Basically, take a matrix and change it so that its mean is equal to 0 and variance is 1. I\'m using numpy\'s arrays so if it can already do it it\'s better, but I can implem
from sklearn.preprocessing import StandardScaler
standardized_data = StandardScaler().fit_transform(your_data)
Example:
>>> import numpy as np
>>> from sklearn.preprocessing import StandardScaler
>>> data = np.random.randint(25, size=(4, 4))
>>> data
array([[17, 12, 4, 17],
[ 1, 16, 19, 1],
[ 7, 8, 10, 4],
[22, 4, 2, 8]])
>>> standardized_data = StandardScaler().fit_transform(data)
>>> standardized_data
array([[ 0.63812398, 0.4472136 , -0.718646 , 1.57786412],
[-1.30663482, 1.34164079, 1.55076242, -1.07959124],
[-0.57735027, -0.4472136 , 0.18911737, -0.58131836],
[ 1.24586111, -1.34164079, -1.02123379, 0.08304548]])
Works well on large datasets.