# Principal component analysis ('function' object is not subscriptable)

I am trying to use a principal component analysis to determine the most important features in my dataset. When I run the code below, I receive an error saying "PCA object is not subscribable." I have attempted to add various arguments into the PCA() function but receive the same error any time.

from sklearn.decomposition import PCA

pca =   PCA()
x_pca = pca.fit(X_train)

def myplot(score,coeff,labels=None):
xs = score[:,0]
ys = score[:,0]
n = coeff.shape[0]
scalex = 1.0/(xs.max() - xs.min())
scaley = 1.0/(ys.max() - ys.min())
plt.scatter(xs * scalex,ys * scaley, c = y)
for i in range(n):
plt.arrow(0, 0, coeff[i,0], coeff[i,1],color = 'r',alpha = 0.5)
if labels is None:
plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, "Var"+str(i+1), color = 'g', ha = 'center', va = 'center')
else:
plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color = 'g', ha = 'center', va = 'center')
plt.xlim(-1,1)
plt.ylim(-1,1)
plt.xlabel("PC{}".format(1))
plt.ylabel("PC{}".format(2))
plt.grid()

#Call the function. Use only the 2 PCs.
myplot(x_pca[:,0:2],np.transpose(pca.components_[0:2, :]))
plt.show()
• Please note that PCA stands for Principal Component Analysis
– noe
Feb 9 at 15:59