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I cannot stress enough how important it is that we were able to do this work publicly. I've done large, ecosystem-wide evaluations of visualization libraries in the past, but these journeys were kept private. This audit is the first publicly available one of its kind done at this scale! Huge!

This is a large-scale, systematic application of my framework, Chartability (workbook linked below). But we hope that other Python and R can follow suit now that they see how it's done. Data science accessibility matters and progress over the next decade will be key. We hope to lead the way there.

Our summary document is the place to start, from there we organize and reference all of our evidence documents where we capture failures to pass Chartability test criteria. And if you're attending @pycon.us in 2 weeks, we will be sharing some of our work Friday May 16th! I hope to see you there.