import numpy as np
from sklearn import preprocessing

data = np.array([[ 3, -1.5, 2, -5.4],
 [ 0, 4, -0.3, 2.1],
 [ 1, 3.3, -1.9, -4.3]])

# mean removal
data_standardized = preprocessing.scale(data)
print "\nMean =", data_standardized.mean(axis=0)
print "Std deviation =", data_standardized.std(axis=0)

# min max scaling
data_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
data_scaled = data_scaler.fit_transform(data)
print "\nMin max scaled data:\n", data_scaled

# normalization
data_normalized = preprocessing.normalize(data, norm='l1')
print "\nL1 normalized data:\n", data_normalized

# binarization
data_binarized = preprocessing.Binarizer(threshold=1.4).transform(data)
print "\nBinarized data:\n", data_binarized

# one hot encoding
encoder = preprocessing.OneHotEncoder()
encoder.fit([[0, 2, 1, 12], [1, 3, 5, 3], [2, 3, 2, 12], [1, 2, 4, 3]])
encoded_vector = encoder.transform([[2, 3, 5, 3]]).toarray()
print "\nEncoded vector:\n", encoded_vector