DataTree: missing methods

[mathause]

Is your feature request related to a problem?

There are still missing methods on DataTree and I did not find an issue tracking those.

• Did I miss that?
• Is there a reason they are missing?

---

e.g.:

• broadcast_like
• dropna
• transpose
• ...

(I am sure there are more but these are the ones I need.)

Describe the solution you'd like

No response

Describe alternatives you've considered

We could use map_over_datasets(xr.Dataset.transpose, dt) but (1) that's annoying, (2) obj.transpose() would work for DataTree and Dataset etc.

Additional context

No response

[mathause]

Here is a more complete list (code-generated):

• apply
• argmax
• argmin
• as_numpy
• assign_attrs
• assign_coords
• astype
• bfill
• broadcast_equals
• broadcast_like
• chunks
• clip
• coarsen
• combine_first
• convert_calendar
• cumulative
• cumulative_integrate
• curvefit
• diff
• differentiate
• drop
• drop_attrs
• drop_dims
• drop_duplicates
• drop_encoding
• drop_indexes
• drop_isel
• drop_sel
• drop_vars
• dropna
• dtypes
• dump_to_store
• eval
• expand_dims
• ffill
• fillna
• filter_by_attrs
• from_dataframe
• get_index
• groupby
• groupby_bins
• head
• idxmax
• idxmin
• imag
• info
• integrate
• interp
• interp_calendar
• interp_like
• interpolate_na
• isin
• isnull
• load_store
• loc
• map
• map_blocks
• merge
• notnull
• pad
• plot
• polyfit
• quantile
• query
• rank
• real
• reindex
• reindex_like
• rename
• rename_dims
• rename_vars
• reorder_levels
• resample
• reset_coords
• reset_encoding
• reset_index
• roll
• rolling
• rolling_exp
• set_coords
• set_index
• set_xindex
• shift
• sortby
• squeeze
• stack
• swap_dims
• tail
• thin
• to_array
• to_dask_dataframe
• to_dataarray
• to_dataframe
• to_pandas
• to_stacked_array
• transpose
• unify_chunks
• unstack
• weighted
• where

I think these are not available because they where defined directly on Dataset, while DataTree uses mixins to define reductions and binary ops...

import xarray as xr

dt_methods = set(m for m in dir(xr.DataTree) if not m.startswith("_"))
ds_methods = set(m for m in dir(xr.Dataset) if not m.startswith("_"))
for m in sorted(ds_methods - dt_methods):
    print(f"- [ ] {m}")