code: def apply_fit_filters(self, choosers, alternatives):
          
          ch = []
          alts = []
  
          for name, df in self._iter_groups(choosers):
              filtered_choosers, filtered_alts = \
                  self.models[name].apply_fit_filters(df, alternatives)
              ch.append(filtered_choosers)
              alts.append(filtered_alts)
  
          return pd.concat(ch), pd.concat(alts)
docs: Filter `choosers` and `alternatives` for fitting.
          This is done by filtering each submodel and concatenating
          the results.
  
          Parameters
          ----------
          choosers : pandas.DataFrame
              Table describing the agents making choices, e.g. households.
          alternatives : pandas.DataFrame
              Table describing the things from which agents are choosing,
              e.g. buildings.
  
          Returns
          -------
          filtered_choosers, filtered_alts : pandas.DataFrame