issues: 363629186

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
363629186 MDU6SXNzdWUzNjM2MjkxODY= 2438 Efficient workaround to group by multiple dimensions 5308236 closed 0     3 2018-09-25T15:11:38Z 2018-10-02T15:56:53Z 2018-10-02T15:56:53Z NONE      

Grouping by multiple dimensions is not yet supported (#324):

python d = DataAssembly([[1, 2, 3], [4, 5, 6]], coords={'a': ('multi_dim', ['a', 'b']), 'c': ('multi_dim', ['c', 'c']), 'b': ['x', 'y', 'z']}, dims=['multi_dim', 'b']) d.groupby(['a', 'b']) # TypeError: `group` must be an xarray.DataArray or the name of an xarray variable or dimension

An inefficient solution is to run the for loops manually:

```python a, b = np.unique(d['a'].values), np.unique(d['b'].values) result = xr.DataArray(np.zeros([len(a), len(b)]), coords={'a': a, 'b': b}, dims=['a', 'b']) for a, b in itertools.product(a, b): cells = d.sel(a=a, b=b) merge = cells.mean() result.loc[{'a': a, 'b': b}] = merge

result = DataArray (a: 2, b: 2)> array([[2., 3.], [5., 6.]])

Coordinates:

* a (a) <U1 'x' 'y'

* b (b) int64 0 1

```

This is however horribly slow for larger arrays. Is there a more efficient / straight-forward work-around?

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.7.0.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-17134-Microsoft machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: None LOCALE: en_US.UTF-8 xarray: 0.10.8 pandas: 0.23.4 numpy: 1.15.1 scipy: 1.1.0 netCDF4: 1.4.1 h5netcdf: None h5py: None Nio: None zarr: None bottleneck: None cyordereddict: None dask: None distributed: None matplotlib: 2.2.3 cartopy: None seaborn: None setuptools: 40.2.0 pip: 10.0.1 conda: None pytest: 3.7.4 IPython: 6.5.0 sphinx: None

Related: #324, https://stackoverflow.com/questions/52453426/grouping-by-multiple-dimensions

 
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