Course Content

Ultimate NumPy

Creating Higher Dimensional Arrays

2D Arrays

Let’s now create a higher dimensional array, namely a 2D array:


              1234
            
import numpy as np
# Creating a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(f'2-dimensional array: \n{array_2d}')

Basically, creating a higher-dimensional NumPy array involves passing a higher-dimensional list as the argument of the array() function.

Note
Any NumPy array object is called an ndarray.

Here is a visualization of our 2D array:

We can think of it as a 2x3 matrix.

3D Array (Optional)

Creating 3D arrays is nearly identical to creating 2D arrays. The only difference is that we now need to pass a 3D list as an argument:


              12345678
            
import numpy as np
# Creating a 3D array
array_3d = np.array([
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
    [[19, 20, 21], [22, 23, 24], [25, 26, 27]]
])
print(f'3-dimensional array: \n{array_3d}')

However, visualizing a 3D array is a bit more complex, but it can still be done:

The array is 3x3x3, which is why we have a cube with each side equal to 3. The innermost 1D arrays lie along axis 2 (e.g., [1, 2, 3]), where each small cube with a side length of 1 is a particular element (number).

All the elements of a 3D array are stored inside these innermost 1D arrays. The cube is just a visual representation to make things clear. The total number of elements (small cubes) is 27 (the volume of the cube).

However, in practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.

Task

Create a 2D array named array_2d:

Use the correct function to create a numpy 2D array;
Create a 2D array based on two lists (the first argument): [24, 41] and [32, 25] in this order;
Set the data type of its elements to np.int8 via specifying the second argument.

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 3

Creating Higher Dimensional Arrays

2D Arrays

Let’s now create a higher dimensional array, namely a 2D array:


              1234
            
import numpy as np
# Creating a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(f'2-dimensional array: \n{array_2d}')

Basically, creating a higher-dimensional NumPy array involves passing a higher-dimensional list as the argument of the array() function.

Note
Any NumPy array object is called an ndarray.

Here is a visualization of our 2D array:

We can think of it as a 2x3 matrix.

3D Array (Optional)

Creating 3D arrays is nearly identical to creating 2D arrays. The only difference is that we now need to pass a 3D list as an argument:


              12345678
            
import numpy as np
# Creating a 3D array
array_3d = np.array([
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
    [[19, 20, 21], [22, 23, 24], [25, 26, 27]]
])
print(f'3-dimensional array: \n{array_3d}')

However, visualizing a 3D array is a bit more complex, but it can still be done:

However, in practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.

Task

Create a 2D array named array_2d:

Use the correct function to create a numpy 2D array;
Create a 2D array based on two lists (the first argument): [24, 41] and [32, 25] in this order;
Set the data type of its elements to np.int8 via specifying the second argument.

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 3

Creating Higher Dimensional Arrays

2D Arrays

Let’s now create a higher dimensional array, namely a 2D array:


              1234
            
import numpy as np
# Creating a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(f'2-dimensional array: \n{array_2d}')

Basically, creating a higher-dimensional NumPy array involves passing a higher-dimensional list as the argument of the array() function.

Note
Any NumPy array object is called an ndarray.

Here is a visualization of our 2D array:

We can think of it as a 2x3 matrix.

3D Array (Optional)

Creating 3D arrays is nearly identical to creating 2D arrays. The only difference is that we now need to pass a 3D list as an argument:


              12345678
            
import numpy as np
# Creating a 3D array
array_3d = np.array([
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
    [[19, 20, 21], [22, 23, 24], [25, 26, 27]]
])
print(f'3-dimensional array: \n{array_3d}')

However, visualizing a 3D array is a bit more complex, but it can still be done:

However, in practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.

Task

Create a 2D array named array_2d:

Use the correct function to create a numpy 2D array;
Create a 2D array based on two lists (the first argument): [24, 41] and [32, 25] in this order;
Set the data type of its elements to np.int8 via specifying the second argument.

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

Everything was clear?

Thanks for your feedback!

2D Arrays

Let’s now create a higher dimensional array, namely a 2D array:


              1234
            
import numpy as np
# Creating a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(f'2-dimensional array: \n{array_2d}')

Basically, creating a higher-dimensional NumPy array involves passing a higher-dimensional list as the argument of the array() function.

Note
Any NumPy array object is called an ndarray.

Here is a visualization of our 2D array:

We can think of it as a 2x3 matrix.

3D Array (Optional)

Creating 3D arrays is nearly identical to creating 2D arrays. The only difference is that we now need to pass a 3D list as an argument:


              12345678
            
import numpy as np
# Creating a 3D array
array_3d = np.array([
    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
    [[19, 20, 21], [22, 23, 24], [25, 26, 27]]
])
print(f'3-dimensional array: \n{array_3d}')

However, visualizing a 3D array is a bit more complex, but it can still be done:

However, in practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.

Task

Create a 2D array named array_2d:

Use the correct function to create a numpy 2D array;
Create a 2D array based on two lists (the first argument): [24, 41] and [32, 25] in this order;
Set the data type of its elements to np.int8 via specifying the second argument.

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

Section 1. Chapter 3

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