Contenido del Curso
Ultimate NumPy
Ultimate NumPy
Integer Array Indexing in 2D Arrays
Speaking of 2D and higher-dimensional arrays, integer array indexing works the same as in 1D arrays along each axis. If we use only one integer array for indexing, we index along only one axis (axis 0). If we use two arrays separated by a comma, we index along both axes (axis 0 and axis 1).
Indexing only along axis 0 using an array of integers returns a 2D array. When we access elements via such indexing, we group them into a new array. This new array consists of 1D arrays, and grouping them increases the dimensionality by one, resulting in a 2D array.
Indexing along axis 0 and axis 1 using two arrays of integers returns a 1D array.
Note
All integer arrays used for each of the axes must have the same shape.
Now it’s time for an example:
import numpy as np array_2d = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) # Retrieving first and the third row print(array_2d[[0, 2]]) print('-' * 10) # Retrieving the main diagonal elements print(array_2d[[0, 1, 2], [0, 1, 2]]) print('-' * 10) # Retrieving the first and third element of the second row print(array_2d[1, [0, 2]]) # IndexError is thrown, since index 3 along axis 0 is out of bounds print(array_2d[[0, 3], [0, 1]])
Here is an illustration for clarification:
As you can see, we can also combine basic integer indexing and integer array indexing.
Note
Once again, if at least one of the indices is out of bounds, an
IndexErroris thrown.
Tarea
You are analyzing the performance metrics of three different products over two criteria, stored in a 2D NumPy array. Use integer array indexing (a Python list) to retrieve the first metric of the first product and the second metric of the third product from performance_metrics using only positive indices.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Integer Array Indexing in 2D Arrays
Speaking of 2D and higher-dimensional arrays, integer array indexing works the same as in 1D arrays along each axis. If we use only one integer array for indexing, we index along only one axis (axis 0). If we use two arrays separated by a comma, we index along both axes (axis 0 and axis 1).
Indexing only along axis 0 using an array of integers returns a 2D array. When we access elements via such indexing, we group them into a new array. This new array consists of 1D arrays, and grouping them increases the dimensionality by one, resulting in a 2D array.
Indexing along axis 0 and axis 1 using two arrays of integers returns a 1D array.
Note
All integer arrays used for each of the axes must have the same shape.
Now it’s time for an example:
import numpy as np array_2d = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) # Retrieving first and the third row print(array_2d[[0, 2]]) print('-' * 10) # Retrieving the main diagonal elements print(array_2d[[0, 1, 2], [0, 1, 2]]) print('-' * 10) # Retrieving the first and third element of the second row print(array_2d[1, [0, 2]]) # IndexError is thrown, since index 3 along axis 0 is out of bounds print(array_2d[[0, 3], [0, 1]])
Here is an illustration for clarification:
As you can see, we can also combine basic integer indexing and integer array indexing.
Note
Once again, if at least one of the indices is out of bounds, an
IndexErroris thrown.
Tarea
You are analyzing the performance metrics of three different products over two criteria, stored in a 2D NumPy array. Use integer array indexing (a Python list) to retrieve the first metric of the first product and the second metric of the third product from performance_metrics using only positive indices.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Integer Array Indexing in 2D Arrays
Speaking of 2D and higher-dimensional arrays, integer array indexing works the same as in 1D arrays along each axis. If we use only one integer array for indexing, we index along only one axis (axis 0). If we use two arrays separated by a comma, we index along both axes (axis 0 and axis 1).
Indexing only along axis 0 using an array of integers returns a 2D array. When we access elements via such indexing, we group them into a new array. This new array consists of 1D arrays, and grouping them increases the dimensionality by one, resulting in a 2D array.
Indexing along axis 0 and axis 1 using two arrays of integers returns a 1D array.
Note
All integer arrays used for each of the axes must have the same shape.
Now it’s time for an example:
import numpy as np array_2d = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) # Retrieving first and the third row print(array_2d[[0, 2]]) print('-' * 10) # Retrieving the main diagonal elements print(array_2d[[0, 1, 2], [0, 1, 2]]) print('-' * 10) # Retrieving the first and third element of the second row print(array_2d[1, [0, 2]]) # IndexError is thrown, since index 3 along axis 0 is out of bounds print(array_2d[[0, 3], [0, 1]])
Here is an illustration for clarification:
As you can see, we can also combine basic integer indexing and integer array indexing.
Note
Once again, if at least one of the indices is out of bounds, an
IndexErroris thrown.
Tarea
You are analyzing the performance metrics of three different products over two criteria, stored in a 2D NumPy array. Use integer array indexing (a Python list) to retrieve the first metric of the first product and the second metric of the third product from performance_metrics using only positive indices.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Speaking of 2D and higher-dimensional arrays, integer array indexing works the same as in 1D arrays along each axis. If we use only one integer array for indexing, we index along only one axis (axis 0). If we use two arrays separated by a comma, we index along both axes (axis 0 and axis 1).
Indexing only along axis 0 using an array of integers returns a 2D array. When we access elements via such indexing, we group them into a new array. This new array consists of 1D arrays, and grouping them increases the dimensionality by one, resulting in a 2D array.
Indexing along axis 0 and axis 1 using two arrays of integers returns a 1D array.
Note
All integer arrays used for each of the axes must have the same shape.
Now it’s time for an example:
import numpy as np array_2d = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) # Retrieving first and the third row print(array_2d[[0, 2]]) print('-' * 10) # Retrieving the main diagonal elements print(array_2d[[0, 1, 2], [0, 1, 2]]) print('-' * 10) # Retrieving the first and third element of the second row print(array_2d[1, [0, 2]]) # IndexError is thrown, since index 3 along axis 0 is out of bounds print(array_2d[[0, 3], [0, 1]])
Here is an illustration for clarification:
As you can see, we can also combine basic integer indexing and integer array indexing.
Note
Once again, if at least one of the indices is out of bounds, an
IndexErroris thrown.
Tarea
You are analyzing the performance metrics of three different products over two criteria, stored in a 2D NumPy array. Use integer array indexing (a Python list) to retrieve the first metric of the first product and the second metric of the third product from performance_metrics using only positive indices.
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