code: def summary(self):
          
          ci = 1 - self.alpha
          with np.errstate(invalid="ignore", divide="ignore"):
              df = pd.DataFrame(index=self.hazards_.index)
              df["coef"] = self.hazards_
              df["exp(coef)"] = np.exp(self.hazards_)
              df["se(coef)"] = self.standard_errors_
              df["z"] = self._compute_z_values()
              df["p"] = self._compute_p_values()
              df["-log2(p)"] = -np.log2(df["p"])
              df["lower %g" % ci] = self.confidence_intervals_["lower-bound"]
              df["upper %g" % ci] = self.confidence_intervals_["upper-bound"]
              return df
docs: Summary statistics describing the fit.
          Set alpha property in the object before calling.
  
          Returns
          -------
          df : DataFrame
              Contains columns coef, np.exp(coef), se(coef), z, p, lower, upper