[dahart]

The point here is not that it’s loops per se, the point is that the indexing is explicit. It seems like a big win to me. The article’s ~10 non-trivial examples all make the code easier to read, and more importantly, to understand exactly what the code is doing. It is true that some operations are difficult to reason about, that’s where explicit indexing really helps. The article resonates with me because I do want to learn numpy syntax, I’ve written hundreds of programs with nympy, spent countless hours doing battle with it, and I feel like I’m no better off now than someone who’s brand new to it. The indexing is constantly confounding, nothing ever just works. Anytime you see “None” and “axis=“ inside an operation, it’s a tell: bound to be difficult to comprehend. I’m always having to guess how to use some combination of reshape, dstack, hstack, transpose, and five other shape changers I’m forgetting, just to get something to work and it’s difficult to read and understand later. It feels like there is no debugging, only rewriting. I keep reading the manual for einsum over again and I’ve used it, but I can’t explain how, why, or when to use it, it seems like this thing you have to resort to because no other indexing seems to work. The ability to do straightforward explicit non-clever indexing as if you were writing loops seems like a pretty big step forward.

[collingreen]

I involuntarily whispered "reshape" to myself near the top of your comment. Numpy is a very different way for me to think and I have similar feelings to what you're describing.

[cl3misch]

I could never understand why people use dstack, hstack and the like. I think plain np.stack and specifying the axis explicitely is easier to write and to read.

For transposes, np.einsum can be easier to read as it let's you use (single character, admittedly) axis specifiers to "name" them.