Did you check whether this change impacts Pandas? They use numpy datetimes and timedeltas.
If it does impact Pandas, I’d try to get the Pandas maintainers’ opinions. On Sat, Mar 7, 2026 at 6:53 AM riku-sakamoto via NumPy-Discussion < [email protected]> wrote: > Dear NumPy developers, > > Hi all, I am Riku. > I currently work on the PR #29619 ( > https://github.com/numpy/numpy/pull/29619 ), which deprecates `generic` > unit in `timedelta64` dtype. > This change is expected to have a large impact on the downstream > libraries, so I sent this email to notify it. > > Let me briefly summarize the motivation and changed behavior. > > Motivation: > > `generic` unit in `timedelta64` dtype is allowed to be operated with any > other `timedelta64` dtype. > So, comparison between several `timedelta64` dtypes including `generic` > unit becomes not transitive. > It is reported in the issue #28287 ( > https://github.com/numpy/numpy/issues/28287 ) > > ```python > x, y, z = np.timedelta64(1, "ms"), np.timedelta64(2), np.timedelta64(5, > "ns") > print(x < y < z) # True, but x < z is False > ``` > > Changed behavior: > > After the deprecation, when using `generic` unit in `timedelta64` dtype, > `DeprecationWarning` will be raised. > > Following operations will raise the warning: > > ```python > np.timedelta64(1) > > np.array([1, 2], dtype="m8") > > # `+ 1` is treated as adding 1 with generic timedelta > np.array([1, 2], dtype="m8[s]") + 1 > ``` > > I want to note that `NaT` is also required to have specific unit. So, the > following code will also raise the warning: > > ```python > np.array(["NaT"], dtype="m8") > ``` > > I'd appreciate it if you let me know when you have any questions or > concerns about this change. > Any feedback is welcome. > > Best Regards, > > Riku > _______________________________________________ > NumPy-Discussion mailing list -- [email protected] > To unsubscribe send an email to [email protected] > https://mail.python.org/mailman3//lists/numpy-discussion.python.org > Member address: [email protected] >
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