html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/pull/2674#issuecomment-455273458,https://api.github.com/repos/pydata/xarray/issues/2674,455273458,MDEyOklzc3VlQ29tbWVudDQ1NTI3MzQ1OA==,5635139,2019-01-17T18:15:09Z,2019-01-17T18:15:09Z,MEMBER,"Is there a next step for a function which:
- Is a drop-in replacement for `.reduce`
- Similar to https://github.com/pydata/xarray/issues/1618, though maybe even simpler, i.e. only operates on a single input at first
- Wraps `apply_ufunc`
- Would restore dim order https://github.com/pydata/xarray/issues/1739
- Could be used by @fujiisoup 's recent functions, ref https://github.com/pydata/xarray/pull/2650#issuecomment-454164295 (though some of these may require multiple inputs, even though there's only one output?)

I'd be happy to give this a go if we agree on the broad design

For names, it's a shame the current `apply` isn't called `map`, and this could be `apply`. I'm not a great fan of `apply_raw`, since I'm can't yet see why it's any more 'raw'. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,399164733
https://github.com/pydata/xarray/pull/2674#issuecomment-455269655,https://api.github.com/repos/pydata/xarray/issues/2674,455269655,MDEyOklzc3VlQ29tbWVudDQ1NTI2OTY1NQ==,5635139,2019-01-17T18:03:04Z,2019-01-17T18:03:04Z,MEMBER,"> It's probably worth thinking about these APIs more systematically, see #1618, #1251, #1130

Nice, thanks for finding those

> 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.

Is the split needed? I had thought that the function either
- returned a reduced array, and the output dims should be reduced
- returned an array with the original dims, and then output dims shouldn't be reduced","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,399164733
https://github.com/pydata/xarray/pull/2674#issuecomment-455188201,https://api.github.com/repos/pydata/xarray/issues/2674,455188201,MDEyOklzc3VlQ29tbWVudDQ1NTE4ODIwMQ==,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","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,399164733
https://github.com/pydata/xarray/pull/2674#issuecomment-455179590,https://api.github.com/repos/pydata/xarray/issues/2674,455179590,MDEyOklzc3VlQ29tbWVudDQ1NTE3OTU5MA==,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.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,399164733
https://github.com/pydata/xarray/pull/2674#issuecomment-454387095,https://api.github.com/repos/pydata/xarray/issues/2674,454387095,MDEyOklzc3VlQ29tbWVudDQ1NDM4NzA5NQ==,5635139,2019-01-15T13:12:00Z,2019-01-15T13:12:00Z,MEMBER,"Thanks a lot for the comments @shoyer , that's v clarifying 

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`","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,399164733
https://github.com/pydata/xarray/pull/2674#issuecomment-454322871,https://api.github.com/repos/pydata/xarray/issues/2674,454322871,MDEyOklzc3VlQ29tbWVudDQ1NDMyMjg3MQ==,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
```","{""total_count"": 2, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 1, ""rocket"": 0, ""eyes"": 0}",,399164733