code: def group_by():
      # Connect to a pre-existing cluster
  
      h2o_iris = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )
      na_handling = ["rm", "ignore", "all"]
  
      print("Running smoke test")
      # smoke test
      for na in na_handling:
          grouped = h2o_iris.group_by("class")
          grouped.count(na=na).min(na=na).max(na=na).mean(na=na).var(
              na=na
          ).sd(na=na).ss(na=na).sum(na=na)
          print(grouped.get_frame())
          print(grouped.get_frame())
  
label_desc: Use image node_id 1 for calling a global function with example usage: group_by() without return types