Arrange the results as a $2\times 2$-table and use `chi2_contigency` from SciPy in Python to obtain the correct $p$-value (here shown without continuity correction):

    import numpy as np
    from scipy.stats import chi2_contingency, fisher_exact
    
    obs = np.array([[8157, 8],[7906,10]])

    g, p, dof, expctd = chi2_contingency(obs, correction = False)

    p
    
    0.59094761107842753

So the $p$-value is roughly $0.5909$.

A viable alternative would be to use Fisher's exact test. This can be done using `fisher_exact` from SciPy:

    oddsr, p_fish = fisher_exact(obs)
    
    oddsr
    
    1.289685049329623
    
    p_fish
    
    0.64294290970149048

The odds ratio is $1.29$ with an associated $p$-value from Fisher's exact test of $0.643$.