Contenido del Curso
Ultimate NumPy
Ultimate NumPy
Boolean Indexing in 2D Arrays
Boolean indexing in 2D and higher-dimensional arrays works similarly to 1D arrays. However, the boolean array must have the same number of dimensions as the initial array (e.g., 2D for two-dimensional arrays). The returned array, however, will be 1D.
Here is an example:
import numpy as np array_2d = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) # Retrieving elements less than 3 or greater than OR greater than or equal to 8 print(array_2d[(array_2d < 3) | (array_2d >= 8)])
Let’s visualize it:
The boolean array on the right is the resulting boolean array of these two combined conditions. Once again, purple elements match the conditions, and green elements don’t. So when applying array_2d[(array_2d < 3) | (array_2d >= 8)], we get a 1D array of purple elements.
Swipe to show code editor
You are analyzing the temperatures recorded in two different cities over four days. The temperatures are stored in the city_temperatures array where each row represents a city and each column represents a day. Your task is to include only the temperatures that are less than or equal to 15 degrees OR greater than 30 degrees Celsius using boolean indexing.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Boolean Indexing in 2D Arrays
Boolean indexing in 2D and higher-dimensional arrays works similarly to 1D arrays. However, the boolean array must have the same number of dimensions as the initial array (e.g., 2D for two-dimensional arrays). The returned array, however, will be 1D.
Here is an example:
import numpy as np array_2d = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) # Retrieving elements less than 3 or greater than OR greater than or equal to 8 print(array_2d[(array_2d < 3) | (array_2d >= 8)])
Let’s visualize it:
The boolean array on the right is the resulting boolean array of these two combined conditions. Once again, purple elements match the conditions, and green elements don’t. So when applying array_2d[(array_2d < 3) | (array_2d >= 8)], we get a 1D array of purple elements.
Swipe to show code editor
You are analyzing the temperatures recorded in two different cities over four days. The temperatures are stored in the city_temperatures array where each row represents a city and each column represents a day. Your task is to include only the temperatures that are less than or equal to 15 degrees OR greater than 30 degrees Celsius using boolean indexing.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Boolean Indexing in 2D Arrays
Boolean indexing in 2D and higher-dimensional arrays works similarly to 1D arrays. However, the boolean array must have the same number of dimensions as the initial array (e.g., 2D for two-dimensional arrays). The returned array, however, will be 1D.
Here is an example:
import numpy as np array_2d = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) # Retrieving elements less than 3 or greater than OR greater than or equal to 8 print(array_2d[(array_2d < 3) | (array_2d >= 8)])
Let’s visualize it:
The boolean array on the right is the resulting boolean array of these two combined conditions. Once again, purple elements match the conditions, and green elements don’t. So when applying array_2d[(array_2d < 3) | (array_2d >= 8)], we get a 1D array of purple elements.
Swipe to show code editor
You are analyzing the temperatures recorded in two different cities over four days. The temperatures are stored in the city_temperatures array where each row represents a city and each column represents a day. Your task is to include only the temperatures that are less than or equal to 15 degrees OR greater than 30 degrees Celsius using boolean indexing.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Boolean indexing in 2D and higher-dimensional arrays works similarly to 1D arrays. However, the boolean array must have the same number of dimensions as the initial array (e.g., 2D for two-dimensional arrays). The returned array, however, will be 1D.
Here is an example:
import numpy as np array_2d = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) # Retrieving elements less than 3 or greater than OR greater than or equal to 8 print(array_2d[(array_2d < 3) | (array_2d >= 8)])
Let’s visualize it:
The boolean array on the right is the resulting boolean array of these two combined conditions. Once again, purple elements match the conditions, and green elements don’t. So when applying array_2d[(array_2d < 3) | (array_2d >= 8)], we get a 1D array of purple elements.
Swipe to show code editor
You are analyzing the temperatures recorded in two different cities over four days. The temperatures are stored in the city_temperatures array where each row represents a city and each column represents a day. Your task is to include only the temperatures that are less than or equal to 15 degrees OR greater than 30 degrees Celsius using boolean indexing.
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