code: def _generate_remark_str(self, end_of_line = '\n'):
          
          warnings = {}
          msg = '{}'.format(end_of_line)
  
          # generate warnings for continuous variables
          if self._continuous:
              # highlight far outliers
              outlier_mask = self.cont_describe.far_outliers > 1
              outlier_vars = list(self.cont_describe.far_outliers[outlier_mask].dropna(how='all').index)
              if outlier_vars:
                  warnings["Warning, Tukey test indicates far outliers in"] = outlier_vars
  
              # highlight possible multimodal distributions using hartigan's dip test
              # -1 values indicate NaN
              modal_mask = (self.cont_describe.diptest >= 0) & (self.cont_describe.diptest <= 0.05)
              modal_vars = list(self.cont_describe.diptest[modal_mask].dropna(how='all').index)
              if modal_vars:
                  warnings["Warning, Hartigan's Dip Test reports possible multimodal distributions for"] = modal_vars
  
              # highlight non normal distributions
              # -1 values indicate NaN
              modal_mask = (self.cont_describe.normaltest >= 0) & (self.cont_describe.normaltest <= 0.001)
              modal_vars = list(self.cont_describe.normaltest[modal_mask].dropna(how='all').index)
              if modal_vars:
                  warnings["Warning, test for normality reports non-normal distributions for"] = modal_vars
  
  
          # create the warning string
          for n,k in enumerate(sorted(warnings)):
              msg += '[{}] {}: {}.{}'.format(n+1,k,', '.join(warnings[k]), end_of_line)
  
          return msg
docs: Generate a series of remarks that the user should consider
          when interpreting the summary statistics.