code: def _single_feature_logliks_one_step(self, feature, models):
          
          x_non_na = feature[~feature.isnull()]
          if x_non_na.empty:
              return pd.DataFrame()
          else:
              dfs = []
              for name, model in models.items():
                  df = model.single_feature_logliks(feature)
                  df['Modality'] = name
                  dfs.append(df)
              return pd.concat(dfs, ignore_index=True)
docs: Get log-likelihood of models at each parameterization for given data
  
          Parameters
          ----------
          feature : pandas.Series
              Percent-based values of a single feature. May contain NAs, but only
              non-NA values are used.
  
          Returns
          -------
          logliks : pandas.DataFrame