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  • shoyer · 3 ✖
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455188201 https://github.com/pydata/xarray/pull/2674#issuecomment-455188201 https://api.github.com/repos/pydata/xarray/issues/2674 MDEyOklzc3VlQ29tbWVudDQ1NTE4ODIwMQ== shoyer 1217238 2019-01-17T14:21:50Z 2019-01-17T14:21:50Z MEMBER

It might also be clearer to split this functionality into two methods rather than the single reduce() method, e.g., reduce() and transform() in the model of pandas's groupby methods.

It's probably worth thinking about these APIs more systematically, see https://github.com/pydata/xarray/issues/1618, https://github.com/pydata/xarray/issues/1251, https://github.com/pydata/xarray/issues/1130

 
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   Skipping variables in datasets that don't have the core dim 399164733
455179590 https://github.com/pydata/xarray/pull/2674#issuecomment-455179590 https://api.github.com/repos/pydata/xarray/issues/2674 MDEyOklzc3VlQ29tbWVudDQ1NTE3OTU5MA== shoyer 1217238 2019-01-17T13:54:33Z 2019-01-17T13:54:33Z MEMBER

Is there a conceptual overlap between the goals of .reduce and apply_ufunc? I had initially thought that .reduce strictly reduced a dimension, though that's not actually the case given cumsum

Yes, I think so. reduce() is a simpler but complementary model to apply_ufunc.

Maybe we should rename reduce() to something like apply_axis_func() or apply_over_dim? I would use the name apply_along_axis but the numpy function of the same name does something different.

 
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   Skipping variables in datasets that don't have the core dim 399164733
454322871 https://github.com/pydata/xarray/pull/2674#issuecomment-454322871 https://api.github.com/repos/pydata/xarray/issues/2674 MDEyOklzc3VlQ29tbWVudDQ1NDMyMjg3MQ== shoyer 1217238 2019-01-15T09:24:38Z 2019-01-15T09:24:38Z MEMBER

cc @fujiisoup

The challenge here is that this logic depends on the nature of the function. It only makes skip variables if applying the operation to a scalar returns the scalar. This is true for many but not all reductions, and there are lots of edge cases, e.g., - mean returns the same value, but integers should be cast to floats for consistency - sum returns the original element unless it's a scalar NaN (in which case it gets replaced by 0, unless skipna=False - count should always return 1, regardless of the original value - indexing does always return the same value

So this really needs to be opt-in only, and even then I'm not sure it's worth the trouble. It might be better to explicitly define functions even in the case of axis=() (the empty tuple), and either reuse the reduce() interface or make another (e.g., transform?) for functions that are oriented along a set of axes.

Actually, it isn't documented behavior but reduce() will already correctly handle this cases, too, e.g., for functions like cumsum() (this is used internally): ``` In [13]: ds = xarray.Dataset({'a': ('x', np.arange(3))})

In [14]: ds.reduce(np.cumsum) Out[14]: <xarray.Dataset> Dimensions: (x: 3) Dimensions without coordinates: x Data variables: a (x) int64 0 1 3 ```

 
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   Skipping variables in datasets that don't have the core dim 399164733

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