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