Mathias Hauser

But see this comment in the [pandas release notes](https://pandas.pydata.org/pandas-docs/version/2.2.3/whatsnew/v2.2.2.html#pandas-2-2-2-is-now-compatible-with-numpy-2-0): > One major caveat is that arrays created with numpy 2.0’s new `StringDtype` will convert to `object` dtyped arrays upon [Series](https://pandas.pydata.org/pandas-docs/version/2.2.3/reference/api/pandas.Series.html#pandas.Series)/[DataFrame](https://pandas.pydata.org/pandas-docs/version/2.2.3/reference/api/pandas.DataFrame.html#pandas.DataFrame)...

True - I need to update my docs now to showcase the `FlagGrouper` 🙂 . The only advantage I see here is that `StringDtype` accepts a `nan` sentinel while `int`...

@TomNicholas sorry - another one

Thanks for the review. I don't have the capacity to implement this, so I close this PR. (I think my scalars were actually coords, so there it was fine.)

or make them private - as some of them are

Thanks for the clear error report. On master you should be able to do `ds2.to_zarr("test2.zarr", consolidated=True, mode="w", safe_chunks=False)` - see #5065

rtd updates _environment.yml_ and _docs/conf.py_ which makes the git index dirty. Possible workaround: https://github.com/readthedocs/readthedocs.org/issues/8201#issuecomment-1088728000

Currently we can access it as ```python import regionmask countries_50 = regionmask.defined_regions.natural_earth_v5_1_2.countries_50 df = regionmask.defined_regions._natural_earth._countries_50.read("v5.1.2") ``` but have to ensure the version and resolution match. Also some of the regions...