def _preprocess_value_matrix(self): if self._preprocessed_value_matrix: return self._value_matrix = [[ dataproperty.convert_value(value) for value in value_list ] for value_list in self.value_matrix] self._value_matrix = dict(zip(self.header_list, self._value_matrix)) self._preprocessed_value_matrix = True
def _preprocess_value_matrix(self): if self._preprocessed_value_matrix: return self._value_matrix = [ [dataproperty.convert_value(value) for value in value_list] for value_list in self.value_matrix ] self._value_matrix = dict(zip(self.header_list, self._value_matrix)) self._preprocessed_value_matrix = True
def _preprocess_value_matrix(self): if self._preprocessed_value_matrix: return value_matrix = [[ dataproperty.convert_value(value, self.__none_value) for value in value_list ] for value_list in self.value_matrix] table_data = [ dict(zip(self.header_list, value_list)) for value_list in value_matrix ] if dataproperty.is_empty_string(self.table_name): self._value_matrix = table_data else: self._value_matrix = {self.table_name: table_data} self._preprocessed_value_matrix = True
def _preprocess_value_matrix(self): if self._preprocessed_value_matrix: return value_matrix = [ [dataproperty.convert_value(value) for value in value_list] for value_list in self.value_matrix ] table_data = [ dict(zip(self.header_list, value_list)) for value_list in value_matrix ] if dataproperty.is_empty_string(self.table_name): self._value_matrix = table_data else: self._value_matrix = {self.table_name: table_data} self._preprocessed_value_matrix = True
def test_abnormal(self): assert is_nan(convert_value(nan))
def test_none(self, value, none_return_value, expected): assert convert_value(value, none_return_value) == expected
def test_normal(self, value, expected): assert convert_value(value) == expected