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  • Fix concatenating Variables with dtype=datetime64 · 5 ✖

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43548881 https://github.com/pydata/xarray/pull/134#issuecomment-43548881 https://api.github.com/repos/pydata/xarray/issues/134 MDEyOklzc3VlQ29tbWVudDQzNTQ4ODgx shoyer 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.

 
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   Fix concatenating Variables with dtype=datetime64 33772168
43538733 https://github.com/pydata/xarray/pull/134#issuecomment-43538733 https://api.github.com/repos/pydata/xarray/issues/134 MDEyOklzc3VlQ29tbWVudDQzNTM4NzMz shoyer 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].

 
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   Fix concatenating Variables with dtype=datetime64 33772168
43531956 https://github.com/pydata/xarray/pull/134#issuecomment-43531956 https://api.github.com/repos/pydata/xarray/issues/134 MDEyOklzc3VlQ29tbWVudDQzNTMxOTU2 shoyer 1217238 2014-05-19T17:23:02Z 2014-05-19T17:23:02Z MEMBER

@akleeman I have a work-around up and ready for testing.

 
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   Fix concatenating Variables with dtype=datetime64 33772168
43478250 https://github.com/pydata/xarray/pull/134#issuecomment-43478250 https://api.github.com/repos/pydata/xarray/issues/134 MDEyOklzc3VlQ29tbWVudDQzNDc4MjUw shoyer 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...

 
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   Fix concatenating Variables with dtype=datetime64 33772168
43476062 https://github.com/pydata/xarray/pull/134#issuecomment-43476062 https://api.github.com/repos/pydata/xarray/issues/134 MDEyOklzc3VlQ29tbWVudDQzNDc2MDYy shoyer 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...

 
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   Fix concatenating Variables with dtype=datetime64 33772168

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