Best way to perform DataArray.mean while retaining coords defined in the dimension being averaged

[spencerahill]

DataArray.mean applied along a particular dimension causes coordinates that are defined in that dimension to be dropped:

In [39]: x, y = range(2), range(3)
In [40]: arr = xr.DataArray(np.random.random((2,3)), dims=['x', 'y'], coords=dict(x=x, y=y))
In [41]: coord = xr.DataArray(np.random.random((2,3)), dims=['x', 'y'], coords=dict(x=x, y=y))
In [42]: arr = arr.assign_coords(z=coord)
In [43]: arr
Out[43]:
<xarray.DataArray (x: 2, y: 3)>
array([[ 0.132368,  0.746242,  0.48783 ],
       [ 0.12747 ,  0.751283,  0.033713]])
Coordinates:
  * y        (y) int64 0 1 2
  * x        (x) int64 0 1
    z        (x, y) float64 0.993 0.1031 0.1808 0.2769 0.7237 0.2891

In [44]: arr.mean('x')
Out[44]:
<xarray.DataArray (y: 3)>
array([ 0.129919,  0.748763,  0.260772])
Coordinates:
  * y        (y) int64 0 1 2

We have a use case where we'd like to preserve the coordinates. @spencerkclark came up with the following workaround, which entails converting to a dataset, promoting the coord to a variable, performing the mean, and demoting the original coord back from a variable to a coord:

def coord_preserving_mean(arr, *args, **kwargs):
    name = arr.name
    ds = arr.reset_coords()
    names = set(arr.coords) - set(arr.dims)
    ds = ds.mean(*args, **kwargs)
    return ds.set_coords(names)[name]

This works fine, but I just feel like maybe there's an easier way to do this that we're missing. Any ideas? Thanks in advance.

xref spencerahill/aospy#194

[shoyer]

What exactly does the desired array look like in your example?

[spencerahill]

No longer needed; closing