Contenido del Curso

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.

Tarea

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.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 1. Capítulo 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.

Tarea

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.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 1. Capítulo 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.

Tarea

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.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

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.

Tarea

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.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 1. Capítulo 3

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones