.org/altair-viz/altair)

<a href="https://altair-viz.github.io"><img src="https://altair-viz.github.io/_static/altair-logo-light.png" align="left" hspace="40" vspace="6" alt="Altair logo"></a>

**Altair** is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning. 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. *Altair is developed by [<NAME>](https://github.com/jakevdp) and [Brian
Granger](https://github.com/ellisonbg) in close collaboration with the [UW
Interactive Data Lab](http://idl.cs.washington.edu/).*

## Altair Documentation

**Note: Altair's documentation is currently in a very incomplete form; we are
in the process of creating more comprehensive documentation. Stay tuned!**

See [Altair's Documentation Site](http://altair-viz.github.io),
as well as Altair's [Tutorial Notebooks](http://github.com/altair-viz/altair_notebooks).

## Example

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

```python
import altair as alt

# to use with Jupyter notebook (not JupyterLab) run the following
# alt.renderers.enable('notebook')

# load a simple dataset as a pandas DataFrame
from vega_datasets import data<commit_msg>Width<commit_after># Altair

[![build status](http://img.shields.io/travis/altair-viz/altair/master.svg?style=flat)](https://travis-ci.org/altair-viz/altair)

<a href="https://altair-viz.github.io"><img src="https://altair-viz.github.io/_static/altair-logo-light.png" align="left" hspace="40" vspace="6" alt="Altair logo" width="150px"></a>

**Altair** is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning. 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. *Altair is developed by [<NAME>](https://github.com/jakevdp) and [<NAME>](https://github.com/ellisonbg) in close collaboration with the [UW
Interactive Data Lab](http://idl.cs.washington.edu/).*

## Altair Documentation

**Note: Altair's documentation is currently in a very incomplete form; we are
in the process of creating more comprehensive documentation. Stay tuned!**

See [Altair's Documentation Site](http://altair-viz.github.io),
as well as Altair's [Tutorial Notebooks](http://github.com/altair-viz/altair_notebooks).

## Example

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

```python
import altair as alt

# to use with Jupyter notebook (not JupyterLab) run the following
# alt.renderers.enable('notebook')

# load a simple dataset as a pandas DataFrame
from vega_datasets import data
{'fraction_non_alphanumeric': 0.11650783891792192, 'fraction_numerical': 0.0027666769136181985, 'mean_word_length': 5.34307992202729, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, 'https://': 12, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2508485', 'n_tokens_mistral': 1020, 'n_tokens_neox': 1005, 'n_words': 331}