I am running a PCA analysis on a data set using Python's (v2.7.10) NumPy.  I validated that my program works by running the PCA analysis on a smaller dataset and then confirming that I get similar results using sklearn.decomposition.When I run my full dataset (with 60000 variables and 93 samples), I experience some strangeness.  After I construct my covariance matrix (which is 60000 x 60000), I compute the eigenvalues and eigenvectors using numpy.linalg.eig().  When I inspect the eigenvalues and eigenvectors, all the entries are exactly 0.    This leads me to believe that there is something strange with the behavior of numpy.linalg.eig() due to the large matrix size.  QUESTION : What is going on?  What assumptions are breaking down? How do I fix it? 
Finding eigenvectors and eigenvalues of large matrices in Python's numpy
python;numpy