Last January I left my job to spend a year developing siuba, a python port of dplyr. At its core, this decision was driven by a decade of watching python and R users produce similar analyses, but in very different ways.
In this talk, I’ll discuss 3 ways siuba enables R users to transfer their hard-earned programming knowledge to python: (1) leveraging the power of dplyr syntax, (2) options to generate SQL code, and (3) working with the plotnine plotting library.
Looking back, I’ll consider two critical pieces that have helped me develop siuba: using it to livecode TidyTuesday analyses, and building an interactive tutorial for absolute beginners.
Michael is a data science tool builder at Posit, where he works on open source tools for data analysis. He received a Ph.D. in Cognitive Psychology from Princeton University, and is interested in what drives expert data science performance. When not wrangling data, you can find him in Philly writing tiny poems, baking bread, and embroidering.
