Lernen 3-D Arrays | Dimensions in Arrays

With three-dimensional arrays, everything is quite clear and logical. These arrays consist of elements that are two-dimensional arrays.

Let's practice to make it easier to understand.

Here's an example of how we can create a 3-D array with four 2-D arrays, each containing three 1D arrays with 2 elements:


              123456789101112131415161718
            
import numpy as np
# Creating array
arr = np.array([
    [
        [1, 2], [4, 3], [7, 4]
    ],
    [
        [2, 10], [9, 15], [7, 5]
    ],
    [
        [1, 11], [3, 20], [0, 2]
    ],
    [
        [9, 25], [6, 13], [9, 8]
    ]
])
# Displaying array
print(arr)

Let's now take a look at the visualization of this array:

Our 3D array is 4x3x2, hence why we have a rectangular parallelepiped with sides equal to 4, 3 and 2, respectively. The innermost 1D arrays lie along the axis 2 (e.g., [1, 2] or [4, 3]) where each small cube with side equal to 1 is a particular element (number).

Basically, all the elements of a 3D array are stored inside these innermost 1D arrays. The rectangular parallelepiped is just a visual representation for us to make things clear. The total number of elements (small cubes) is equal to 24 (the volume of the rectangular parallelepiped), which 4 * 3 * 2.

Note

Since its a 2D visualization of a 3D object, we cannot display and see here all the elements.

Time to test your strength!

Aufgabe

Swipe to start coding

You need to create two arrays:
- the first one is a 2-D array containing two arrays with the values: 1, 5, 2 and 34, 2, 7;
- the second one is a 3-D array (use only a single line to create this array) containing two 2-D arrays, both of which include two arrays with the values 5, 3, 8 and 6, 1, 9.
Display these arrays on the screen: first arr_1, then arr_2.

Lösung

War alles klar?

Danke für Ihr Feedback!

Abschnitt 2. Kapitel 4

single

Fragen Sie AI

Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen

Swipe um das Menü anzuzeigen

With three-dimensional arrays, everything is quite clear and logical. These arrays consist of elements that are two-dimensional arrays.

Let's practice to make it easier to understand.

Here's an example of how we can create a 3-D array with four 2-D arrays, each containing three 1D arrays with 2 elements:


              123456789101112131415161718
            
import numpy as np
# Creating array
arr = np.array([
    [
        [1, 2], [4, 3], [7, 4]
    ],
    [
        [2, 10], [9, 15], [7, 5]
    ],
    [
        [1, 11], [3, 20], [0, 2]
    ],
    [
        [9, 25], [6, 13], [9, 8]
    ]
])
# Displaying array
print(arr)

Let's now take a look at the visualization of this array:

Note

Since its a 2D visualization of a 3D object, we cannot display and see here all the elements.

Time to test your strength!

Aufgabe

Swipe to start coding

You need to create two arrays:
- the first one is a 2-D array containing two arrays with the values: 1, 5, 2 and 34, 2, 7;
- the second one is a 3-D array (use only a single line to create this array) containing two 2-D arrays, both of which include two arrays with the values 5, 3, 8 and 6, 1, 9.
Display these arrays on the screen: first arr_1, then arr_2.

Lösung

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden

War alles klar?

Danke für Ihr Feedback!

Abschnitt 2. Kapitel 4

single

3-D Arrays

Lösung

Awesome!

3-D Arrays

Lösung

Awesome!