s = np.random.uniform(-1,0,1000)
import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(s, 15, normed=True)
plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
plt.show()
Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.uniform.html
import numpy as np
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
import matplotlib.pyplot as plt
plt.hist(x, bins=50)
plt.savefig('hist.png')
Reference: https://stackoverflow.com/a/43822468
import matplotlib.pyplot as plt
import numpy as np
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
hist, bins = np.histogram(x, bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.show()
fig, ax = plt.subplots()
ax.bar(center, hist, align='center', width=width)
fig.savefig("1.png")
Reference: https://stackoverflow.com/a/5328669
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