With **three-dimensional arrays**, everything is clear and quite logical. These kind of arrays consist of elements that are **two-dimensional arrays**.
Let's look at the practice, so it will be easier to perceive.

There is an example of how we can create a 3-D array with two 2-D arrays, **both containing** two arrays with the values `5, 7, 8, 2` and `13, 15, 16, 18`:

import numpy as np

# Creating array
arr = np.array([[[5, 7, 8, 2], [13, 15, 16, 18]], [[5, 7, 8, 2], [13, 15, 16, 18]]])

# Displaying array
print(arr)

In this section we will get acquainted with what the NumPy library is, as well as learn how to create an array.

In this section we will get acquainted with arrays of different dimensions, and understand the difference between them.

In this section we should recall what slices are and learn how to make them for arrays of different dimensions. We shall also learn to refer to elements in an array using their indexes.

In this section we will learn how to reshape arrays and also how to concatenate an array. Moreover we will learn how to sort an array. Also we will learn about such an interesting method that is often used for arrays, namely: copy().