issue_comments: 366884882

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https://github.com/pydata/xarray/issues/1914#issuecomment-366884882 https://api.github.com/repos/pydata/xarray/issues/1914 366884882 MDEyOklzc3VlQ29tbWVudDM2Njg4NDg4Mg== 1217238 2018-02-20T07:02:37Z 2018-02-20T07:02:37Z MEMBER

xarray.broadcast() could also be helpful for generating a cartesian product. Something like xarray.broadcast(*data.coords.values()) would get you three 3D DataArray objects.

apply_ufunc with vectorize=True could also achieve what you're looking for here: ```python import xarray as xr import numpy as np

data = xr.Dataset(coords={'x': np.linspace(-1, 1), 'y': np.linspace(0, 10), 'a': 1, 'b': 5})

def some_function(x, y): return float(x) * float(y)

xr.apply_ufunc(some_function, data['x'], data['y'], vectorize=True) Results in: <xarray.DataArray (x: 50, y: 50)> array([[ -0. , -0.204082, -0.408163, ..., -9.591837, -9.795918, -10. ], [ -0. , -0.195752, -0.391504, ..., -9.200333, -9.396085, -9.591837], [ -0. , -0.187422, -0.374844, ..., -8.80883 , -8.996252, -9.183673], ..., [ 0. , 0.187422, 0.374844, ..., 8.80883 , 8.996252, 9.183673], [ 0. , 0.195752, 0.391504, ..., 9.200333, 9.396085, 9.591837], [ 0. , 0.204082, 0.408163, ..., 9.591837, 9.795918, 10. ]]) Coordinates: * x (x) float64 -1.0 -0.9592 -0.9184 -0.8776 -0.8367 -0.7959 ... a int64 1 b int64 5 * y (y) float64 0.0 0.2041 0.4082 0.6122 0.8163 1.02 1.224 1.429 ... ```

You can even do this with dask arrays if you set dask='parallelized'.

That said, it does feel like there's some missing functionality here for the xarray equivalent of ndenumerate. I'm not entirely sure what the right API is, yet.

 
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