Course Content

NumPy in a Nutshell

1. Getting Started with NumPy

What is NumPy?Function array()Basic Array Creation Quiz

2. Dimensions in Arrays

1-D Arrays Mathematical Operations with 1-D Arrays 2-D Arrays 3-D Arrays Multidimensional Arrays Quiz

3. Indexing and Slicing

Access Array Elements Match Math Operations Access 2-D and 3-D Arrays Match the Slicing Negative Indexing Match the Indices Get One Dimensional Array Using Slice and Only Positive Indexes Get One Dimensional Array Using Only Negative Indexes

4. Important Functions

Reshaping Flattening Joining Sorting Arrays Copying Arrays

Flattening

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:


              123456
            
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:


              123456
            
import numpy as np

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

print(new_array)

Let's practice!

Task

Swipe to show code editor

Consider the following array:

[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]]

You should transform it into the following array:

[1 2 3 4 5 6 7 8 9 10 11 12].

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 2

Flattening

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:


              123456
            
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:


              123456
            
import numpy as np

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

print(new_array)

Let's practice!

Task

Swipe to show code editor

Consider the following array:

[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]]

You should transform it into the following array:

[1 2 3 4 5 6 7 8 9 10 11 12].

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 2

Switch to desktop for real-world practiceContinue from where you are using one of the options below