I'm not GP, but I find the pandas API incredibly inconsistent and difficult to remember how to do simple transformations. For example, it sometimes overloads operators because it doesn't use built in language features like lambdas. There are reasons for the inconsistency, but using the alternatives like R's tidyverse or Julia's DataFramess.jl is like night and day for me.

I found RedFrames [1] recently which wraps Pandas dataframes with a more consistent interface, it's probably what I'd use if I had to write data transformations that had to be compatible with Pandas.

[1] https://github.com/maxhumber/redframes

Pandas gets the job done, and is overall easy to use and intuitive.

The problem is that it's a huge pile of hacks, exceptions, anti patterns, and regressions.

The API is inconsistent, loose, full of obscure options added as quickfixes.

It really can't be said enough how pandas is a mess. It has way too much surface area and no common thread pulling it all together. This gets obvious when you work with better dataframe libs like dplyr [1] or DataFramesMeta [2]. I've worked on production systems with all of these libs, this is not gratuitous bashing.

[1] https://dplyr.tidyverse.org/ [2] https://juliadata.github.io/DataFramesMeta.jl/stable/