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/134#issuecomment-43548881,https://api.github.com/repos/pydata/xarray/issues/134,43548881,MDEyOklzc3VlQ29tbWVudDQzNTQ4ODgx,1217238,2014-05-19T20:00:48Z,2014-05-19T20:00:48Z,MEMBER,"The reason I cast all datetime64 to datetime64[ns] is because pandas will not let you make an Index of datetime64 objects with anything other than ns precision. If you try to make it an Index with dtype=object you'll actually get an array of datetime.datetime objects:

```
>>> pd.Index(pd.date_range('2000-01-01', periods=5).values.astype('datetime64[us]'), dtype='object').values
array([datetime.datetime(2000, 1, 1, 0, 0),
       datetime.datetime(2000, 1, 2, 0, 0),
       datetime.datetime(2000, 1, 3, 0, 0),
       datetime.datetime(2000, 1, 4, 0, 0),
       datetime.datetime(2000, 1, 5, 0, 0)], dtype=object)
```

But I do agree this is not terribly consistent nor fully thought through. And it should certainly be well-documented.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33772168
https://github.com/pydata/xarray/pull/134#issuecomment-43538733,https://api.github.com/repos/pydata/xarray/issues/134,43538733,MDEyOklzc3VlQ29tbWVudDQzNTM4NzMz,1217238,2014-05-19T18:23:53Z,2014-05-19T18:23:53Z,MEMBER,"I agree, we should either ensure datetime64[ns] or ensure that operations on datetime objects preserve dtype. The latest commit should verify this.

My main reason for not doing the former is that I thought it would be nice (in theory) to support using plain datetime objects if datetime64[ns] does not have a long enough time range for some users. But for most users, I suspect they would indeed rather have datetime64[ns].
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33772168
https://github.com/pydata/xarray/pull/134#issuecomment-43531956,https://api.github.com/repos/pydata/xarray/issues/134,43531956,MDEyOklzc3VlQ29tbWVudDQzNTMxOTU2,1217238,2014-05-19T17:23:02Z,2014-05-19T17:23:02Z,MEMBER,"@akleeman I have a work-around up and ready for testing.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33772168
https://github.com/pydata/xarray/pull/134#issuecomment-43478250,https://api.github.com/repos/pydata/xarray/issues/134,43478250,MDEyOklzc3VlQ29tbWVudDQzNDc4MjUw,1217238,2014-05-19T08:42:55Z,2014-05-19T08:45:03Z,MEMBER,"The later is a (newly exposed) bug in `pandas.isnull`. I'm waiting on Travis before submitting the PR upstream but here is the fix: https://github.com/shoyer/pandas/compare/isnull-0d-object-array

Looks like we'll need to add a work-around for now...
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33772168
https://github.com/pydata/xarray/pull/134#issuecomment-43476062,https://api.github.com/repos/pydata/xarray/issues/134,43476062,MDEyOklzc3VlQ29tbWVudDQzNDc2MDYy,1217238,2014-05-19T08:15:00Z,2014-05-19T08:15:00Z,MEMBER,"With regards to datetime.datetime being converted into integers, the issue is that pandas currently does dtype inference when indexing an Index (https://github.com/pydata/pandas/issues/6370). Fortunately the next version of pandas (0.14), due out in a few weeks, stops doing this.

I had made some attempts to fix this previously but it was untested and only sort of worked. I think this latest commit should fix that up.

Let me check on that second issue...
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,33772168