Altair is an open source declarative statistical visualization library for Python, based on Vega and Vega-Lite.
This means you can write Python but output Vega-Lite.
Vega is a visualization grammar (think Grammar of Graphics) that can be written as a JSON specification. Vega-lite provides most of the functionality of Vega more concisely, by relying on smart defaults and simpler encodings.
Features of Altair:
- Simple, friendly and consistent API.
- Built on top of the powerful Vega-Lite JSON specification.
- Compound plot types that can be used to create stacked, layered, faceted, and repeated charts.
- Layered charts – allows you to overlay two different charts on the same set of axes. They can be useful, for example, when you wish to draw multiple marks for the same data.
- Horizontal Concatenation – display two plots side-by-side.
- Vertical Concatenation – offers vertical concatenation via the vconcat() function or the & operator.
- Repeated Charts – provides a convenient interface for a particular type of horizontal or vertical concatenation, in which the only difference between the concatenated panels is modification of one or more encodings.
- Faceted Charts – provide a more convenient API for creating multiple views of a dataset for a specific type of chart: one where each panel contains a different subset of data.
- Compound Charts and Data Specification – specify data in multiple places.
- Renderers for JupyterLab, nteract, Jupyter Notebook, and Google Colab.
- Customize renderers by allowing users to define and enable new MIME types.
- Data Transformations:
- Before the chart definition, using standard Pandas data transformations.
- Within the chart definition, using Vega-Lite’s data transformation tools.
- Save charts in a variety of formats: PNG, SVG, JSON, and HTML.
- Support for JupyterLab/nteract through MIME based rendering.
Declarative means you only have to declare links between data columns to the encoding channels. All the plot details are handled automatically.
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