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  • shoyer · 2 ✖

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  • Ensure decoding as datetime64[ns] · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
37505097 https://github.com/pydata/xarray/pull/59#issuecomment-37505097 https://api.github.com/repos/pydata/xarray/issues/59 MDEyOklzc3VlQ29tbWVudDM3NTA1MDk3 shoyer 1217238 2014-03-13T06:58:15Z 2014-03-13T06:58:15Z MEMBER

If you're sure that 'time' is stored as pandas.DatetimeIndex (e.g., you use a standard calendar and all dates within roughly 1700-2300), you can do those comparisons with ds['time'].index, which even lets you compare to strings: ds['time'].index > '2014-01-01'. But I agree it would be nice to have a general to_datetime method like pandas. If we already wrote this functionality (and it was somewhat tricky), we might as well expose it to users.

 
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   Ensure decoding as datetime64[ns] 29067976
37434012 https://github.com/pydata/xarray/pull/59#issuecomment-37434012 https://api.github.com/repos/pydata/xarray/issues/59 MDEyOklzc3VlQ29tbWVudDM3NDM0MDEy shoyer 1217238 2014-03-12T16:58:56Z 2014-03-12T16:58:56Z MEMBER

So I think the unfortunate reality is that np.datetime64 is still our best option. Otherwise we are left with dates as Python objects, which is painfully slow.

 
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   Ensure decoding as datetime64[ns] 29067976

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