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/1837#issuecomment-369438064,https://api.github.com/repos/pydata/xarray/issues/1837,369438064,MDEyOklzc3VlQ29tbWVudDM2OTQzODA2NA==,6815844,2018-03-01T00:59:37Z,2018-03-01T00:59:37Z,MEMBER,"@shyer, do you have further suggestions?
I think this is almost ready.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289556132
https://github.com/pydata/xarray/pull/1837#issuecomment-366572887,https://api.github.com/repos/pydata/xarray/issues/1837,366572887,MDEyOklzc3VlQ29tbWVudDM2NjU3Mjg4Nw==,6815844,2018-02-19T02:14:35Z,2018-02-19T02:14:35Z,MEMBER,"@shoyer , thanks for the detailed review.
I noticed the benchmark test is still failing.
After fixing this, I will merge this.
Thanks :)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289556132
https://github.com/pydata/xarray/pull/1837#issuecomment-366488673,https://api.github.com/repos/pydata/xarray/issues/1837,366488673,MDEyOklzc3VlQ29tbWVudDM2NjQ4ODY3Mw==,6815844,2018-02-18T02:56:49Z,2018-02-18T02:56:49Z,MEMBER,"@shoyer , thanks for the review.
> These names are very descriptive, but unfortunately they are different for DataArray and Dataset, which means that users will need to write different code for different xarray types.
Agreed. Changed `to_dataarray / to_dataset` -> `construct`.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289556132
https://github.com/pydata/xarray/pull/1837#issuecomment-364825407,https://api.github.com/repos/pydata/xarray/issues/1837,364825407,MDEyOklzc3VlQ29tbWVudDM2NDgyNTQwNw==,6815844,2018-02-12T04:18:32Z,2018-02-12T04:18:32Z,MEMBER,"> What's the current status here vs #1883? Do you still want to merge that one first?
Yes.
It looks I reverted to a wrong commit.
I will overwrite most modifications in `duck_array_ops` and `test_duck_array_ops` after #1883 is merged..","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289556132
https://github.com/pydata/xarray/pull/1837#issuecomment-363621372,https://api.github.com/repos/pydata/xarray/issues/1837,363621372,MDEyOklzc3VlQ29tbWVudDM2MzYyMTM3Mg==,6815844,2018-02-07T01:11:12Z,2018-02-07T01:11:12Z,MEMBER,"I sent a separate PR (#1883) for nan-reduce methods.
I will continue / finish up this PR after #1883 is merged.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289556132
https://github.com/pydata/xarray/pull/1837#issuecomment-361562466,https://api.github.com/repos/pydata/xarray/issues/1837,361562466,MDEyOklzc3VlQ29tbWVudDM2MTU2MjQ2Ng==,6815844,2018-01-30T11:16:34Z,2018-01-30T11:16:34Z,MEMBER,"Hmm...
The support of `nansum` for object-dtype is much more difficult than I expected.
Now, I am stacked in supporting numpy<1.13.
With numpy<1.13, `nansum` for the object type gives just a `nan`.
`nansum` scripts defined in numpy==1.13 does not help because `!=` operator used [here](https://github.com/numpy/numpy/blob/6914bb41f0fb3c1ba500bae4e7d671da9536786f/numpy/lib/nanfunctions.py#L68)
behaves differently in numpy<1.13.
I think our best choice is to revert this PR to 4189d71 and left this issue to #1866.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289556132
https://github.com/pydata/xarray/pull/1837#issuecomment-359960625,https://api.github.com/repos/pydata/xarray/issues/1837,359960625,MDEyOklzc3VlQ29tbWVudDM1OTk2MDYyNQ==,6815844,2018-01-23T22:58:46Z,2018-01-23T22:58:46Z,MEMBER,"Although nan-reduce methods (such as `nanmean`) might be sped up by replacing nan by a certain number before constructing a rolling window, this improvement brings another complexity into the code (and bottleneck already does this).
I want to finish this PR first and left such improvements to another chance.
Please let me know if anyone has comments of this PR.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289556132
https://github.com/pydata/xarray/pull/1837#issuecomment-359252185,https://api.github.com/repos/pydata/xarray/issues/1837,359252185,MDEyOklzc3VlQ29tbWVudDM1OTI1MjE4NQ==,6815844,2018-01-21T14:25:25Z,2018-01-21T14:25:25Z,MEMBER,"I think this is ready for the final review, including docs.
I would like to add rolling methods to airspeed benchmark, but in another PR.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289556132
https://github.com/pydata/xarray/pull/1837#issuecomment-358982464,https://api.github.com/repos/pydata/xarray/issues/1837,358982464,MDEyOklzc3VlQ29tbWVudDM1ODk4MjQ2NA==,6815844,2018-01-19T14:35:31Z,2018-01-19T14:35:31Z,MEMBER,"During the work, I notice a somehow unexpected behavior of rolling.
I expected that with `min_periods=1` option, we will get an array without `nan`,
It is true with `center=False` and also `pd.rolling`.
```python
In [2]: da = xr.DataArray(np.arange(10), dims='x')
In [3]: da.rolling(x=3, min_periods=1, center=False).sum()
Out[3]:
array([ 0., 1., 3., 6., 9., 12., 15., 18., 21., 24.])
Dimensions without coordinates: x
In [5]: s = pd.Series(np.arange(10))
s.rolling(3, min_periods=1, center=True).sum()
Out[7]:
0 1.0
1 3.0
2 6.0
3 9.0
4 12.0
5 15.0
6 18.0
7 21.0
8 24.0
9 17.0
dtype: float64
```
But with `center=True`, we have a `nan` at the end.
```
In [4]: da.rolling(x=3, min_periods=1, center=True).sum()
Out[4]:
array([ 1., 3., 6., 9., 12., 15., 18., 21., 24., nan])
Dimensions without coordinates: x
```
It is because we make `shift` operation after the rolling.
https://github.com/pydata/xarray/blob/74d8318c68be884134d449afad18dfe731d48b72/xarray/core/rolling.py#L263-L269
If we `pad` the array before the rolling operation instead of `shift`, we will not get the last `nan` and the result would be the same to pandas.
(`rolling.to_dataarray('window_dim')` does this).
I think this path is more intuitive.
Any thoughts?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289556132