code: def target_real_feature_binary_test(x, y):
      """
      Calculate the feature significance of a binary feature to a real-valued target as a p-value.
      Use the `Kolmogorov-Smirnov` test from from :func:`~scipy.stats.ks_2samp` for this.
  
      :param x: the binary feature vector
      :type x: pandas.Series
  
      :param y: the real-valued target vector
      :type y: pandas.Series
  
      :return: the p-value of the feature significance test. Lower p-values indicate a higher feature significance.
      :rtype: float
  
      :raise: ``ValueError`` if the feature is not binary.
      """
      __check_if_pandas_series(x, y)
      _check_for_nans(x, y)
  
      # Check for correct value range
      __check_for_binary_feature(x)
  
      # Extract the unique values
      x0, x1 = np.unique(x.values)
  
      # Divide target according to feature
      y_x1 = y[x == x1]
      y_x0 = y[x == x0]
  
      # Perform Kolmogorov-Smirnov test
      KS, p_value = stats.ks_2samp(y_x1, y_x0)
  
      return p_value
  
file_path: tsfresh/tsfresh/feature_selection/significance_tests.py
imports: import warnings
  from builtins import str
  import numpy
  import pandas
  from scipy import stats
label_desc: Use image node_id 3 for calling a global function with example usage: target_real_feature_binary_test(x, y) and returns: p_value