Altair provides a Python API for building statistical visualizations in a declarative manner. By statistical visualization we mean:

• The data source is a DataFrame that consists of columns of different data types (quantitative, ordinal, nominal and date/time).
• The DataFrame is in a tidy format where the rows correspond to samples and the columns correspond the observed variables.
• The data is mapped to the visual properties (position, color, size, shape, faceting, etc.) using the group-by operation of Pandas and SQL.

The Altair API contains no actual visualization rendering code but instead emits JSON data structures following the Vega-Lite specification. For convenience, Altair can optionally use ipyvega to display client-side renderings seamlessly in the Jupyter notebook.

Altair has the following features:

• Carefully-designed, declarative Python API based on traitlets.
• Auto-generated internal Python API that guarantees visualizations are type-checked and in full conformance with the Vega-Lite specification.
• Auto-generate Altair Python code from a Vega-Lite JSON spec.
• Display visualizations in the live Jupyter Notebook, on GitHub and nbviewer.
• Export visualizations to PNG images, stand-alone HTML pages and the Online Vega-Lite Editor.
• Serialize visualizations as JSON files.
• Explore Altair with 40 example datasets and over 70 examples.

Altair is developed by Brian Granger and Jake Vanderplas in close collaboration with the UW Interactive Data Lab.