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3-D Arrays | Dimensions in Arrays
NumPy in a Nutshell
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Contenido del Curso

NumPy in a Nutshell

NumPy in a Nutshell

1. Getting Started with NumPy
2. Dimensions in Arrays
3. Indexing and Slicing
4. Important Functions

book3-D 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)
copy

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!

Tarea

  1. 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.
  2. Display these arrays on the screen: first arr_1, then arr_2.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 4
toggle bottom row

book3-D 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)
copy

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!

Tarea

  1. 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.
  2. Display these arrays on the screen: first arr_1, then arr_2.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 4
toggle bottom row

book3-D 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)
copy

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!

Tarea

  1. 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.
  2. Display these arrays on the screen: first arr_1, then arr_2.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

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)
copy

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!

Tarea

  1. 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.
  2. Display these arrays on the screen: first arr_1, then arr_2.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 2. Capítulo 4
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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