Python random.gauss() Method

Therandom.gauss() method in Python generates random numbers that follows the Gaussian Distribution, also called as Normal Distribution. It is a family of continuous probability distributions, depends on the values of two parametersmu andsigma. Where,mu is the mean andsigma is the standard deviation of the Gaussian distribution. This distribution is often used in statistics, data analysis, and various fields of science, including natural and social sciences.

This method is faster than therandom.normalvariate() method for generating random numbers from a Gaussian (normal) distribution.

Syntax

Following is the syntax of the Pythonrandom.gauss() method −

random.gauss(mu, sigma)

Parameters

This method accepts the following parameters −

• mu: This is the mean of the Gaussian distribution. It defines the center of the distribution around which the data points are distributed.

• sigma: This is the standard deviation of the Gaussian distribution. It determines the spread of the distribution; a larger standard deviation results in a wider distribution.

Return Value

This method returns a random number that follows the Gaussian distribution.

Example 1

Let's see a basic example of using the Pythonrandom.gauss() method for generating a random number from a Gaussian distribution with a mean of 0 and a standard deviation of 1.

import random# meanmu = 0  # standard deviationsigma = 1  # Generate a Gaussian-distributed random numberrandom_number = random.gauss(mu, sigma)# Print the outputprint("Generated random number from Gaussian-distribution:",random_number)

Following is the output −

Generated random number from Gaussian-distribution: 0.5822883447626581

Note: The output generated will vary each time you run the program due to its random nature.

Example 2

This example generates a list of 10 random numbers that follows the Gaussian distribution using therandom.gauss() method.

import random# meanmu = -2 # standard deviationsigma = 0.5  result = []# Generate a list of random numbers from the Gaussian distributionfor i in range(10):    result.append(random.gauss(mu, sigma))print("List of random numbers from Gaussian distribution:", result)

While executing the above code you will get the similar output like below −

List of random numbers from Gaussian distribution: [-1.6883491787714924, -2.2670950449189835, -1.68497316636885, -2.62956757323328, -1.8888429377204585, -2.6139116413700765, -2.287545626016553, -1.5470101615690448, -2.259090829777413, -1.9380772732164955]

Example 3

Here is another example that uses therandom.gauss() method, and demonstrates how changing the mean and standard deviation affects the shape of the Gaussian distribution.

import randomimport matplotlib.pyplot as plt# Define a function to generate and plot data for a given mu and sigmadef plot_gaussian(mu, sigma, label, color):    # Generate Gaussian-distributed data    data = [random.gauss(mu, sigma) for _ in range(10000)]    # Plot histogram of the generated data    plt.hist(data, bins=100, density=True, alpha=0.6, color=color, label=f'(mu={mu}, sigma={sigma})')fig = plt.figure(figsize=(7, 4))# Plotting for each set of parametersplot_gaussian(0, 0.2, '0, 0.2', 'blue')plot_gaussian(0, 1, '0, 1', 'red')plot_gaussian(0, 2, '0, 2', 'yellow')plot_gaussian(-2, 0.5, '-2, 0.5', 'green')# Adding labels and titleplt.title('Gaussian Distributions')plt.legend()# Show plotplt.show()

The output of the above code is as follows −