text: import pandas as pd
  import numpy as np
  
  janus_timefile = '../data/janus/janus_unprocessed_timings.csv'
  bridges_timefile = '../data/bridges/bridges_unprocessed_timings.csv'
  comet_timefile = '../data/comet/comet_unprocessed_timings.csv'
  properties_file = '../data/unprocessed_properties.csv'
  
  janus_times = pd.read_csv(janus_timefile, header=0, index_col=0)
  bridges_times = pd.read_csv(bridges_timefile, header=0, index_col=0)
  comet_times = pd.read_csv(comet_timefile, header=0, index_col=0)
  properties = pd.read_csv(properties_file, header=0, index_col=0)
  
  times = [janus_times, bridges_times, comet_times]
  combined_times = pd.concat(times)
  combined_times.dropna()
  combined_times.drop_duplicates()
  combined_times.info()
  combined_times.describe()
  
  converged = combined_times[combined_times['status_id'] == 1]
  grouped = converged.groupby(['system_id', 'numprocs', 'matrix'])
  
  i = 0
  file = open('good_bad_timings.csv', 'w') 
  best_times = grouped.time.aggregate(np.min)
  print(best_times[1][28]['nd6k.mtx'])
  
  for sys_np_mat, group in grouped:
      for t in group.time:
          file.write(str(sys_np_mat[0]) + '\t' + str(sys_np_mat[1]) + 
                     '\t' + str(sys_np_mat[2]) + '\t' + str(t) + '\t' +
                     '\t' + str(best_times[sys_np_mat]) + '\t')
          if t <= 1.25 * best_times[sys_np_mat]:
              file.write('GOOD\n') 
          else:
              file.write('BAD\n')
          #print('b', best_times[sys_np_mat]    
  
  i_a = 0
  X_a_train, X_a_test = [], []
  X_b_train, X_b_test = [], []
  y_a_train, y_a_test = [], []
  y_b_train, y_b_test = [], []
  
  np_a = 1
  np_b = 1
  system_a = [1,2]
  system_b = 0
  
  a = pd.DataFrame()
  b = pd.DataFrame()
  
  if type(np_a) == str and np_a == "all":
      a = all_combined
  elif type(np_a) == int:
      a = all_combined[(all_combined.np == np_a)]
  elif type(np_a) == list:
      for num in np_a:
          a = a.append(all_combined[(all_combined.np == num)], 
                       ignore_index=True)
  
  # now process system_a
  if type(system_a) == str and system_a == "all":
      a = a
  elif type(system_a) == int:
      a = a[(a.system_id == system_a)]
  elif type(system_a) == list:
      temp = pd.DataFrame()
      for num in system_a:
          temp = temp.append(a[(a.system_id == num)], ignore_index=True)
      a = temp
  
  a.describe()
  l = list(a.columns)
  l[-1]
  
  