I feel the same way. I do ML research and every time I'm provided a Pandas dataframe, the first thing I do before I try to work with it (after getting over my v

I feel the same way. I do ML research and every time I'm provided a Pandas dataframe, the first thing I do before I try to work with it (after getting over my vague feeling of annoyance) is convert it to a format I understand -- e.g. a numpy array, a dict of numpy arrays, etc. I find the pandas API very unintuitive from an idiomatic Python perspective.* And it's not that I've never used SQL (I've written some small websites/apps that use it), I just haven't encountered a situation in my ML work where I've needed joins etc. to the point where I'd care to learn a whole new API for them. I gather it's very useful and extremely well loved for certain types of work though. But "the usability of Pandas" definitely doesn't ring true for me, let alone the implication that numpy is relatively unusable...if you've written lots of idiomatic Python working with its native data structures, NumPy will come very naturally, whereas Pandas is like a whole new language. * I have to call `at` instead of using Python indexing to get a row. wat. If I try to iterate over it like "for x in df", x is a column name rather than a row. wat.