def normalize_binop_value(self, other: DatetimeLikeScalar) -> ScalarLike:
if isinstance(other, cudf.Scalar):
return other
if isinstance(other, np.ndarray) and other.ndim == 0:
other = other.item()
if isinstance(other, dt.datetime):
other = np.datetime64(other)
elif isinstance(other, dt.timedelta):
other = np.timedelta64(other)
elif isinstance(other, pd.Timestamp):
other = other.to_datetime64()
elif isinstance(other, pd.Timedelta):
other = other.to_timedelta64()
elif isinstance(other, cudf.DateOffset):
return other
if isinstance(other, np.datetime64):
if np.isnat(other):
return cudf.Scalar(None, dtype=self.dtype)
other = other.astype(self.dtype)
return cudf.Scalar(other)
elif isinstance(other, np.timedelta64):
other_time_unit = cudf.utils.dtypes.get_time_unit(other)
if other_time_unit not in ("s", "ms", "ns", "us"):
other = other.astype("timedelta64[s]")
if np.isnat(other):
return cudf.Scalar(None, dtype=other.dtype)
return cudf.Scalar(other)
else:
raise TypeError(f"cannot normalize {type(other)}")
def __contains__(self, item: DatetimeLikeScalar) -> bool:
try:
item = np.timedelta64(item, self._time_unit)
except ValueError:
# If item cannot be converted to duration type
# np.timedelta64 raises ValueError, hence `item`
# cannot exist in `self`.
return False
return item.view("int64") in self.as_numerical