So I saw someone use this code to find out how much overlap two normal distributions have.
from statistics import NormalDistribution NormalDist(mu=2.5, sigma=1).overlap(NormalDist(mu=5.0, sigma=1)) # 0.2112995473337106
AttributeError: 'numpy.ndarray' object has no attribute 'overlap'
so like y_l-y_l[n] is supposed to the data of each distribution
ov_l =  for i in range(0, int(s_lvl) - 1): y1 = y_l[i] y2 = y_l[i + 1] z = y1.overlap(y2) ov_l.append(round((z * 100.0, 1))) print(ov_l)
I'm not super familiar with python yet so I think I might have gotten this part wrong
y_l =  xx = np.linspace(0 , 3500000, 1000) for i in range(0, int(s_lvl)): y = norm.pdf(xx, mn_l[i], var_l[i]) y_l.append(y) #ov_l.append(round()) plt.plot(xx, y)
so s_lvl is supposed to be the number of distributions I want to see and mn_l and var_l is supposed be a list of the average of one level and the Standard Deviation for each level.
I was wondering if there was an easy way like x.overlap(y) to find out how much they overlap. if not, a way to find out how much overlap probability distributions have. Or I might be doing something wrong(most likely)