【小白学编程Python方向】项目练习第4期（截止5.16）

#numpy, matplotlib import numpy      from matplotlib import mlab from matplotlib import pyplot mu = 100      sigma = 15   x = mu + sigma * numpy.random.randn(1000)     num_bins = 50     n, bins, patches = pyplot.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)     y = mlab.normpdf(bins, mu, sigma)     pyplot.plot(bins, y, 'r--')     pyplot.xlabel('x-axis')     pyplot.ylabel('y-axis')     pyplot.title('$\mu=100$, $\sigma=15$')     pyplot.subplots_adjust(left=0.15)     pyplot.show() 

import numpy as np import matplotlib.pyplot as plt import scipy.stats as st mu, sigma = 100, 15 s = st.norm(mu, sigma).rvs(1000) s_fit = np.linspace(s.min(), s.max()) plt.title('$\mu=100$,$\sigma=15$',color='r') count, bins, _ = plt.hist(s, 30, normed=True, color='g') plt.plot(s_fit, st.norm(mu, sigma).pdf(s_fit), lw=1, c='r') plt.show()

import numpy as np import matplotlib.pyplot as plt mu, sigma = 100, 15 np_list = np.random.normal(mu, sigma, 1000) plt.title('$\mu=100$,$\sigma=15$',color='r') count, bins, _ = plt.hist(np_list, 50, normed=True, color='g') plt.plot(bins, 1./(np.sqrt(2*np.pi)*sigma)*np.exp(-(bins-mu)**2/(2*sigma**2)), lw=1, c='r') plt.show()