Seconded. Xarray has mostly replaced bare NumPy for me and it makes me so much more productive.

Is there anything similar to this for something like Tensorflow, Keras or Pytorch? I haven't used them super recently, but in the past I needed to do all of the things you just described in painful to debug ways.

For Torch, I have come across Named Tensors, which should work in a similar way: https://docs.pytorch.org/docs/stable/named_tensor.html

The docs say that it's a prototype feature, and I think it has been that way for a few years now, so no idea how production-ready it is.

It's a much worse API than Xarrays, it seems like somebody should build it on top of PyTorch.

I really like einops. This works for numpy, pytorch and keras/tensorflow and has easy named transpose, repeat, and eimsum operations.

Same - I’ve been using einops and jaxtyping together pretty extensively recently and it helps a lot for reading/writing multidimensional array code. Also array_api_compat, the API coverage isn’t perfect but it’s pretty satisfying to write code that works for both PyTorch and numpy arrays

https://docs.kidger.site/jaxtyping/

https://data-apis.org/array-api-compat/

For pytorch the analogue is Named Tensors, but it's a provisional feature and not supported everywhere.

https://docs.pytorch.org/docs/stable/named_tensor.html

Thanks for sharing this library. I will give it a try.

For a while I had a feeling that I was perhaps a little crazy for seeming to be only person to really dislike the use of things like ‘array[:, :, None]’ and so forth.

along those lines, for biosignal processing, NeuroPype[0] also builds on numpy and implements named axes for n-dimensional tensors, with the ability to store per-element data (i.e. channel names, positions, etc.) for each axis

[0] https://www.neuropype.io/docs/

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/

xarray is nice

Life goes full circle sometimes. I remember that Numpy roughly came out of the amalgamation of the Numeric and Numarray libraries; I want to imagine that the Numarray people kept fighting these past 20 years to prove they were the right solution, at some point took some money from Elon Musk and renamed to Xarray [0], and finally started beating Numpy.

[0] most of the above is fiction