Yeah, that's what I meant. I would like per column type-hinting so that data frames are type-checked along with the rest of our stuff and everything is explicit.
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.
For type-hinting on dataframe-like objects, people recommend pandera [0].
> 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.
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!