class_docstr: The unit tests in this module make sure if the time series features are created properly
class_docstr_tok: ["The","unit","tests","in","this","module","make","sure","if","the","time","series","features","are","created","properly"]
code: def test_extract_features_with_and_without_parallelization(self):
      df = self.create_test_data_sample()
  
      features_parallel = extract_features(
          df,
          column_id="id",
          column_sort="sort",
          column_kind="kind",
          column_value="val",
          n_jobs=2,
      )
  
      features_serial = extract_features(
          df,
          column_id="id",
          column_sort="sort",
          column_kind="kind",
          column_value="val",
          n_jobs=0,
      )
  
      self.assertCountEqual(
          features_parallel.columns, features_serial.columns
      )
  
      for col in features_parallel.columns:
          np.testing.assert_array_almost_equal(
              features_parallel[col], features_serial[col]
          )
  
imports: import os
  import tempfile
  import numpy
  import pandas
  from mock import Mock
  from tests.fixtures import DataTestCase
  from tsfresh.feature_extraction.extraction import extract_features
  from tsfresh.feature_extraction.settings import ComprehensiveFCParameters, PickableSettings
  from tsfresh.utilities.distribution import IterableDistributorBaseClass, MapDistributor
label_desc: Use image node_id 11 for calling the ExtractionTestCase obj's underlying member method code with example usage: obj.test_extract_features_with_and_without_parallelization() without return types