# Creating a normal distribution
import numpy as np 
mu, sigma = 0, 0.1 # mean and standard deviation
s = np.random.normal(mu, sigma, 1000)

# Visualizing the Normal Distribution
import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(s, 30, density=True)
plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
         np.exp(- (bins - mu)**2 / (2 * sigma**2)), linewidth=2, color='r')
plt.show()