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That python is slow is utterly irrelevant. You would never deploy an unoptimized Python codebase into production if you cared about performance. You would profile the code and optimize the hot paths with the appropriate technology, be it numba-jit, cython, numpy, cffi, or any of the other many ways you can easily optimize Python

I wouldn't but I've seen a lot of people who will. If you have code that runs, why spend extra money to optimise it? Hardware is cheap and the cloud allow to scale as much as you want (not my opinion obviously but I've heard that more than once and I'm not even directly involved in these kind of decisions).

Clearly if there is no incentive to spend money to optimize it then it is fast enough.

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