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/815#issuecomment-204561434,https://api.github.com/repos/pydata/xarray/issues/815,204561434,MDEyOklzc3VlQ29tbWVudDIwNDU2MTQzNA==,5635139,2016-04-01T20:46:56Z,2016-04-01T20:47:21Z,MEMBER,"FWIW I'm not a fan of the `sel_where` name, `sel` is otherwise associated with labels, and this takes bools.
Have you thought about including this in `where`, with a kwarg such as `drop`?
","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,145243134
https://github.com/pydata/xarray/pull/815#issuecomment-204561404,https://api.github.com/repos/pydata/xarray/issues/815,204561404,MDEyOklzc3VlQ29tbWVudDIwNDU2MTQwNA==,5635139,2016-04-01T20:46:49Z,2016-04-01T20:46:49Z,MEMBER,"Right, I see. I think it's equivalent only to a pandas slice on `Series`, rather than a `DataFrame`.
``` python
In [71]: series = pd.Series(range(10))
In [72]: series[series>5]
Out[72]:
6 6
7 7
8 8
9 9
dtype: int64
# ...same as sel_where
In [78]: df =pd.DataFrame(pd.np.arange(40).reshape(4,10))
In [79]: df[(df>11) & (df < 28)]
Out[79]:
0 1 2 3 4 5 6 7 8 9
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 NaN NaN 12 13 14 15 16 17 18 19
2 20 21 22 23 24 25 26 27 NaN NaN
3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
# ...same as where
```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,145243134
https://github.com/pydata/xarray/pull/815#issuecomment-204525759,https://api.github.com/repos/pydata/xarray/issues/815,204525759,MDEyOklzc3VlQ29tbWVudDIwNDUyNTc1OQ==,5635139,2016-04-01T19:02:55Z,2016-04-01T19:02:55Z,MEMBER,"Forgive me if this is naive - is this equivalent to a pandas slice?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,145243134