random.gauss() function in Python

random module is used to generate random numbers in Python. Not actually random, rather this is used to generate pseudo-random numbers. That implies that these randomly generated numbers can be determined. gauss() is an inbuilt method of the random module. It is used to return a random floating point number with gaussian distribution.

Example:

import random 
mu = 100
sigma = 50
print(random.gauss(mu, sigma)) 

Output :

127.80261974806497

Explanation: This code generates and prints a random number from a Gaussian distribution with a mean (mu) of 100 and a standard deviation (sigma) of 50 using the random.gauss() function. The result will be a value close to 100 but can vary within a range due to the standard deviation.

Syntax

random.gauss(mu, sigma)

Parameters

• mu: mean
• sigma: standard deviation

Return Value

• Returns a random gaussian distribution floating number

Examples of random.gauss() function

1. Gaussian Distribution Plot

We can generate the number multiple times and plot a graph to observe the gaussian distribution.

import random 
import matplotlib.pyplot as plt 
	
# store the random numbers in a list 
nums = [] 
mu = 100
sigma = 50
	
for i in range(100): 
	temp = random.gauss(mu, sigma) 
	nums.append(temp) 
		
# plotting a graph 
plt.plot(nums) 
plt.show() 

Output :

Explanation: This code generates 100 random numbers following a Gaussian distribution with a mean of 100 and a standard deviation of 50. It stores these numbers in a list and then plots the values using matplotlib to visualize the distribution.

2. Gaussian Distribution Histogram

We can create a histogram to observe the density of the gaussian distribution.

import random 
import matplotlib.pyplot as plt 
	
# store the random numbers in a list 
nums = [] 
mu = 100
sigma = 50
	
for i in range(10000): 
	temp = random.gauss(mu, sigma) 
	nums.append(temp) 
		
# plotting a graph 
plt.hist(nums, bins = 200) 
plt.show() 

Output :

Explanation: This code generates 10,000 random numbers following a Gaussian distribution with a mean of 100 and a standard deviation of 50. It stores the numbers in a list and then plots a histogram using matplotlib to visualize the distribution with 200 bins.