code: def test_4672():
      # Compute means in numpy
      width = 4
      data = data_fixture(100, width)
      data_means = np.mean(data, axis=0)
      # Now upload data to H2O and compute means with H2O Rapids lambda
      fr = h2o.H2OFrame.from_python(
          data.tolist(), column_names=list("ABCD")
      )
      # If this didn't throw an exception in Python 3.6, then we're good.
      h2o_means = fr.apply(lambda x: x.mean(skipna=False))
      # Explicitly check that all columns means are equal
      assert all(
          [
              abs(data_means[i] - h2o_means[0, i]) < 1e-12
              for i in range(0, width)
          ]
      ), "Numpy and H2O column means need to match"
  
imports: import h2o
  from tests import pyunit_utils
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_4672() without return types