def pd_range_index_eq_overload(self, other):
self_is_range_index = isinstance(self, RangeIndexType)
other_is_range_index = isinstance(other, RangeIndexType)
if not (
self_is_range_index and other_is_range_index or
(self_is_range_index and
(check_is_numeric_array(other) or isinstance(other, types.Number))) or
((check_is_numeric_array(self)
or isinstance(self, types.Number) and other_is_range_index))):
return None
one_operand_is_scalar = isinstance(self, types.Number) or isinstance(
other, types.Number)
def pd_range_index_eq_impl(self, other):
if one_operand_is_scalar == False: # noqa
if len(self) != len(other):
raise ValueError("Lengths must match to compare")
# names do not matter when comparing pd.RangeIndex
left = self.values if self_is_range_index == True else self # noqa
right = other.values if other_is_range_index == True else other # noqa
return list(
left == right
) # FIXME_Numba#5157: result must be np.array, remove list when Numba is fixed
return pd_range_index_eq_impl
def pd_int64_index_ne_overload(self, other):
self_is_index = isinstance(self, Int64IndexType)
other_is_index = isinstance(other, Int64IndexType)
if not (self_is_index and other_is_index
or (self_is_index and (check_is_numeric_array(other) or isinstance(other, types.Number)))
or ((check_is_numeric_array(self) or isinstance(self, types.Number)) and other_is_index)):
return None
def pd_int64_index_ne_impl(self, other):
eq_res = np.asarray(self == other) # FIXME_Numba#5157: remove np.asarray and return as list
return list(~eq_res)
return pd_int64_index_ne_impl