issue_comments: 1397627848

I'm not an xarray developer, but my guess is that your argument is why positional indexing/slicing is not available for datasets.

As for the specific case of using axis parameter of expand_dims, I think this is useful for the case in which the user is either confident about the axis order in each DataArray or will use label based operations such that axis order doesn’t matter. I was curious so I did a quick comparison of the speed for using this parameter versus a subsequent transpose operation: shape = (10, 50, 100, 200) ds = xr.Dataset( { "foo": (["time", "x", "y", "z"], np.random.rand(*shape)), "bar": (["time", "x", "y", "z"], np.random.randint(0, 10, shape)), }, { "time": (["time"], np.arange(shape[0])), "x": (["x"], np.arange(shape[1])), "y": (["y"], np.arange(shape[2])), "z": (["z"], np.arange(shape[3])), }, ) %%timeit -r 4 ds1 = ds.expand_dims("sample", axis=1) 38.1 µs ± 76 ns per loop (mean ± std. dev. of 4 runs, 10,000 loops each) %%timeit -r 4 ds2 = ds.expand_dims("sample").transpose("time", "sample", "x", "y", "z") 172 µs ± 612 ns per loop (mean ± std. dev. of 4 runs, 10,000 loops each)