The only way to understand what's going on with DF code is to step it in a debugger. I know they can be much faster, but man you pay a maintainability price!
(Sure, it's easy to write obfuscated pandas, and it sometimes has version-specific bugs or deprecations which need to be hacked around in a way that compromises readability, and sometimes the API has active changes/namings that are non-trivial. But that's miles from "only way to understand is with a debugger". If you want to claim otherwise, post a counterexample on SO (or Codidact) and post the link here.)
I don't have anything I can show because the stuff I was working on was commercial and I don't code Pandas for fun at home ;)
The code I was maintaining / updating had long pipelines, had lots of folding, and would drift in and out of numpy quite a bit.
Part of the issue was my unfamiliarity with Pandas, for sure. But if I just picked a random function in the code, I would have no idea as to the shape of the data flowing in and out, without reading up and down the callstack to see what columns are in play.
Breakpoint and then look at the data, every time!
> The code I was maintaining / updating had long pipelines, had lots of folding, and would drift in and out of numpy quite a bit.
(Protein folding?)
Anyway yeah if your codebase is a large proprietary pipeline that thunks to and from pandas-numpy then now I understand you. But that's your very specific usecase. The claim "The only way to understand what's going on with DF code is to step it in a debugger" is in general overkill.
(Disclaimer: I wrote three of them and spend a good deal of my time helping others level up their Pandas. Spent this morning helping a medical AI company with Pandas.)