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  • BUG: not converting datetime64[ns] with tz from pandas.Series · 3 ✖

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168730777 https://github.com/pydata/xarray/issues/701#issuecomment-168730777 https://api.github.com/repos/pydata/xarray/issues/701 MDEyOklzc3VlQ29tbWVudDE2ODczMDc3Nw== max-sixty 5635139 2016-01-04T16:48:44Z 2016-01-04T16:48:44Z MEMBER

@jreback

Yeah if you store things as Index objects, then this would go away.

@shoyer remains unconvinced, and I defer to him without reservation - but if you think this would make sense, say so

 
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   BUG: not converting datetime64[ns] with tz from pandas.Series 124685682
168675157 https://github.com/pydata/xarray/issues/701#issuecomment-168675157 https://api.github.com/repos/pydata/xarray/issues/701 MDEyOklzc3VlQ29tbWVudDE2ODY3NTE1Nw== jreback 953992 2016-01-04T13:21:16Z 2016-01-04T13:21:16Z MEMBER

yeh, this is fine. maybe just note which dtypes are lossless and which are not. Yeah if you store things as Index objects, then this would go away.

 
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   BUG: not converting datetime64[ns] with tz from pandas.Series 124685682
168588381 https://github.com/pydata/xarray/issues/701#issuecomment-168588381 https://api.github.com/repos/pydata/xarray/issues/701 MDEyOklzc3VlQ29tbWVudDE2ODU4ODM4MQ== shoyer 1217238 2016-01-04T05:54:25Z 2016-01-04T05:54:25Z MEMBER

This is difficult to do properly, because xray uses numpy or dask.array to store array data, and datetime64 with a timezone is not a real numpy dtype.

I guess the right solution (similar to what I did for PeriodIndex in #692) would be to convert to dtype=object when necessary. Is there an easy way to get this from pandas?

Although, I do think it's pretty consistent that we use the result of np.asarray(s) as the values in xray. The timezone info is thrown away when you call this.

 
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   BUG: not converting datetime64[ns] with tz from pandas.Series 124685682

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