Do you know what **flattening** an array is? What does this mean? This is a transformation of any multidimensional array to one-dimensional. Flattening can be implemented using two different functions, the first of which we already know:
 - `reshape(-1)` function with an argument of -1;
 - and the next one is `flatten()`.
Let's take a look at both these functions in practice.

Let's take a look at how to use `reshape(-1)` function.

import numpy as np

array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]])
new_array = array.reshape(-1)

print(new_array)

Let's take a look at how to use `flatten()` function.

import numpy as np

array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]])
new_arr = array.flatten()

print(new_array)

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().