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It is a very natural feature, but it makes discovering what you can and can't do with a library really hard. Learning what is and isn't legal with math librarie

It is a very natural feature, but it makes discovering what you can and can't do with a library really hard. Learning what is and isn't legal with math libraries that use a lot of them can be really tricky. For example, numpy code is really easy to read, which is fantastic, but figuring out how you're intended to do things from the documentation alone is quite difficult.

constantcrying 3y ago
In my experience numpy has also been on of the worst numerics libraries to deal with. The main reason is that Python seems designed to be hostile to numerics. Loose typing, assumptive conversions, specific numeric types hard to access, tedious array notations, etc. all are bad preconditions for a language which sadly seems to have become the prototyping standard in that area. The moment you have a language actually designed for numerics all these things vanish. One of Julias core design aspects is multiple dispatch, including operator overloading and it works extremely well there. I also don't see the point for discoverability at all. The documentation will list the overloads and the non-overloaded calls are exactly as discoverable as the others.