import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np


### NOTE: make one of these an exact density or KDE to exactly match your plot intention.
randns1 = np.random.normal(size=10000)
counts1, bins1 =  np.histogram(randns1, bins=30)

randns2 = np.random.normal(size=10000)
counts2, bins2 =  np.histogram(randns2, bins=30)


data = np.array([counts1,counts2])

## other data
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
colors = ["r","g","b"]*10

## Draw 3D hist
ncnt, nbins = data.shape[:2]
xs_new = np.arange(-3,3,6/30)
for i in range(ncnt):
    ys = data[i]
    cs = [colors[i]] * nbins
    ax.bar(xs_new, ys.ravel(), zs=i, zdir='x', color=cs, alpha=0.8)
    ax.set_axis_off()


x = np.linspace(0, 1, 1000)
y = randns1[::10]
ax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)')


# ax.set_xlabel('idx')
# ax.set_ylabel('bins')
# ax.set_zlabel('nums')
plt.show()