code: def zero_fill_missing_phenotypes(self):
          
          if self.is_uniform(verbose=False): return self.copy()
          output = self.copy()
          def _do_fill(d,names):
              old_names = list(d.keys())
              old_values = list(d.values())
              missing = set(names)-set(old_names)
              return dict(zip(old_names+list(missing),old_values+([0]*len(missing))))
          ## Need to make these uniform
          pnames = self.phenotypes
          output['phenotype_calls']= output.apply(lambda x:
              _do_fill(x['phenotype_calls'],pnames)
              ,1)
          return output
docs: Fill in missing phenotypes and scored types by listing any missing data as negative
  
          Returns:
              CellDataFrame: The CellDataFrame modified.