test_with_sklearn_pca.py

import matplotlib.pyplot as plt 
from sklearn.datasets import make_classification
from sklearn.decomposition import PCA 
import numpy as np

X1, Y2 = make_classification(n_features=2, n_informative=2, n_redundant=0,
    n_classes=1,
    n_clusters_per_class=1)

x0 = X1[:,0]
y0 = X1[:,1]

plt.subplot(211)
plt.scatter(X1[:,0], X1[:,1])

M = np.column_stack((x0,y0))
pca = PCA(n_components=2)
pca.fit(M)
print(pca.explained_variance_)
print(pca.explained_variance_ratio_)
transformed = pca.transform(M)
print(len(transformed))

plt.subplot(212)
plt.scatter(transformed[:,0], transformed[:,1])
plt.show()