code: def fill_phenotype_label(self,inplace=False):
          
          def _get_phenotype(d):
              vals = [k for k,v in d.items() if v ==  1]
              return np.nan if len(vals) == 0 else vals[0]
          if inplace:
              if self.shape[0] == 0: return self
              self['phenotype_label'] = self.apply(lambda x: _get_phenotype(x['phenotype_calls']),1)
              return
          fixed = self.copy()
          if fixed.shape[0] == 0: return fixed
          fixed['phenotype_label'] = fixed.apply(lambda x: _get_phenotype(x['phenotype_calls']),1)
          return fixed
docs: Set the phenotype_label column according to our rules for mutual exclusion