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  • raybellwaves · 2 ✖

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  • Calling pd.to_datetime on cftime variable · 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
731743767 https://github.com/pydata/xarray/issues/4598#issuecomment-731743767 https://api.github.com/repos/pydata/xarray/issues/4598 MDEyOklzc3VlQ29tbWVudDczMTc0Mzc2Nw== raybellwaves 17162724 2020-11-22T12:54:18Z 2020-11-22T12:54:18Z CONTRIBUTOR

Why not pandas.date_range

Good point. Given the "normal" spacing of the data that makes sense.

The problematic variable in this dataset is "tau"

Ah thanks for the digging into this for me.

 
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   Calling pd.to_datetime on cftime variable 748229907
731741542 https://github.com/pydata/xarray/issues/4598#issuecomment-731741542 https://api.github.com/repos/pydata/xarray/issues/4598 MDEyOklzc3VlQ29tbWVudDczMTc0MTU0Mg== raybellwaves 17162724 2020-11-22T12:37:15Z 2020-11-22T12:37:15Z CONTRIBUTOR

Which functionality are you looking for in xarray that pandas Timestamp objects provide, but cftime objects do not?

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.tz_localize.html

For a bit more reference. I'm working with HyCOM data and want to keep track of 'local time'.

ds = xr.open_dataset('https://tds.hycom.org/thredds/dodsC/GLBy0.08/latest', decode_times=False) reference_date = ds.time.attrs['units'][12:25] ds['time'] = xr.cftime_range(start=reference_date, periods=len(ds.time), freq='3H')

 
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   Calling pd.to_datetime on cftime variable 748229907

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