parameters: (a, axis=0, ddof=0)
docstring: Calculate the z score of each value in the sample, relative to the
      sample mean and standard deviation.
  
      Parameters
      ----------
      a : array_like
          An array like object containing the sample data.
      axis : int or None, optional
          Axis along which to operate. Default is 0. If None, compute over
          the whole array `a`.
      ddof : int, optional
          Degrees of freedom correction in the calculation of the
          standard deviation. Default is 0.
  
      Returns
      -------
      zscore : array_like
          The z-scores, standardized by mean and standard deviation of
          input array `a`.
  
      Notes
      -----
      This function preserves ndarray subclasses, and works also with
      matrices and masked arrays (it uses `asanyarray` instead of
      `asarray` for parameters).
  
      Examples
      --------
      >>> a = np.array([ 0.7972,  0.0767,  0.4383,  0.7866,  0.8091,
      ...                0.1954,  0.6307,  0.6599,  0.1065,  0.0508])
      >>> from scipy import stats
      >>> stats.zscore(a)
      array([ 1.1273, -1.247 , -0.0552,  1.0923,  1.1664, -0.8559,  0.5786,
              0.6748, -1.1488, -1.3324])
  
      Computing along a specified axis, using n-1 degrees of freedom
      (``ddof=1``) to calculate the standard deviation:
  
      >>> b = np.array([[ 0.3148,  0.0478,  0.6243,  0.4608],
      ...               [ 0.7149,  0.0775,  0.6072,  0.9656],
      ...               [ 0.6341,  0.1403,  0.9759,  0.4064],
      ...               [ 0.5918,  0.6948,  0.904 ,  0.3721],
      ...               [ 0.0921,  0.2481,  0.1188,  0.1366]])
      >>> stats.zscore(b, axis=1, ddof=1)
      array([[-0.19264823, -1.28415119,  1.07259584,  0.40420358],
             [ 0.33048416, -1.37380874,  0.04251374,  1.00081084],
             [ 0.26796377, -1.12598418,  1.23283094, -0.37481053],
             [-0.22095197,  0.24468594,  1.19042819, -1.21416216],
             [-0.82780366,  1.4457416 , -0.43867764, -0.1792603 ]])
docstring_summary: Calculate the z score of each value in the sample, relative to the
      sample mean and standard deviation.
docstring_tokens: Calculate the z score of each value in the sample relative to the sample mean and standard deviation .
function: def zscore(a, axis=0, ddof=0):
      """
      Calculate the z score of each value in the sample, relative to the
      sample mean and standard deviation.
  
      Parameters
      ----------
      a : array_like
          An array like object containing the sample data.
      axis : int or None, optional
          Axis along which to operate. Default is 0. If None, compute over
          the whole array `a`.
      ddof : int, optional
          Degrees of freedom correction in the calculation of the
          standard deviation. Default is 0.
  
      Returns
      -------
      zscore : array_like
          The z-scores, standardized by mean and standard deviation of
          input array `a`.
  
      Notes
      -----
      This function preserves ndarray subclasses, and works also with
      matrices and masked arrays (it uses `asanyarray` instead of
      `asarray` for parameters).
  
      Examples
      --------
      >>> a = np.array([ 0.7972,  0.0767,  0.4383,  0.7866,  0.8091,
      ...                0.1954,  0.6307,  0.6599,  0.1065,  0.0508])
      >>> from scipy import stats
      >>> stats.zscore(a)
      array([ 1.1273, -1.247 , -0.0552,  1.0923,  1.1664, -0.8559,  0.5786,
              0.6748, -1.1488, -1.3324])
  
      Computing along a specified axis, using n-1 degrees of freedom
      (``ddof=1``) to calculate the standard deviation:
  
      >>> b = np.array([[ 0.3148,  0.0478,  0.6243,  0.4608],
      ...               [ 0.7149,  0.0775,  0.6072,  0.9656],
      ...               [ 0.6341,  0.1403,  0.9759,  0.4064],
      ...               [ 0.5918,  0.6948,  0.904 ,  0.3721],
      ...               [ 0.0921,  0.2481,  0.1188,  0.1366]])
      >>> stats.zscore(b, axis=1, ddof=1)
      array([[-0.19264823, -1.28415119,  1.07259584,  0.40420358],
             [ 0.33048416, -1.37380874,  0.04251374,  1.00081084],
             [ 0.26796377, -1.12598418,  1.23283094, -0.37481053],
             [-0.22095197,  0.24468594,  1.19042819, -1.21416216],
             [-0.82780366,  1.4457416 , -0.43867764, -0.1792603 ]])
      """
      a = np.asanyarray(a)
      mns = a.mean(axis=axis)
      sstd = a.std(axis=axis, ddof=ddof)
      if axis and mns.ndim < a.ndim:
          return ((a - np.expand_dims(mns, axis=axis)) /
                  np.expand_dims(sstd, axis=axis))
      else:
          return (a - mns) / sstd
function_tokens: def zscore ( a , axis = 0 , ddof = 0 ) : a = np . asanyarray ( a ) mns = a . mean ( axis = axis ) sstd = a . std ( axis = axis , ddof = ddof ) if axis and mns . ndim < a . ndim : return ( ( a - np . expand_dims ( mns , axis = axis ) ) / np . expand_dims ( sstd , axis = axis ) ) else : return ( a - mns ) / sstd