code: def test_partitioned_dataframe(setup_cluster):
      import numpy as np
      import pandas as pd
      from dask import dataframe as dd
      from pandas._testing import assert_frame_equal
  
      data = np.random.randn(10000, 100)
      df = dd.from_pandas(
          pd.DataFrame(data, columns=[f"col{i}" for i in range(100)]),
          npartitions=4,
      )
      df["col0"] = df["col0"] + df["col1"] / 2
      col2_mean = df["col2"].mean()
      df = df[df["col2"] > col2_mean]
  
      dask_res = df.compute()
      assert_frame_equal(
          dask_res,
          df.compute(scheduler=mars_scheduler),
          check_index_type=False,
      )
      assert_frame_equal(
          dask_res,
          convert_dask_collection(df).execute().fetch(),
          check_index_type=False,
      )
  
file_path: mars/mars/contrib/dask/tests/test_dask.py
imports: import pytest
  from ....utils import lazy_import
  from ..None import convert_dask_collection, mars_scheduler
label_desc: Use image node_id 2 for calling a global function with example usage: test_partitioned_dataframe(setup_cluster) without return types