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That's not true. Sure, many hot path functions dealing with tensor calculations are done in numpy functions, but etl and args/results are python objects and functions. And most web development libs are pure python (flask, django, etc)

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For performance, hot paths are the only ones that matter.

Sure, but only a small subset of problems have a hot path. You can easily offload huge tensor operations to C. That's the best possible case. More usually the "hot path" is fairly evenly distributed through your entire codebase. If you offload the hot path to C you'll end up rewriting the whole thing in C.

> "hot path" is fairly evenly distributed

No, hot paths are seldom fairly evenly distributed, even on non-numeric applications. In most cases they will be in a small number of locations.

Not in my experience.

Yeah, this is a benchmark of recursion and tight loops doing integer math on array members. Nontrivial recursion is nonidiomatic in Python, and tight loops doing integer math on array members will probably be done via one of the many libraries that do one or more of optimizing, jitting, or move those to GPU (Numpy, Taichi, Numba, etc.)

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