How would I chi-squared test these simple results from A/B experiment?
If what you're trying to answer is if the action taken by a user (watch, interact, nothing) is influenced by the group they are in (A or B) you can use the chi2 test of independence.

For that you can use the scipy.stats.chi2_contingency function:

a = [327445, 271602, 744702]
b = [376455, 140737, 818204]

chi2, pvalue, _, _ = scipy.stats.chi2_contingency([a, b])


In this case it returns a chi2 test statistic of 48376.48 and a p-value of 0.0, so the null hypothesis ("action is independent of group") is rejected.

You can also use the scipy.stats.chisquare function to arrive at the same results, but other than with the chi2_contingency function you'll have to calculate the "expected frequencies" yourself. The data you have recorded are your observed frequencies:

obs = np.array([a, b]).astype(float)


(Note that I converted the numbers to float, as the chisquare function will run into some weird integer overflows otherwise...!?)

The expected frequencies are calculated like that:

exp = np.outer(obs.sum(axis=1), obs.sum(axis=0)) / obs.sum()


Finally, calling

chi2, pvalue = scipy.stats.chisquare(obs.ravel(), exp.ravel(), ddof=sum(obs.shape)-2)


returns the same chi2 test statistic and p-value as before.