Normal/Gaussian Distribution Explained using Python

Hi! The main idea with these publications it is to cover the main topics using Python.

Simple and direct learning, remembering that this will not replace foundations of coding and a few hours of study and effort for putting into practice

For further information, visit Numpy page.

Source of image: Author

Normal/Gaussian Distribution

Also called “Bell shaped curve”:

Source of image: sciences.usca.edu

Basically, we can see that the curve represents “Probability density” and “Distance from mean” on axis Y and X, respectively.

A simple example:

• Baby weights in Normal distribution

Source of image: Research Gate

The average weight of the babies centers between 3200 and 3600 associated with highest frequency, consider it as the “common”. The extremities of the normal distribution represents “rare” cases where babies can born with 1600 grams or 4800 grams.

Python example:

Note:

• Mean: μ
• Standard deviation: σ
• Variance: σ²

Draw samples from the distribution:

Generate a random normal distribution of size 2x3 with mean at 1 and standard deviation of 2:

Visualization of Normal Distribution

I hope that this article was helpful more content will be posted soon.