I remember a panel once at a PyCon where we were discussing, I think, the anaconda distribution in the context of packaging and a respected data scientist (whose talks have always been hugely popular) made the point that he doesn't like Pandas because it's not excel. The latter was his go to tool for most of his exploratory work. If the data were too big, he'd sample it and things like that but his work finally was in Excel.

Quick Python/bash to cleanup data is fine too I suppose and with LLMs, it's easier than ever to write the quick throwaway script.

I took a bio statistic class. The tools were Excel/ R or Stata.

I think most people used R. Free and great graphing. Though the interactivity of Excel is great for what ifs. I never got R till I took that class. Though RStudio makes R seem like scriptable excel.

R/Python are fast enough for most things though a lot of genomic stuff (Blast alignments etc..) are in compiled languages.

Whenever I had to use anaconda it was slow as molasses. Was that ever fixed?