Normarize data with Scipy

>>> import numpy as np
>>> from scipy import stats

>>> a = np.array([ 0.7972,  0.0767,  0.4383,  0.7866,  0.8091,  0.1954,
                   0.6307, 0.6599,  0.1065,  0.0508])

>>> stats.zscore(a)
array([ 1.1273, -1.247 , -0.0552,  1.0923,  1.1664, -0.8559,  0.5786,
        0.6748, -1.1488, -1.3324])

>>> 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]])

>>> z_score = stats.zscore(b, axis=1, ddof=1)

>>> z_score
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 ]])

>>> np.allclose(np.mean(z_score, axis=1), np.zeros(5))
True

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