Project Philosophy#

Many excellent plotting libraries exist in Python, including:

Each library does a particular set of things well.

User Challenges#

However, such a proliferation of options creates great difficulty for users as they have to wade through all of these APIs to find which of them is the best for the task at hand. None of these libraries are optimized for high-level statistical visualization, so users have to assemble their own using a mishmash of APIs. For individuals just learning to work with data, this forces them to focus on learning APIs rather than exploring their data.

Another challenge is current plotting APIs require the user to write code, even for incidental details of a visualization. This results in an unfortunate and unnecessary cognitive burden as the visualization type (histogram, scatterplot, etc.) can often be inferred using basic information such as the columns of interest and the data types of those columns.

For example, if you are interested in the visualization of two numerical columns, a scatterplot is almost certainly a good starting point. If you add a categorical column to that, you probably want to encode that column using colors or facets. If inferring the visualization proves difficult at times, a simple user interface can construct a visualization without any coding. Tableau and the Interactive Data Lab’s Polestar and Voyager are excellent examples of such UIs.

Design Approach and Solution#

We believe that these challenges can be addressed without the creation of yet another visualization library that has a programmatic API and built-in rendering. Vega-Altair’s approach to building visualizations uses a layered design that leverages the full capabilities of existing visualization libraries:

  1. Create a constrained, simple Python API (Vega-Altair) that is purely declarative

  2. Use the API (Vega-Altair) to emit JSON output that follows the Vega-Lite spec

  3. Render that spec using existing visualization libraries

This approach enables users to perform exploratory visualizations with a much simpler API initially, pick an appropriate renderer for their usage case, and then leverage the full capabilities of that renderer for more advanced plot customization.

We realize that a declarative API will necessarily be limited compared to the full programmatic APIs of Matplotlib, Bokeh, etc. That is a deliberate design choice we feel is needed to simplify the user experience of exploratory visualization.

You can find a more detailed comparison between Plotly and Altair in this StackOverflow answer.

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