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NumPy in a Nutshell
course content

Course Content

NumPy in a Nutshell

NumPy in a Nutshell

1. Getting Started with NumPy
What is NumPy?Function array()Basic Array Creation Quiz
2. Dimensions in Arrays
1-D ArraysMathematical Operations with 1-D Arrays2-D Arrays3-D ArraysMultidimensional Arrays Quiz
3. Indexing and Slicing
Access Array ElementsMatch Math Operations Access 2-D and 3-D ArraysMatch the Slicing Negative IndexingMatch the IndicesGet One Dimensional Array Using Slice and Only Positive Indexes Get One Dimensional Array Using Only Negative Indexes
4. Important Functions
ReshapingFlatteningJoining Sorting ArraysCopying Arrays

bookFunction 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)
copy

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))
copy

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
test

Swipe to show code editor

  1. 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].
  2. Display these arrays on the screen.
  3. Display the type of these arrays on the screen.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 2
toggle bottom row

bookFunction 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)
copy

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))
copy

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
test

Swipe to show code editor

  1. 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].
  2. Display these arrays on the screen.
  3. Display the type of these arrays on the screen.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 2
toggle bottom row

bookFunction 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)
copy

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))
copy

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
test

Swipe to show code editor

  1. 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].
  2. Display these arrays on the screen.
  3. Display the type of these arrays on the screen.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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)
copy

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))
copy

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
test

Swipe to show code editor

  1. 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].
  2. Display these arrays on the screen.
  3. Display the type of these arrays on the screen.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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