在python中画正态分布图像的实例

import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt


def demo1():
  mu ,sigma = 0, 1
  sampleNo = 1000
  np.random.seed(0)
  s = np.random.normal(mu, sigma, sampleNo)

  plt.hist(s, bins=100, normed=True)
  plt.show()

import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt

def demo2():
  mu, sigma , num_bins = 0, 1, 50
  x = mu + sigma * np.random.randn(1000000)
  # 正态分布的数据
  n, bins, patches = plt.hist(x, num_bins, normed=True, facecolor = 'blue', alpha = 0.5)
  # 拟合曲线
  y = mlab.normpdf(bins, mu, sigma)
  plt.plot(bins, y, 'r--')
  plt.xlabel('Expectation')
  plt.ylabel('Probability')
  plt.title('histogram of normal distribution: $\mu = 0$, $\sigma=1$')

  plt.subplots_adjust(left = 0.15)
  plt.show()