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While I use Python for ML myself, I find it weird to say that a language isn't slow because you don't really use it anyway. It's true that Scripts in Python can be fast if 99% of the executed logic is in C anyway but that doesn't mean the language isn't slow. As soon as your Python script needs to do anything not available in a library you'll notice how slow it really is.

Python is really neat to quickly experiment platform independent but it's definitely extremely slow.

As parent mentioned, python!=cpython. You've got pypy, cython, typed cython, nuitka, and probably some others I forgot about. Without knowing what you're trying to achieve and what you've tried, "extremely slow" is pretty hard to accept.

Cython is a subset of Python, while PyPy has shitty C interop if nothing changed from the last time that I checked. Ergo in a general sense Python = CPython. If you have specific needs you may want to consider the alternatives, but saying that Cython is Python is at least shady if not completely deceptive.

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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