Contenido del Curso
Ultimate NumPy
Ultimate NumPy
Slicing in 2D Arrays
Slicing in 2D and higher-dimensional arrays works similarly to slicing in 1D arrays. However, in 2D arrays, there are two axes.
If we want to perform slicing only on axis 0 to retrieve 1D arrays, the syntax remains the same: array[start:end:step]. If we want to perform slicing on the elements of these 1D arrays (axis 1), the syntax is as follows: array[start:end:step, start:end:step]. Essentially, the number of slices corresponds to the number of dimensions of an array.
Moreover, we can use slicing for one axis and basic indexing for the other axis. Let's look at an example of 2D slicing (purple squares represent the elements retrieved from slicing, and the black arrow indicates that the elements are taken in reverse order):
Here is the code for this example:
import numpy as np array_2d = np.array([ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12] ]) print(array_2d[1:]) print('-' * 15) print(array_2d[:, 0]) print('-' * 15) print(array_2d[1:, 1:-1]) print('-' * 15) print(array_2d[:-1, ::2]) print('-' * 15) print(array_2d[2, ::-1])
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You are working with a 2D NumPy array that represents the scores of three students in three different subjects. The scores for each student are stored in a separate row, with each element representing the score in a specific subject. Create a slice of student_scores with the last two scores of the first student (first row) using basic indexing (positive indexing) and slicing (specify only a positive start).
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Slicing in 2D Arrays
Slicing in 2D and higher-dimensional arrays works similarly to slicing in 1D arrays. However, in 2D arrays, there are two axes.
If we want to perform slicing only on axis 0 to retrieve 1D arrays, the syntax remains the same: array[start:end:step]. If we want to perform slicing on the elements of these 1D arrays (axis 1), the syntax is as follows: array[start:end:step, start:end:step]. Essentially, the number of slices corresponds to the number of dimensions of an array.
Moreover, we can use slicing for one axis and basic indexing for the other axis. Let's look at an example of 2D slicing (purple squares represent the elements retrieved from slicing, and the black arrow indicates that the elements are taken in reverse order):
Here is the code for this example:
import numpy as np array_2d = np.array([ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12] ]) print(array_2d[1:]) print('-' * 15) print(array_2d[:, 0]) print('-' * 15) print(array_2d[1:, 1:-1]) print('-' * 15) print(array_2d[:-1, ::2]) print('-' * 15) print(array_2d[2, ::-1])
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You are working with a 2D NumPy array that represents the scores of three students in three different subjects. The scores for each student are stored in a separate row, with each element representing the score in a specific subject. Create a slice of student_scores with the last two scores of the first student (first row) using basic indexing (positive indexing) and slicing (specify only a positive start).
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Slicing in 2D Arrays
Slicing in 2D and higher-dimensional arrays works similarly to slicing in 1D arrays. However, in 2D arrays, there are two axes.
If we want to perform slicing only on axis 0 to retrieve 1D arrays, the syntax remains the same: array[start:end:step]. If we want to perform slicing on the elements of these 1D arrays (axis 1), the syntax is as follows: array[start:end:step, start:end:step]. Essentially, the number of slices corresponds to the number of dimensions of an array.
Moreover, we can use slicing for one axis and basic indexing for the other axis. Let's look at an example of 2D slicing (purple squares represent the elements retrieved from slicing, and the black arrow indicates that the elements are taken in reverse order):
Here is the code for this example:
import numpy as np array_2d = np.array([ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12] ]) print(array_2d[1:]) print('-' * 15) print(array_2d[:, 0]) print('-' * 15) print(array_2d[1:, 1:-1]) print('-' * 15) print(array_2d[:-1, ::2]) print('-' * 15) print(array_2d[2, ::-1])
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You are working with a 2D NumPy array that represents the scores of three students in three different subjects. The scores for each student are stored in a separate row, with each element representing the score in a specific subject. Create a slice of student_scores with the last two scores of the first student (first row) using basic indexing (positive indexing) and slicing (specify only a positive start).
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Slicing in 2D and higher-dimensional arrays works similarly to slicing in 1D arrays. However, in 2D arrays, there are two axes.
If we want to perform slicing only on axis 0 to retrieve 1D arrays, the syntax remains the same: array[start:end:step]. If we want to perform slicing on the elements of these 1D arrays (axis 1), the syntax is as follows: array[start:end:step, start:end:step]. Essentially, the number of slices corresponds to the number of dimensions of an array.
Moreover, we can use slicing for one axis and basic indexing for the other axis. Let's look at an example of 2D slicing (purple squares represent the elements retrieved from slicing, and the black arrow indicates that the elements are taken in reverse order):
Here is the code for this example:
import numpy as np array_2d = np.array([ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12] ]) print(array_2d[1:]) print('-' * 15) print(array_2d[:, 0]) print('-' * 15) print(array_2d[1:, 1:-1]) print('-' * 15) print(array_2d[:-1, ::2]) print('-' * 15) print(array_2d[2, ::-1])
Swipe to show code editor
You are working with a 2D NumPy array that represents the scores of three students in three different subjects. The scores for each student are stored in a separate row, with each element representing the score in a specific subject. Create a slice of student_scores with the last two scores of the first student (first row) using basic indexing (positive indexing) and slicing (specify only a positive start).
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opcionesCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones