NumPy: difference between linalg.eig() and linalg.eigh()

In a Python 3 application I'm using NumPy to calculate eigenvalues and eigenvectors of a symmetric real matrix.

Here's my demo code:

import numpy as np
a = np.random.rand(3,3)  # generate a random array shaped (3,3)

a = (a + a.T)/2  # a becomes a random simmetric matrix    

evalues1, evectors1 = np.linalg.eig(a)

evalues2, evectors2 = np.linalg.eigh(a)

Except for the signs, I got the same eigenvectors and eigenvalues using np.linalg.eig and np.linalg.eigh. So, what's the difference between the two methods?

Thanks

EDIT: I've read the docs here https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eig.html and here https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eigh.html but still I can not understand why I should use eigh() when I have a symmetric array.