Python Polars Cheatsheet (based on our O'Reilly book)

Is there an .MD version? For, uh, reasons :)

The bare R experience is not that great, to put it mildly, but it's a whole other story if you add tidyverse on top of it. The data work becomes really easy then, but I still prefer Python because of familiarity and a better experience & ecosystem when you want to do anything beyond data wrangling & analysis.

I found `polars` to be a better experience than `pandas` even though I'd say it leaks some "Rustisms" in its Python APIs. But LLMs alleviate those pains and it's easy enough to review. I'd say it's even easier when there's less of a chance of implicit behavior.

Data science is a broad church, but my own branch of it (molecular biology, genomics and epigenetics, bioinformatics) and my partner's (ecology) are still very much R-based and entrenched. If you primarily care about statistical modeling, then R still wins (easily).

If your work is more focused on statistics or pure modeling, then I agree R wins hands down. The issue is that most projects have "unclean" parts where you have to gather data from multiple sources, use connectors for services, S3 buckets and whatnot; dealing with that mess is where Python really shines.

AI probably changes the equation to some extent, but I still believe I'd rather maintain a complicated data pipeline like that in Python rather than R.