I don't like NumPy

TFA keeps repeating "you can't use loops", but aren't they, like, merely less performant? I understand that there are going to be people out there doing complex algorithms (perhaps part of an ML system) where that performance is crucial and you might as well not be using NumPy in the first place if you skip any opportunities to do things in The Clever NumPy Way. But say I'm just, like, processing a video frame by frame, by using TCNW on each frame and iterating over the time dimension; surely that won't matter?

Also: TIL you can apparently use multi-dimensional NumPy arrays as NumPy array indexers, and they don't just collapse into 1-dimensional iterables. I expected `A[:,i,j,:]` not to work, or to be the same as if `j` were just `(0, 1)`. But instead, it apparently causes transposition with the previous dimension... ?

Right, you can use loops. But then it goes much slower than a GPU permits.

My point is that plenty of people use NumPy for reasons that have nothing to do with a GPU.

The whole point of NumPy is to make things much, much faster than interpreted Python, whether you're GPU-accelerated or not.

Even code you write now, you may need to GPU accelerate later, as your simulations grow.

Falling back on loops is against the entire reason of using NumPy in the first place.