[Python] Why the left matrix of single value decomposition obtained by scipy.linalg.svd() and...

I tried to get eigen vectors by single value decomposition using scipy.linalg.svd() which gives me three outputs the left matrix U, diagonal matrix S, right matrix Vh. Where U is supposed to be eigenvectors for symmetric positive definite matrix.
But eigenvectors obtained from scipy.linalg.eig() are different from U or the left matrix. Different in a sense that they have same absolute values but different signs for some of the values.

import scipy
import numpy as np
mat = np.array([[3,2],[2,3]])
U, S, Vh = scipy.linalg.svd(mat)
eigenvalues, eigenvectors = scipy.linalg.eig(mat)

print(U)
>>> [[-0.70710678 -0.70710678]
[-0.70710678 0.70710678]]
print(eigenvectors)
>>> [[ 0.70710678 -0.70710678]
[ 0.70710678 0.70710678]]


Maybe I am missing some mathematical point. It would be very helpful if someone explain it to me why this happens.

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