How to use scikit-learn PCA for features reduction and know which features are discarded

Question

I am trying to run a PCA on a matrix of dimensions m x n where m is the number of features and n the number of samples.

Suppose I want to preserve the nf fe

Answer 1

The answer marked above is incorrect. The sklearn site clearly states that the components_ array is sorted. so it can't be used to identify the important features.

components_ : array, [n_components, n_features] Principal axes in feature space, representing the directions of maximum variance in the data. The components are sorted by explained_variance_.

http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html