issue_comments: 1293902008

How about Indexes.from_pandas_multi_index() classmethod?

Yes that would make sense. However, it would be adding another pandas.MultiIndex special case while we'd like to remove them in Xarray. Maybe a more generic Indexes class method that could be reused by 3rd-party indexes too? E.g., via some kind of hook or entrypoint... The tricky thing is that arguments would probably differ much from one index type to another.

1. does indexes get merged with existing ._indexes?

Indexes are not merged together but the new / replaced coordinate variables must be compatible with the other variables of the dataset. Dataset.assign_indexes(indexes) is actually implemented like this:

python def assign_indexes(self, indexes: Indexes[Index]): ds_indexes = Dataset(indexes=indexes) return ( self # prepare drop-in index / coordinate replacement .drop_vars(indexes, errors="ignore") # ensure the new indexes / coordinates are compatible with the Dataset .merge( ds_indexes, compat="minimal", # probably not the right option? join="override", # fastest option? (no real effect because of `drop_vars`) combine_attrs="no_conflicts", ) )

1. Can we extract enough information from Index to have xr.merge(Indexes) -> Indexes work?

That is actually a good idea for https://github.com/pydata/xarray/pull/7214#issuecomment-1292089179! Not sure I would reuse xr.merge() for this as it would make the API messy, but why not an xr.merge_indexes() top-level function or an Indexes.merge() method?