def where(self, cond, other=None):
values = self.view("i8")
if is_scalar(other) and isna(other):
other = NaT.value
else:
# Do type inference if necessary up front
# e.g. we passed PeriodIndex.values and got an ndarray of Periods
other = Index(other)
if is_categorical_dtype(other):
# e.g. we have a Categorical holding self.dtype
if is_dtype_equal(other.categories.dtype, self.dtype):
other = other._internal_get_values()
if not is_dtype_equal(self.dtype, other.dtype):
raise TypeError(
f"Where requires matching dtype, not {other.dtype}")
other = other.view("i8")
result = np.where(cond, values, other).astype("i8")
arr = type(self._data)._simple_new(result, dtype=self.dtype)
return type(self)._simple_new(arr, name=self.name)
def where(self, cond, other=None):
values = self.view("i8")
if is_scalar(other) and isna(other):
other = NaT.value
else:
# Do type inference if necessary up front
# e.g. we passed PeriodIndex.values and got an ndarray of Periods
other = Index(other)
if is_categorical_dtype(other):
# e.g. we have a Categorical holding self.dtype
if needs_i8_conversion(other.categories):
other = other._internal_get_values()
if not is_dtype_equal(self.dtype, other.dtype):
raise TypeError(f"Where requires matching dtype, not {other.dtype}")
other = other.view("i8")
result = np.where(cond, values, other).astype("i8")
return self._shallow_copy(result)
