code: def fill_trajectory(performance_list, time_list):
      # Create n series objects.
      series_list = []
      for n in range(len(time_list)):
          series_list.append(
              pd.Series(data=performance_list[n], index=time_list[n])
          )
  
      # Concatenate to one Series with NaN vales.
      series = pd.concat(series_list, axis=1)
  
      # Fill missing performance values (NaNs) with last non-NaN value.
      series = series.fillna(method="ffill")
  
      # return the trajectories over seeds (series object)
      return series
  
file_path: auto-sklearn/scripts/2015_nips_paper/plot/plot_ranks.py
imports: import csv
  import sys
  import os
  import numpy
  import pandas
  import matplotlib.pyplot
label_desc: Use image node_id 2 for calling a global function with example usage: fill_trajectory(performance_list, time_list) and returns: series