I don't like NumPy | slacker news

If your arrays have more than two dimensions, please consider using Xarray [1], which adds dimension naming to NumPy arrays. Broadcasting and alignment then becomes automatic without needing to transpose, add dummy axes, or anything like that. I believe that alone solves most of the complaints in the article.

Compared to NumPy, Xarray is a little thin in certain areas like linear algebra, but since it's very easy to drop back to NumPy from Xarray, what I've done in the past is add little helper functions for any specific NumPy stuff I need that isn't already included, so I only need to understand the NumPy version of the API well enough one time to write that helper function and its tests. (To be clear, though, the majority of NumPy ufuncs are supported out of the box.)

I'll finish by saying, to contrast with the author, I don't dislike NumPy, but I do find its API and data model to be insufficient for truly multidimensional data. For me three dimensions is the threshold where using Xarray pays off.

[1] https://xarray.dev

Xarray is great. It marries the best of Pandas with Numpy.

Indexing like `da.sel(x=some_x).isel(t=-1).mean(["y", "z"])` makes code so easy to write and understand.

Broadcasting is never ambiguous because dimension names are respected.

It's very good for geospatial data, allowing you to work in multiple CRSs with the same underlying data.

We also use it a lot for Bayesian modeling via Arviz [1], since it makes the extra dimensions you get from sampling your posterior easy to handle.

Finally, you can wrap many arrays into datasets, with common coordinates shared across the arrays. This allows you to select `ds.isel(t=-1)` across every array that has a time dimension.

[1] https://www.arviz.org/en/latest/