code: def pandas(self):
          
          names,prior,posterior = [],[],[]
          for iname,name in enumerate(self.posterior_parameter.row_names):
              names.append(name)
              posterior.append(np.sqrt(float(
                  self.posterior_parameter[iname, iname]. x)))
              iprior = self.parcov.row_names.index(name)
              prior.append(np.sqrt(float(self.parcov[iprior, iprior].x)))
          for pred_name, pred_var in self.posterior_prediction.items():
              names.append(pred_name)
              posterior.append(np.sqrt(pred_var))
              prior.append(self.prior_prediction[pred_name])
          return pd.DataFrame({"posterior": posterior, "prior": prior},
                                  index=names)
docs: get a pandas dataframe of prior and posterior for all predictions
  
          Returns:
              pandas.DataFrame : pandas.DataFrame
                  a dataframe with prior and posterior uncertainty estimates
                  for all forecasts (predictions)