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

Function array()

In fact, there are various functions in NumPy for creating arrays. Now, we'll explore one of the most commonly used ones, namely np.array(). Below, you'll find an example of how to use this function:


              12345678
            
# Importing NumPy
import numpy as np

# Creating array
arr = np.array([1, 3, 5, 7, 9, 11, 13])

# Displaying array
print(arr)

Let's now determine the type of object that this function creates. We can do this using the well-known function type().

Note
The type() function takes an object of any type and returns its type. The argument can indeed be of any type: number, string, list, dictionary, tuple, function, class, module, etc.


              12345678
            
import numpy as np
arr = np.array([1, 3, 5, 7, 9, 11, 13])

# Displaying array
print(arr)

# Displaying the type of created array
print(type(arr))

We can see the type of the created array is ndarray. But what does that mean? ndarray - This object is a multidimensional homogeneous array with a predetermined number of elements.

Now it's time to practice!

Task

You have to create two NumPy arrays. The first one should look like this: [65, 2, 89, 5, 0, 1] and the second one should look like this: [1, 2, 3].
Display these arrays on the screen.
Display the type of these arrays on the screen.

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 1. Chapter 2

Function array()


              12345678
            
# Importing NumPy
import numpy as np

# Creating array
arr = np.array([1, 3, 5, 7, 9, 11, 13])

# Displaying array
print(arr)

Let's now determine the type of object that this function creates. We can do this using the well-known function type().

Note
The type() function takes an object of any type and returns its type. The argument can indeed be of any type: number, string, list, dictionary, tuple, function, class, module, etc.


              12345678
            
import numpy as np
arr = np.array([1, 3, 5, 7, 9, 11, 13])

# Displaying array
print(arr)

# Displaying the type of created array
print(type(arr))

We can see the type of the created array is ndarray. But what does that mean? ndarray - This object is a multidimensional homogeneous array with a predetermined number of elements.

Now it's time to practice!

Task

You have to create two NumPy arrays. The first one should look like this: [65, 2, 89, 5, 0, 1] and the second one should look like this: [1, 2, 3].
Display these arrays on the screen.
Display the type of these arrays on the screen.

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 1. Chapter 2

Function array()


              12345678
            
# Importing NumPy
import numpy as np

# Creating array
arr = np.array([1, 3, 5, 7, 9, 11, 13])

# Displaying array
print(arr)

Let's now determine the type of object that this function creates. We can do this using the well-known function type().

Note
The type() function takes an object of any type and returns its type. The argument can indeed be of any type: number, string, list, dictionary, tuple, function, class, module, etc.


              12345678
            
import numpy as np
arr = np.array([1, 3, 5, 7, 9, 11, 13])

# Displaying array
print(arr)

# Displaying the type of created array
print(type(arr))

We can see the type of the created array is ndarray. But what does that mean? ndarray - This object is a multidimensional homogeneous array with a predetermined number of elements.

Now it's time to practice!

Task

You have to create two NumPy arrays. The first one should look like this: [65, 2, 89, 5, 0, 1] and the second one should look like this: [1, 2, 3].
Display these arrays on the screen.
Display the type of these arrays on the screen.

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

Everything was clear?

Thanks for your feedback!


              12345678
            
# Importing NumPy
import numpy as np

# Creating array
arr = np.array([1, 3, 5, 7, 9, 11, 13])

# Displaying array
print(arr)

Let's now determine the type of object that this function creates. We can do this using the well-known function type().

Note
The type() function takes an object of any type and returns its type. The argument can indeed be of any type: number, string, list, dictionary, tuple, function, class, module, etc.


              12345678
            
import numpy as np
arr = np.array([1, 3, 5, 7, 9, 11, 13])

# Displaying array
print(arr)

# Displaying the type of created array
print(type(arr))

We can see the type of the created array is ndarray. But what does that mean? ndarray - This object is a multidimensional homogeneous array with a predetermined number of elements.

Now it's time to practice!

Task

You have to create two NumPy arrays. The first one should look like this: [65, 2, 89, 5, 0, 1] and the second one should look like this: [1, 2, 3].
Display these arrays on the screen.
Display the type of these arrays on the screen.

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

Section 1. Chapter 2

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