Why are eigenvectors produced by np.linalg.eig different than the PCA components stored in the instance of the PCA object?
Two problems:

Your sorting is incorrect:

eVec = eVec1[np.flip(np.argsort(eVal1))]

sorts the rows of the matrix, but you want to sort the columns.  Replacing this with
eVec = eVec1[:, np.flip(np.argsort(eVal1))]

fixes this issue.

The sign of the eigenvectors are sometimes opposite.  (That's fine, being an eigenvector is scale invariant, and while both np.linalg and sklearn use unit eigenvectors, that still leaves a choice of two.  I'm not sure how the packages end up picking one.)