issues: 849771808

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
849771808 MDU6SXNzdWU4NDk3NzE4MDg= 5107 Converting `cftime.datetime` objects to `np.datetime64` values through `astype` 6628425 open 0     0 2021-04-04T01:02:55Z 2021-10-05T00:00:36Z   MEMBER      

The discussion of the use of the indexes property in #5102 got me thinking about this StackOverflow answer. For a while I have thought that my answer there isn't very satisfying, not only because it relies on this somewhat obscure indexes property, but also because it only works on dimension coordinates -- i.e. something that would be backed by an index.

Describe the solution you'd like

It would be better if we could do this conversion with astype, e.g. da.astype("datetime64[ns]"). This would allow conversion to datetime64 values for all cftime.datetime DataArrays -- dask-backed or NumPy-backed, 1D or ND -- through a fairly standard and well-known method. To my surprise, while you do not get the nice calendar-switching warning that CFTimeIndex.to_datetimeindex provides, this actually already kind of seems to work (?!):

``` In [1]: import xarray as xr

In [2]: times = xr.cftime_range("2000", periods=6, calendar="noleap")

In [3]: da = xr.DataArray(times.values.reshape((2, 3)), dims=["a", "b"])

In [4]: da.astype("datetime64[ns]") Out[4]: <xarray.DataArray (a: 2, b: 3)> array([['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000', '2000-01-03T00:00:00.000000000'], ['2000-01-04T00:00:00.000000000', '2000-01-05T00:00:00.000000000', '2000-01-06T00:00:00.000000000']], dtype='datetime64[ns]') Dimensions without coordinates: a, b ```

NumPy obviously does not officially support this -- nor would I expect it to -- so I would be wary of simply documenting this behavior as is. Would it be reasonable for us to modify xarray.core.duck_array_ops.astype to explicitly implement this conversion ourselves for cftime.datetime arrays? This way we could ensure this was always supported, and we could include appropriate errors for out-of-bounds times (the NumPy method currently overflows in that case) and warnings for switching from non-standard calendars.

 
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