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/issues/652#issuecomment-155470414,https://api.github.com/repos/pydata/xarray/issues/652,155470414,MDEyOklzc3VlQ29tbWVudDE1NTQ3MDQxNA==,5635139,2015-11-10T16:18:24Z,2015-11-10T16:18:24Z,MEMBER,"Wonderful, thanks @shoyer. 

FWIW, `reduce` [doesn't seem well documented](http://xray.readthedocs.org/en/stable/search.html?q=reduce&check_keywords=yes&area=default). It also looks very similar to `apply` [from pandas](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html), albeit with the requirement (rather than the option) to lose a dimension. 

I also found `.groupby('x').apply(func)`, which I think is broadly equivalent to a pandas `.apply(func, axis=0)`.

Closing for now, cheers.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,115979105
https://github.com/pydata/xarray/issues/652#issuecomment-155293177,https://api.github.com/repos/pydata/xarray/issues/652,155293177,MDEyOklzc3VlQ29tbWVudDE1NTI5MzE3Nw==,1217238,2015-11-10T05:08:05Z,2015-11-10T05:08:05Z,MEMBER,"Yes, use the `reduce` method -- which should probably be called something more appropriately generic such as `aggregate` given that it doesn't only do reduces, e.g.,

```
In [5]: data_array = xray.DataArray(np.random.randn(3, 4, 5), dims=['x', 'y', 'z'])

In [14]: data_array.reduce(np.nanpercentile, q=95, dim=['x', 'y'])
Out[14]:
<xray.DataArray (z: 5)>
array([ 1.79306097,  0.9788271 ,  0.9385694 ,  1.30198262,  1.78693993])
Coordinates:
  * z        (z) int64 0 1 2 3 4
```

To apply an operation along an axis that doesn't do an aggregation, you can use the `get_axis_num`, e.g.,

```
In [27]: xray.DataArray(np.argsort(data_array, axis=data_array.get_axis_num('x')), data_array.coords)
Out[27]:
<xray.DataArray (x: 3, y: 4, z: 5)>
array([[[2, 1, 1, 2, 0],
        [0, 2, 2, 1, 0],
        [1, 1, 2, 1, 0],
        [2, 0, 0, 0, 0]],

       [[1, 2, 2, 0, 2],
        [2, 1, 1, 0, 1],
        [2, 0, 1, 0, 2],
        [1, 1, 2, 2, 1]],

       [[0, 0, 0, 1, 1],
        [1, 0, 0, 2, 2],
        [0, 2, 0, 2, 1],
        [0, 2, 1, 1, 2]]])
Coordinates:
  * x        (x) int64 0 1 2
  * y        (y) int64 0 1 2 3
  * z        (z) int64 0 1 2 3 4
```

We don't currently have a convenience method for the later but that might be good idea. I'm not entirely sure if it should be called `DataArray.apply`, but it could reuse `maybe_wrap_array` [from `Dataset.apply`](https://github.com/xray/xray/blob/dce268f6db091fd1d44fb5a6caa084a9cb517ac2/xray/core/dataset.py#L1781) and add auto-conversion from `dim` to `axis` arguments
","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,115979105