import statsmodels.stats.api as sms
baseline_cvr=0.1
alpha=0.05
power=0.8
mini_diff=0.1*baseline_cvr
effect_size=sms.proportion_effectsize(baseline_cvr, baseline_cvr+mini_diff)
sample_size=sms.NormalIndPower().solve_power(effect_size=effect_size, power=power, alpha=alpha, ratio=1)
print('Required sample size ~ {0:.1f}'.format(sample_size) + ' per group')
#Output:
#Required sample size ~ 14744.1 per group