A Tour of Python’s Data Visualization Landscape, Including Ggplot and Altair

Why Even Try, Man?
I recently came upon Brian Granger and Jake VanderPlas’s Altair, a promising young visualization library. Altair seems well-suited to addressing Python’s ggplot envy, and its tie-in with JavaScript’s Vega-Lite grammar means that as the latter develops new functionality (e.g., tooltips and zooming), Altair benefits — seemingly for free!
Indeed, I was so impressed by Altair that the original thesis of my post was going to be: “Yo, use Altair.”
But then I began ruminating on my own Pythonic visualization habits, and — in a painful moment of self-reflection — realized I’m all over the place: I use a hodgepodge of tools and disjointed techniques depending on the task at hand (usually whichever library I first used to accomplish that task1).
This is no good. As the old saying goes: “The unexamined plot is not worth exporting to a PNG.” 
Thus, I’m using my discovery of Altair as an opportunity to step back —


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