import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
mean = 100
std_dev = 15
distribution = norm(loc=mean, scale=std_dev)
data = distribution.rvs(10000) # Create a Norm Distribution data set
plt.hist(data, 50, density=1, facecolor='b', histtype='stepfilled',alpha=0.5);
plt.text(60, .025, r'$\mu={},\ \sigma={}$'.format(std_dev,mean))
plt.xlabel('X')
plt.ylabel('Probability')
plt.title('Histogram of Norm Distribution')
plt.axis([40, 160, 0, 0.03]);
mu, sigma = 100, 15
data_set = mu + sigma * np.random.randn(10000)
plt.hist(data_set, 50, density=1, facecolor='g', alpha=0.5)
plt.xlabel('X')
plt.ylabel('Probability')
plt.title('Histogram of Norm Distribution')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show();
# probability density function (pdf)
x = np.linspace(norm.ppf(0.01),norm.ppf(0.99), 100)
fig, ax = plt.subplots(1, 1)
ax.plot(x, norm.pdf(x),'r-', lw=5, alpha=0.6, label='norm pdf');
rv = norm() #returns a “frozen” RV object holding the given parameters fixed.
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf');
r = norm.rvs(size=1000)
ax.hist(r, 20, density=1,facecolor='g', alpha=0.2);