I have some code which tries to extract all rows between two dates. In Dask I tried two solutions, firstly the obvious way, with t1 and t2 being numpy.datetime64 :

df[(df["date"] => t1) & df["date"] <= t2]

which works. I was wondering why using set_index and then indexing didn’t work though :

df.set_index("date", sorted=True)
df.loc[t1:t2].compute()

This gives multiple exceptions :

KeyError: numpy.datetime64('2023-10-28T06:00:00.000000000')
numpy.exceptions.DTypePromotionError: The DType <class 'numpy.dtypes.DateTime64DType'> could not be promoted by <class 'numpy.dtypes.Int32DType'>. This means that no common DType exists for the given inputs. For example they cannot 
be stored in a single array unless the dtype is `object`. The full list of DTypes is: (<class 'numpy.dtypes.DateTime64DType'>, <class 'numpy.dtypes.Int32DType'>)
numpy.core._exceptions.UFuncTypeError: ufunc 'greater' did not contain a loop with signature matching types (<class 'numpy.dtypes.DateTime64DType'>, <class 'numpy.dtypes.Int32DType'>) -> None

I tried wrapping with to_datetime() from pandas and dask with no luck. Is this a type error or am I misusing the set_index interface?