import numpy as np
import matplotlib.pyplot as plt
xs = np.random.normal(0, 1, size=(5000))
fig = plt.figure()
for i,b in enumerate([10, 50, 100, 200],1):
    ax = fig.add_subplot(2,2,i)
    plt.hist(xs, bins=b)
plt.show()

hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, *, data=None, **kwargs)

types = ['bar', 'step', 'stepfilled']
fig = plt.figure()
for i,t in enumerate(types,1):
    ax = fig.add_subplot(1,3,i)
    plt.hist(xs, bins=50, histtype=t, rwidth=0.5)
plt.show()

bins = [10, 30, 100]
ws = [1, 0.7, 0.5]
for b,w in zip(bins, ws):
    print(b,w)
    plt.hist(xs, bins=b, density=True, 
        histtype='barstacked', rwidth = w, alpha=w)
plt.show()

N = 10000
labels = ["norm", "power", "poisson"]
data = np.array([
    np.random.normal(0, 1, size=N)**2,
    np.random.power(5, size=N),
    np.random.uniform(0, 1, size=N)
]).T
plt.hist(data, 50, density=True, range=(0,1), label=labels)
plt.legend()
plt.show()