Python Data Visualization 2018: Why So Many Libraries?

Missed one! Spotify just released their own dataviz library yesterday https://labs.spotify.com/2018/11/15/introducing-chartify-eas...

I'm pretty good at Python and do most of my work in it nowadays, but if I need to make a data visualization, I still go to R just for ggplot2. Nothing currently in Python compares (not even the "ggplot2 port"), and it takes an order of magnitude longer to make a comparable viz.

Also missed my Veusz GUI plotting package and library. https://veusz.github.io/

I find Pandas+Bokeh to be a pretty killer combination. You get nice vectorized operations for manipulating the data upfront, and a pretty wide range of visualizations that are plenty customizable. Main beefs with bokeh are:

- It's great that I can manipulate a view by zooming, panning, turning a series off, whatever, but all that state is lost when I send the URL of the plot to someone else (eg, for the case where it was generated by a CI job or something rather than locally on my machine). It should be easier in Bokeh to grab all that state and stuff it into a querystring that can then rehydrate the same view later on.

- Doing plots with more than one y-axis is a lot more awkward and fiddly than it should be, and even once you succeed, the axes can't be panned independently.

- If your data series name overlaps with the label in your legend, the whole plot breaks in an extremely non-obvious way (basically, Bokeh thinks that you want a legend entry per row in the named data series). Even being fully prepared for it, I lose time to this every once in a while, and it's almost a rite of passage that every junior dev hits it and burns an hour or two trying to figure out what is wrong.

I wonder if Plotly will ever take privacy and security seriously... I've had this ticket open for years. https://github.com/plotly/plotly.js/issues/316