[hansvm]

It gets more comfortable over time, but I remember feeling that way for the first year or three. My wishlist now is for most of numpy to just be a really great einsum implementation, along with a few analogous operations for the rest of the map-reduces numpy accelerates.

I've been writing my own low-level numeric routines lately, so I'm not up-to-date on the latest news, but there have been a few ideas floating around over the last few years about naming your axes and defining operations in terms of those names [0,1,2,3]. That sort of thing looks promising to me, and one of those projects might be a better conceptual fit for you.

[0] https://nlp.seas.harvard.edu/NamedTensor

[1] https://pypi.org/project/named-arrays/

[2] https://pytorch.org/docs/stable/named_tensor.html

[3] https://docs.xarray.dev/en/stable/

[mgunyho]

I want to second the idea of broadcasting based on named axes/dimensions. I think it's a logical next step in the evolution of the array programming paradigm.

I particularly recommend checking out xarray. It has made my numpy-ish code like 90% shorter and it makes it trivial to juggle six+ dimensional arrays. If your data is on a grid (not shaped like a table/dataframe), I see no downsides to using xarray instead of bare numpy.