code: def summarize(self, **kwargs):
          
          import pandas as pd
          colnames = ["fname", "wall_time", "cpu_time", "mpi_nprocs", "omp_nthreads", "mpi_rank"]
  
          frame = pd.DataFrame(columns=colnames)
          for i, timer in enumerate(self.timers()):
              frame = frame.append({k: getattr(timer, k) for k in colnames}, ignore_index=True)
          frame["tot_ncpus"] = frame["mpi_nprocs"] * frame["omp_nthreads"]
  
          # Compute parallel efficiency (use the run with min number of cpus to normalize).
          i = frame["tot_ncpus"].values.argmin()
          ref_wtime = frame.ix[i]["wall_time"]
          ref_ncpus = frame.ix[i]["tot_ncpus"]
          frame["peff"] = (ref_ncpus * ref_wtime) / (frame["wall_time"] * frame["tot_ncpus"])
  
          return frame
docs: Return pandas DataFrame with the most important results stored in the timers.