StandardScaler sklearn get params normalization
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(data)
scaled_data = scaler.transform(data)
means = scaler.mean_
vars = scaler.var_
# for later usage of means and vars
def scale_data(array,means=means,stds=vars **0.5):
return (array-means)/stds
scale_new_data = scale_data(new_data)