normalize data python

>>> from sklearn import preprocessing
>>>
>>> data = [100, 10, 2, 32, 31, 949]
>>>
>>> preprocessing.normalize([data])
array([[0.10467389, 0.01046739, 0.00209348, 0.03349564, 0.03244891,0.99335519]])

import pandas as pd
from sklearn import preprocessing

x = df.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df = pd.DataFrame(x_scaled)

# Scaling features to a range using MinMaxScaler

X_train = np.array([[ 1., -1.,  2.],
                    [ 2.,  0.,  0.],
                    [ 0.,  1., -1.]])

min_max_scaler = preprocessing.MinMaxScaler()
X_train_minmax = min_max_scaler.fit_transform(X_train)
X_train_minmax
# array([[0.5		, 0.		, 1.	    ],
#        [1.		, 0.5		, 0.33333333],
#        [0.		, 1.		, 0.		]])

X_test = np.array([[-3., -1.,  4.]])
X_test_minmax = min_max_scaler.transform(X_test)
X_test_minmax
# array([[-1.5		,	0.		, 	1.66666667]])

min_max_scaler.scale_
# array([0.5       , 0.5       , 0.33...])

min_max_scaler.min_
# array([0.       , 0.5       , 0.33...])