issue_comments: 273544152

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https://github.com/pydata/xarray/issues/1217#issuecomment-273544152 https://api.github.com/repos/pydata/xarray/issues/1217 273544152 MDEyOklzc3VlQ29tbWVudDI3MzU0NDE1Mg== 10050469 2017-01-18T17:34:13Z 2017-01-18T17:34:13Z MEMBER

. So in my case extracting the data with the shared dimension using ds.drop() is appropriate. It would be nice to have xarray throw a warning or error to prevent me chomping up all the RAM in my system if I do try to do this sort of thing though.

I'll let @shoyer give a definitive answer here, but I don't think that .where is meant to check whether the input makes "sense" or not. What happens is related to how xarray chooses to broadcast non matching dimensions:

```python import xarray as xr import numpy as np d1 = xr.DataArray(np.arange(3), coords={'t1': np.linspace(0, 1, 3)}, dims='t1') d2 = xr.DataArray(np.arange(4), coords={'t2': np.linspace(0, 1, 4)}, dims='t2')

d2 * d1 <xarray.DataArray (t2: 4, t1: 3)> array([[0, 0, 0], [0, 1, 2], [0, 2, 4], [0, 3, 6]]) Coordinates: * t2 (t2) float64 0.0 0.3333 0.6667 1.0 * t1 (t1) float64 0.0 0.5 1.0

d2.where(d1 == 1) <xarray.DataArray (t2: 4, t1: 3)> array([[ nan, 0., nan], [ nan, 1., nan], [ nan, 2., nan], [ nan, 3., nan]]) Coordinates: * t2 (t2) float64 0.0 0.3333 0.6667 1.0 * t1 (t1) float64 0.0 0.5 1.0 ``` which "makes sense", but is going to have a huge memory consumption if your arrays are large. 

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