def update_def(df_defaults: pd.DataFrame, df_check: pd.DataFrame, default_columns: str, checker_columns: str, time_def: str, time_check: str, rating: str):
            counter = 0
            for i in df_defaults.index:
                if df_defaults[default_columns][i] in df_check[checker_columns].values:
                    temp_df = \
                    df_check[df_check[checker_columns] == df_defaults[default_columns][i]].reset_index(drop=True).iloc[[0]]
                    temp_df.at[0, rating] = 'D'
                    temp_df.at[0, time_check] = df_defaults[time_def][i]
                    # print(temp_df)
                    df_check = pd.concat([df_check, temp_df], axis=0)
                    counter += 1

            return df_check

        import numpy as np
        from scipy.linalg import expm

        # to weighed avg pass more than 2 elem of slice_list
        # todo ignore value outdated?
        df_migration = df[[_ro_id, slice_column, _value_column]]
        df_migration.reset_index(drop=True, inplace=True)  # todo check, outdated?

        # pivot
        df_default = pd.DataFrame() #todo create df of defaults in any ways
        df_migration = update_def(df_default, df_migration, 'ogrn', _ro_id, 'time', slice_column, _value_column)