from sklearn.decomposition import PCA
import numpy as np

X = np.arange(20).reshape((5,4))

print("Separate")
XT = X.copy()
pcaT = PCA(n_components=2, copy=True)
print("Original: ", XT)
results = pcaT.fit(XT).transform(XT)
print("New: ", XT)
print("Results: ", results)

print("nCombined")
XF = X.copy()
pcaF = PCA(n_components=2, copy=True)
print("Original: ", XF)
results = pcaF.fit_transform(XF)
print("New: ", XF)
print("Results: ", results)

########## Results
Separate
Original:  [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]
 [16 17 18 19]]
New:  [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]
 [16 17 18 19]]
Results:  [[  1.60000000e+01  -2.66453526e-15]
 [  8.00000000e+00  -1.33226763e-15]
 [  0.00000000e+00   0.00000000e+00]
 [ -8.00000000e+00   1.33226763e-15]
 [ -1.60000000e+01   2.66453526e-15]]

Combined
Original:  [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]
 [16 17 18 19]]
New:  [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]
 [16 17 18 19]]
Results:  [[  1.60000000e+01   1.44100598e-15]
 [  8.00000000e+00  -4.80335326e-16]
 [ -0.00000000e+00   0.00000000e+00]
 [ -8.00000000e+00   4.80335326e-16]
 [ -1.60000000e+01   9.60670651e-16]]