code: def test_cfitz_pandas():
      # 1d
      dta = macrodata.load_pandas().data
      index = date_range(
          start="1959-01-01", end="2009-10-01", freq=QUARTER_END
      )
      dta.index = index
      cycle, trend = cffilter(dta["infl"])
      ndcycle, ndtrend = cffilter(dta["infl"].values)
      assert_allclose(cycle.values, ndcycle, rtol=1e-14)
      assert_equal(cycle.index[0], datetime(1959, 3, 31))
      assert_equal(cycle.index[-1], datetime(2009, 9, 30))
      assert_equal(cycle.name, "infl_cycle")
  
      # 2d
      cycle, trend = cffilter(dta[["infl", "unemp"]])
      ndcycle, ndtrend = cffilter(dta[["infl", "unemp"]].values)
      assert_allclose(cycle.values, ndcycle, rtol=1e-14)
      assert_equal(cycle.index[0], datetime(1959, 3, 31))
      assert_equal(cycle.index[-1], datetime(2009, 9, 30))
      assert_equal(cycle.columns.values, ["infl_cycle", "unemp_cycle"])
  
imports: from statsmodels.compat.pandas import QUARTER_END, assert_frame_equal, make_dataframe
  from datetime import datetime
  import numpy
  from numpy import array, column_stack
  from numpy.testing import assert_, assert_allclose, assert_almost_equal, assert_equal, assert_raises
  from pandas import DataFrame, concat, date_range
  from statsmodels.datasets import macrodata
  from statsmodels.tsa.filters._utils import pandas_wrapper
  from statsmodels.tsa.filters.bk_filter import bkfilter
  from statsmodels.tsa.filters.cf_filter import cffilter
  from statsmodels.tsa.filters.filtertools import convolution_filter, recursive_filter
  from statsmodels.tsa.filters.hp_filter import hpfilter
label_desc: Use image node_id 6 for calling a global function with example usage: test_cfitz_pandas() without return types