Understanding Normal Distribution and its Properties using Python A Normal or Gaussian distribution is used to represent continuous random variables. BMI of people, height of people amongst other phenomena tend to follow a Normal distribution. It is generally used to describe a lot of natural phenomena around us. A normal distribution generally follows a bell curve. Let's see this in action. A normal distribution is defined by 2 parameters viz. Mean and Standard Deviation . This is how you can define this distribution using the Stats functionality from Scipy . import numpy as np from matplotlib import pyplot as plt from scipy.stats import norm import scipy x= np.linspace(0,700,1000000)##Create evenly spaced numbers from 0 to 400 r1 = norm.rvs(loc=350,scale=50,size=1000000) ###Create samples with mean=350 and stdev=50 Notice the rvs attribute of norm . We will talk about it in a while. Let's see how the plot for this distribution look...