 Do you know what it means to **flatten** an array? Flattening is the process of transforming a multidimensional array into a one-dimensional one. 

This transformation can be achieved using two different methods: 
- the first one we're already familiar with is the `.reshape(-1)` method with an argument of `-1`; 
- the other option is to use the `.flatten()` method. 

Now, let's have a look at both of these methods in practice.

Let's see how to use the `.reshape(-1)` method:

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 see how to use the `.flatten()` method:

import numpy as np

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

print(new_array)

NumPy is one of the basic packages for scientific computing in Python. The 'NumPy in a Nutshell' course will introduce you to such a powerful tool as NumPy, which is convenient for working with arrays of different sizes. After completing this course, you will be able to easily work with matrices, using various functions. In addition, during the course, you will learn basic methods for working with arrays that simplify code writing.

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().

Flattening

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