Dataframes in Python are a wrapper around 2D numpy arrays, that have labels and various accessors. Operations on them are OOM slower than using the underlying arrays.
If your data fits into numpy arrays or structured arrays (mainly if it is in numeric types), numpy is designed for this and will likely be much faster than pandas/polars (though I've also heard pandas can be faster on very large tables).
Pandas and Polars are designed for ease of use on heterogeneous data. They also include a python 'Object' data type which numpy very much does not. They are also designed more like database (e.g. 'join' operations). This allows you to work directly with imported data that numpy won't accept - after which Pandas uses numpy for underlying operations.
So I think the point is if you are running into speed issues in Pandas/Polars, you may find that the time-critical operations could be things that are more efficiently done in numpy (and this would be a much bigger gain than moving from Pandas to Polars)
It's in the documentation.
I just learned pandas recently, and I would have said this same thing. Not because I read through the numpy code, but because I read the documentation.
Is it wrong? Can't a user pick up a new tool and trust some documentation without reading through 100's of libraries built on libraries.
When was last time someone traced out every dependency, so they can confidently say something is "Myth".