Principle Component Analysis (PCA) is a dimensionality reduction technique. It is used to remove less useful (less correlated) features from the dataset. It is more useful in unsupervised machine learning, where we work on unlabelled data.
The features that PCA object has determined during fitting are in pca.components_. The vector space orthogonal to the one spanned by pca.components_ is discarded.
PCA does not "discard" or "retain" any of your pre-defined features (encoded by the columns you specify). It mixes all of them (by weighted sums) to find orthogonal directions of maximum variance.
If this is not the behavior you are looking for, then PCA dimensionality reduction is not the way to go. For some simple general feature selection methods, you can take a look at sklearn.feature_selection.
# Principal Component Analysis
from numpy import array
from sklearn.decomposition import PCA
# define a matrix
A = array([[1, 2], [3, 4], [5, 6]])
# create the PCA instance
pca = PCA(2)
# fit on data
# access values and vectors
# transform data
B = pca.transform(A)
Hope this answer helps.
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