I have never seen a worse documented library. Initially I thought that they were lazy, now I realize that it cannot be documented because it is a total mess of a library held together with tape.
Close second is the plotly library.
Close second is the plotly library.
(I also wrote a Pandas book or two... So there's that)
But for a lot of people who use it infrequently its documentation is a frustrating mess. Simple problems turn into significant time sinks of trying to find which page of the documentation to look at.
A lot of issues are made worse by shit-awful interop between libraries that claim to fully support dayaframes, but often fail in non-obvious ways... meaning back to the documentation mines.
I'd argue that because there's a market for a single author to write two books about it is indicative of documentation problems.
However, I always though the 10 minutes to Pandas page was decent for getting started. I picked up Polars recently and thought it was more difficult than Pandas because there wasn't any quick intro docs. What projects have great introductory docs for you?
Also, I am curious to learn more about the specifics of interop libraries you are referring to.
Learning a new tool is generally a challenge. I think another challenge with a lot of data tools is that non-programmers tend to be the major audience. I make my living teaching "non-programmers" how to use these tools.
That said, I always teach "go to the docstrings and stay in your environment (to not break flow) if you can." The pydata docstrings are better than most, including Python (the language).
However, maybe it makes more sense that it's just a mess that's hard to document.
The user guide material absolutely needs work, and the examples in the reference docs tend to be a little contrived. But I absolutely have seen worse-documented libraries, such as Gunicorn and Pydantic.