Normal (Gaussian) Distribution

 Normal Distribution

The Normal Distribution (also known as the Gaussian Distribution, named after mathematician Carl Friedrich Gauss) is one of the most commonly used probability distributions.

It models many natural phenomena such as IQ scores, heartbeat rates, and more.

Creating a Normal Distribution in Python

You can use the random.normal() method from NumPy to generate a normal distribution.

This method takes three main parameters:

loc: The mean (center) of the distribution.

scale: The standard deviation (controls the spread or width of the bell curve).

size: The shape of the output array.

Program: 

Generate a 2x3 normal distribution array (default mean=0, std=1):

from numpy import random

x = random.normal(size=(2, 3))

print(x)

Output:

[[ 0.234 -0.875  0.491]

 [-1.293  0.732  0.084]]

Note: Your output will be different every time because it’s random.

Program: 

Generate a normal distribution with a mean of 1 and standard deviation of 2:

from numpy import random

x = random.normal(loc=1, scale=2, size=(2, 3))

print(x)

Output:

[[ 1.532  2.473 -1.218]

 [ 0.432 -0.752  4.134]]

Note: Your output will be different on every run because it's randomly generated.

If you want to reproduce the same result every time, you can set a seed like this:

Visualizing a Normal Distribution

Here's how to visualize a normal distribution using Seaborn and Matplotlib:

from numpy import random

import matplotlib.pyplot as plt

import seaborn as sns

sns.displot(random.normal(size=1000), kind="kde")

plt.show()