betas_plot.py

import matplotlib.pyplot as plt

def calc_beta_mode(a, b):
    '''this function calculate the mode (peak) of the Beta distribution'''
    return (a-1)/(a+b-2)

def plot(betas, names, linf=0, lsup=0.01):
    '''this function plots the Beta distribution'''
    x=np.linspace(linf,lsup, 100)
    for f, name in zip(betas,names) :
        y=f.pdf(x) #this for calculate the value for the PDF at the specified x-points
        y_mode=calc_beta_mode(f.args[0], f.args[1])
        y_var=f.var() # the variance of the Beta distribution
        plt.plot(x,y, label=f"{name} sample, conversion rate: {y_mode:0.1E} $\pm$ {y_var:0.1E}")
        plt.yticks([])
    plt.legend()
    plt.show()

plot([beta_C, beta_T], names=["Control", "Test"])