x
import pandasimport scipyimport numpyfrom sklearn.preprocessing import StandardScalerfileurl = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'names = ['septal_length','sepal_with','petal_length','pedal_width','class']data = pandas.read_csv(fileurl, names=names)array = data.values# input/output component separationX = array[:,0:4]Y = array[:,4]scaler = StandardScaler().fit(X)standardX = scaler.transform(X)# summarize transformed datanumpy.set_printoptions(precision=2)print(standardX[0:20,:])#The Iris data is transformed to a standard deviation of 1 and a mean of 0.
[[-0.9 1.03 -1.34 -1.31]
[-1.14 -0.12 -1.34 -1.31]
[-1.39 0.34 -1.4 -1.31]
[-1.51 0.11 -1.28 -1.31]
[-1.02 1.26 -1.34 -1.31]
[-0.54 1.96 -1.17 -1.05]
[-1.51 0.8 -1.34 -1.18]
[-1.02 0.8 -1.28 -1.31]
[-1.75 -0.36 -1.34 -1.31]
[-1.14 0.11 -1.28 -1.44]
[-0.54 1.49 -1.28 -1.31]
[-1.26 0.8 -1.23 -1.31]
[-1.26 -0.12 -1.34 -1.44]
[-1.87 0.12 -1.51 -1.44]
[-0.05 2.19 -1.46 -1.31]
[-0.17 3.11 -1.28 -1.05]
[-0.54 1.96 -1.4 -1.05]
[-0.9 1.03 -1.34 -1.18]
[-0.17 1.73 -1.17 -1.18]
[-0.9 1.73 -1.28 -1.18]]
[-1.14 -0.12 -1.34 -1.31]
[-1.39 0.34 -1.4 -1.31]
[-1.51 0.11 -1.28 -1.31]
[-1.02 1.26 -1.34 -1.31]
[-0.54 1.96 -1.17 -1.05]
[-1.51 0.8 -1.34 -1.18]
[-1.02 0.8 -1.28 -1.31]
[-1.75 -0.36 -1.34 -1.31]
[-1.14 0.11 -1.28 -1.44]
[-0.54 1.49 -1.28 -1.31]
[-1.26 0.8 -1.23 -1.31]
[-1.26 -0.12 -1.34 -1.44]
[-1.87 0.12 -1.51 -1.44]
[-0.05 2.19 -1.46 -1.31]
[-0.17 3.11 -1.28 -1.05]
[-0.54 1.96 -1.4 -1.05]
[-0.9 1.03 -1.34 -1.18]
[-0.17 1.73 -1.17 -1.18]
[-0.9 1.73 -1.28 -1.18]]