python：sns distplot区域重叠

Question

How can I get the overlapping area of 2 sns.distplots?

Apart from the difference in mean (as below) I would like to add a number that descripes how different the (normalised) distributions are (for example 2 distributions could have the same mean but still look very different if they are not normal).

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

x1 = np.random.normal(size=2000)
x2 = np.random.normal(size=1000)+1

sns.distplot(x1, hist=False, kde=True, color="r", norm_hist=True)
sns.distplot(x2, hist=False, kde=True, color="b", norm_hist=True)

m1 = x1.mean()
m2 = x2.mean()

plt.title("m1={:2.2f}, m2={:2.2f} (diffInMean={:2.2f})".format(m1, m2, m1-m2))

plt.show(block=True)

Answer 1

If somebody is interested: I have approximated it now with an integral of the distributions (unfortunately not quite the 1-liner I was searching for):

data1 = np.random.normal(size=9000)
data2 = np.random.normal(size=5000, loc=0.5, scale=1.5)
num_bins = 100

xmin = min(data1.min(), data2.min())
xmax = max(data1.max(), data2.max())
bins = np.linspace(xmin, xmax, num_bins)
weights1 = np.ones_like(data1) / float(len(data1))
weights2 = np.ones_like(data2) / float(len(data2))

hist_1 = np.histogram(data1, bins, weights=weights1)[0]
hist_2 = np.histogram(data2, bins, weights=weights2)[0]

tvd = 0.5*sum(abs(hist_1 - hist_2))
print("overlap: {:2.2f} percent".format((1-tvd)*100))

plt.figure()
ax = plt.gca()
ax.hist(data1, bins, weights=weights1, color='red', edgecolor='white', alpha=0.5)[0]
ax.hist(data2, bins, weights=weights2, color='blue', edgecolor='white', alpha=0.5)[0]
plt.show()