code: def perform_pca(A):
      
      # First subtract the mean
      M = (A-numpy.mean(A.T, axis=1)).T
      # Get eigenvectors and values of covariance matrix
      return numpy.linalg.eig(numpy.cov(M))
docs: Computes eigenvalues and eigenvectors of covariance matrix of A.
      The rows of a correspond to observations, the columns to variables.