> R is perhaps the closest, because it has data.frame as a 'first class citizen', but most people don't seem to use it, and use e.g. tibbles from dplyr instead.

You're forgetting R's data.table, https://cran.r-project.org/web/packages/data.table/vignettes...,

which is amazing. Tibbles only wins because they fought the docs/onboarding battle better, and dplyr ended up getting industry buy-in.

And readability. data.table is very capable, but the incantations to use it are far less obvious (both for reading and writing) than dplyr.

But you can have the best of both worlds with https://dtplyr.tidyverse.org/, using data.table's performance improvements with dplyr syntax.

Yeah data.table is just about the best-in-class tool/package for true high-throughput "live" data analysis. Dplyr is great if you are learning the ropes, or want to write something that your colleagues with less experience can easily spot check. But in my experience if you chat with people working in the trenches of banks, lenders, insurance companies, who are running hundreds of hand-spun crosstabs/correlational analyses daily, you will find a lot of data.table users.

Relevant to the author's point, Python is pretty poor for this kind of thing. Pandas is a perf mess. Polars, duckdb, dask etc, are fine perhaps for production data pipelines but quite verbose and persnickety for rapid iteration. If you put a gun to my head and told me to find some nuggets of insight in some massive flat files, I would ask for an RStudio cloud instance + data.table hosted on a VM with 256GB+ of RAM.