old_contents: import sys
  sys.path.insert(1, "../../../")
  import h2o
  
  def cupMediumGBM(ip,port):
    # Connect to h2o
    h2o.init(ip,port)
  
    train = h2o.import_frame(path=h2o.locate("bigdata/laptop/usecases/cup98LRN_z.csv"))
    test = h2o.import_frame(path=h2o.locate("bigdata/laptop/usecases/cup98VAL_z.csv"))
  
    train["TARGET_B"] = train["TARGET_B"].asfactor()
  
    # Train H2O GBM Model:
    train_cols = train.names()
    for c in ['', "TARGET_D", "TARGET_B", "CONTROLN"]:
      train_cols.remove(c)
    model = h2o.gbm(x=train[train_cols], y=train["TARGET_B"], distribution = "bernoulli", ntrees = 5)
  
  if __name__ == "__main__":
    h2o.run_test(sys.argv, cupMediumGBM)
  
new_contents: import sys
  sys.path.insert(1, "../../../")
  import h2o
  
  def cupMediumGBM(ip,port):
    # Connect to h2o
    h2o.init(ip,port)
  
    train = h2o.import_frame(path=h2o.locate("bigdata/laptop/usecases/cup98LRN_z.csv"))
    test = h2o.import_frame(path=h2o.locate("bigdata/laptop/usecases/cup98VAL_z.csv"))
  
    train["TARGET_B"] = train["TARGET_B"].asfactor()
  
    # Train H2O GBM Model:
    train_cols = train.names()
    for c in ['C1', "TARGET_D", "TARGET_B", "CONTROLN"]:
      train_cols.remove(c)
    model = h2o.gbm(x=train[train_cols], y=train["TARGET_B"], distribution = "bernoulli", ntrees = 5)
  
  if __name__ == "__main__":
    h2o.run_test(sys.argv, cupMediumGBM)
  
subject: Update test for new column naming rules. Remove '' as a name and call column 'C1'