import pandas

import scipy

import numpy

from sklearn.preprocessing import Normalizer

​

fileurl = '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 separation

X = array[:,0:4]

Y = array[:,4]

scaler = Normalizer().fit(X)

normalizedX = scaler.transform(X)

​

# summarize transformed data

numpy.set_printoptions(precision=2)

print(normalizedX[0:10,:])

​

#All rows now have a maximum length of 1.

​