code: def test_describe_df():
      """Test returned data frame of describe method."""
      pytest.importorskip("pandas")
      fname = Path(__file__).parent / "data" / "test_raw.fif"
      raw = read_raw_fif(fname)
  
      df = raw.describe(data_frame=True)
      assert df.shape == (376, 8)
      assert df.columns.tolist() == [
          "name",
          "type",
          "unit",
          "min",
          "Q1",
          "median",
          "Q3",
          "max",
      ]
      assert df.index.name == "ch"
      assert_allclose(
          df.iloc[0, 3:].astype(float),
          np.array(
              [
                  -2.218017605790535e-11,
                  -3.857421923113974e-12,
                  -9.643554807784935e-13,
                  1.928710961556987e-12,
                  4.146728567347522e-11,
              ]
          ),
      )
  
imports: import math
  import os
  import re
  from contextlib import redirect_stdout
  from io import StringIO
  from os import path
  from pathlib import Path
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal, assert_array_less
  import mne
  from mne import Annotations, concatenate_raws, create_info, pick_types
  from mne._fiff._digitization import DigPoint, _dig_kind_dict
  from mne._fiff.constants import FIFF
  from mne._fiff.meas_info import Info, _get_valid_units, _writing_info_hdf5
  from mne._fiff.pick import _ELECTRODE_CH_TYPES, _FNIRS_CH_TYPES_SPLIT
  from mne._fiff.proj import Projection
  from mne._fiff.utils import _mult_cal_one
  from mne.fixes import _numpy_h5py_dep
  from mne.io import BaseRaw, RawArray, read_raw_fif
  from mne.io.base import _get_scaling
  from mne.transforms import Transform
  from mne.utils import _import_h5io_funcs, _raw_annot, _stamp_to_dt, _TempDir, catch_logging, check_version, object_diff
label_desc: Use image node_id 17 for calling a global function with example usage: test_describe_df() without return types