NumPy Tutorial · NumPy Random

Normal Distribution

Learn all about Normal Distribution in this comprehensive tutorial.

5 min read advanced

The Normal Distribution is one of the most important distributions.

It is also called the Gaussian Distribution after the German mathematician Carl Friedrich Gauss.

It fits the probability distribution of many events, eg. IQ Scores, Heartbeat etc.

Use the random.normal() method to get a Normal Data Distribution.

It has three parameters:

loc - (Mean) where the peak of the bell exists.

scale - (Standard Deviation) how flat the graph distribution should be.

size - The shape of the returned array.

python

from numpy import randomx = random.normal(size=(2, 3))print(val_1)

python

from numpy import randomx = random.normal(loc=1, scale=2, size=(2, 3))
print(val_1)

Visualization of Normal Distribution

python

from numpy import randomimport matplotlib.pyplot as pltimport seaborn as sns
  sns.displot(random.normal(size=1000), kind="kde")plt.show()

Note: Note: The curve of a Normal Distribution is also known as the Bell Curve because of the bell-shaped curve.

2 questions

Which of the following is true about Normal Distribution?

It is strictly typedIt is evaluated at runtimeIt requires explicit memory managementIt is deprecated

What is the most common pitfall when working with Normal Distribution?

SyntaxErrorIndentationErrorNameErrorTypeError

Answer all questions to submit.