数组(带有行索引)的numpy数组的Pythonic方法 [英] Pythonic way for numpy array of array (with index of rows)

问题描述

我想在表中找到与特定索引相对应的值. 例如，这是我的桌子:

I want to find the values in a table that correspond to specific indexes. For example, this is my table:

import numpy as np
my_array = np.array([[0,1,0,1,0,1,0],[1,2,1,2,1,2,1],[4,5,4,3,3,4,5]])

#---------------------------------------------------------------------
#    my_array :     [[0, 1, 0, 1, 0, 1, 0],
#                    [1, 2, 1, 2, 1, 2, 1],
#                    [4, 5, 4, 3, 3, 4, 5]])

下面是一个索引数组.该数组中的值是my_array的行. (列未建立索引，索引的列索引对应于my_array的第一个索引.)

And below is an array of indexes. The values in this array are rows of my_array. (The columns are not indexed, and column index of indexes correspond to the first index of my_array.)

indexes = np.array([[0,0,0,0,0],[1,2,1,2,1]])

#---------------------------------------------------------------------
#   indexes :    [[0, 0, 0, 0, 0],
#                 [1, 2, 1, 2, 1]])

我想计算一个数组，其索引和值的形状与my_array行中的值相对应. 这是我的代码:

I want to compute an array with the same shape of indexes and values corresponding to the values in row of my_array. This is my code:

result = np.zeros(indexes.shape)

for i in range(0, indexes.shape[0]):
     result[i, :] = my_array[indexes[i, :], np.arange(0, indexes.shape[1])]

#---------------------------------------------------------------------
#   Result :    [[ 0.,  1.,  0.,  1.,  0.],
#                [ 1.,  5.,  1.,  3.,  1.]]

还有更多"pythonic方式"可以做到这一点吗?

Is there a more "pythonic way" to do that?

推荐答案

使用如果使用索引列表indexes进行索引以每列选择一个索引，请使用-

If indexing with list of indices indexes to select one per column, use -

my_array[indexes, np.arange(len(indexes))]

这篇关于数组(带有行索引)的numpy数组的Pythonic方法的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！