R to Python convert here (numpy, pandas). Agreed about the general claim of clunkiness, but at least for statistical computing R still wins because of the richn

R to Python convert here (numpy, pandas). Agreed about the general claim of clunkiness, but at least for statistical computing R still wins because of the richness of the long tail of packages (representative example: https://cran.r-project.org/web/packages/poweRlaw/index.html https://cran.r-project.org/web/packages/poweRlaw/index.html). In my experience, Python equivalents are much less developed and documented, if extant. Many data scientists I know would disagree with this claim, but that's because they tend to stick with things supported by scipy, pymc3, statsmodels, and a few other common libraries. One solution I have found for small and medium data is to use rpy in Jupyter to let me keep most of my workflow in python, then shuttle stuff to R for exotic tests or to use key packages (ggplot, brms, lme4).