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/964#issuecomment-248938413,https://api.github.com/repos/pydata/xarray/issues/964,248938413,MDEyOklzc3VlQ29tbWVudDI0ODkzODQxMw==,1197350,2016-09-22T15:30:35Z,2016-09-22T15:30:35Z,MEMBER,"Of course I think this is a fantastic feature which will change the way use use xarray.

I gave it a test run for a problem we come across a lot on the mailing list: estimating a linear trend along one dimension of a dataarray. A short example notebook is here:
https://gist.github.com/rabernat/a0ec6a7e947f2d928615a30f5cb91ee9

Overall it worked as I hoped, but there were a few bumps I had to overcome. My feedback is from a user perspective, regarding the api and documentation
- In the documentation, I would _not_ assume that the user is familiar with Numpy generalized universal functions. A more explicit explanation of the syntax and meaning of the signature in the docstring would be very helpful. It took me lots of trial an error to find the signature that worked.
- The function I wanted to apply, `np.polyfit`, works on the _first_ axis of the array, not the last. This required an extra swap-axis step inside a wrapper function.
- This would not work if the data ndim were > 2, because `np.polyfit` expects a 2D array. So an additional stacking step would also be required.

Perhaps I am not using this as designed, but this was the most obvious example application I could think of.
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