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# scipy.stats.zscore

`scipy.stats.``zscore`(a, axis=0, ddof=0)

  Calculates 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 ]])