[seasily]

The author understands little to nothing about programming. I'd bet on the law of the straight line over his/her uninformed take.

Python is slow, but PyTorch is fast, GBTs are fast, Cython is fast, Pandas and Numpy are fast (and even faster libraries or even basic joblib code can parallelize these).

Anything that needs to be fast either is or can be made fast--and most compute in data-intensive applications exists inside these optimized libraries anyway.

[yawaramin]

> Anything that needs to be fast either is or can be made fast

It can be made fast how? Typically by writing it in some other language. That's the point.