def normalize_binop_value(
self, other: ScalarLike
) -> Union[ColumnBase, ScalarLike]:
if other is None:
return other
if isinstance(other, cudf.Scalar):
if self.dtype == other.dtype:
return other
# expensive device-host transfer just to
# adjust the dtype
other = other.value
elif isinstance(other, np.ndarray) and other.ndim == 0:
other = other.item()
other_dtype = np.min_scalar_type(other)
if other_dtype.kind in {"b", "i", "u", "f"}:
if isinstance(other, cudf.Scalar):
return other
other_dtype = np.promote_types(self.dtype, other_dtype)
if other_dtype == np.dtype("float16"):
other_dtype = np.dtype("float32")
other = other_dtype.type(other)
if self.dtype.kind == "b":
other_dtype = min_signed_type(other)
if np.isscalar(other):
other = np.dtype(other_dtype).type(other)
return other
else:
ary = utils.scalar_broadcast_to(
other, size=len(self), dtype=other_dtype
)
return column.build_column(
data=Buffer(ary), dtype=ary.dtype, mask=self.mask,
)
else:
raise TypeError(f"cannot broadcast {type(other)}")
def normalize_binop_value(self, other: ScalarLike) -> CategoricalColumn:
if isinstance(other, np.ndarray) and other.ndim == 0:
other = other.item()
ary = cudf.utils.utils.scalar_broadcast_to(self._encode(other),
size=len(self),
dtype=self.codes.dtype)
col = column.build_categorical_column(
categories=self.dtype.categories._values,
codes=column.as_column(ary),
mask=self.base_mask,
ordered=self.dtype.ordered,
)
return col