%pylab inline

from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

digits = load_digits()

X = digits.data
y = digits.target
target_names = digits.target_names

pca = PCA(n_components=2)
X_r = pca.fit(X).transform(X)

plt.figure()
for c, i, target_name in zip(['b', 'r', 'g', 'c', 'm', 'y', 'k', 'burlywood',
                              'chartreuse', '0.75'],
                             [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], target_names):
    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=target_name)
plt.legend()
plt.show()