Thanks @headtr1ck !

Yes great, let's merge this.

And then open another issue on how to procede with rename in light of the index refactor? Great!

Then two main options I see for synthesizing the vars issues: - rename operates on DataArray as described in https://github.com/pydata/xarray/pull/6665#issuecomment-1150810485. Generally I'm less keen on "different types have different semantics", and here a positional arg would mean a DataArray rename, and kwarg would mean var rename. But it does work locally to DataArray quite well. - rename only exists on DataArrays for the name of the DataArray, and we use rename_vars & rename_dims for both DataArrays & Datasets. So Dataset.rename is soft-deprecated.

Would this also rename the coord associated with the dim?

To decide that we probably need to figure out what the role of a dimension will be after the index refactor. Before the refactor, we needed to rename the dimension coordinate along with the dimension to keep the index, but now that indexes are independent of dimensions it might be fine not to do that.

@keewis v interesting suggestion — the object name as positional and vars as kwargs.

As I think was mentioned above, one choice is whether a kwarg with the name of the object renames the object. I would worry it's slightly ambiguous. And potentially not additive. WDYT?

a rename_dims method

This could be good. Would this also rename the coord associated with the dim?

We do want it to be fluent though, so it can be directly on the DataArray, otherwise you can't do:

I was referring to the concept rather than the method. The idea is that you can refer to the DataArray by positional or by name, and the method takes either a single string or a dict mapping old names to new names, also with a kwargs version (the merging happens with either_dict_or_kwargs). That would mean these all do the same thing: ```python a = xr.DataArray([0, 1, 2], dims="x") a.rename("new") a.rename({None: "new"}) # None is the current name

b = xr.DataArray([0, 1, 2], dims="x", name="b") b.rename("new") b.rename(b="new") # b is the current name

with coords

c = xr.DataArray([0, 1, 2], dims="x", coords={"x": ["a", "b", "c"]}, name="c") c.rename("new", x="y") c.rename(c="new", x="y") c.rename({"c": "new", "x": "y"}) # providing both the dict and the kwargs raises ```

in pint-xarray we have the .pint.to method which uses a very similar concept, maybe it is worth copying that for rename?

We do want it to be fluent though, so it can be directly on the DataArray, otherwise you can't do:

python ( da .rename(...) .sum(...) )

in pint-xarray we have the .pint.to method which uses a very similar concept, maybe it is worth copying that for rename?

This seems like a nice improvement given the existing state.

I've found the effort to specialize methods (e.g. drop_var vs. drop_sel) has been good, and we might want to extend this principle. With the proposed code: - renaming a coord on a DataArray is rename - renaming a coord on a Dataset is rename_vars

...which is not ideal, though might be the best tradeoff.

WDYT @headtr1ck ? Or any other thoughts from others?