issue_comments: 1237014040

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https://github.com/pydata/xarray/issues/6906#issuecomment-1237014040 https://api.github.com/repos/pydata/xarray/issues/6906 1237014040 IC_kwDOAMm_X85Ju1YY 6628425 2022-09-05T13:18:21Z 2022-09-05T13:18:21Z MEMBER

Thanks @amckinstry. I guess my last try to produce a pandas minimal example might be: ```

import numpy as np; import pandas as pd pd.Series(np.array([np.int64(1000000).astype("<M8[h]")])) 0 2084-01-29 16:00:00 dtype: datetime64[ns] or potentially more simply: import numpy as np; import pandas as pd pd.Series(np.int64(1000000).astype("<M8[h]")) 0 2084-01-29 16:00:00 dtype: datetime64[ns] `` Somewhere something is going wrong in converting a non-nanosecond-precision datetime value to a nanosecond-precision one (maybe the cast to apd.Timestamp` in my earlier example was short-circuiting this).

I think #6988 should likely work around this issue at least on the xarray side, since it passes datetime64[ns] values into the DataArray constructor immediately. It also seems like the function where the error occurs (ensure_datetime64ns) was recently eliminated in favor of an updated implementation in pandas, so I wonder if this will be an issue there going forward.

 
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