假设我们有一个输入数组，其中包含一些（但不是全部）nan值，我们希望将这些值写入一个纳米初始化的输出数组。在将非纳米数据写入输出数组后，仍然存在纳米值，我根本不理解 原因：
# minimal example just for testing purposes
import numpy as np
# fix state of seed
np.random.seed(1000)
# create input array and nan-filled output array
a = np.random.rand(6,3,5)
b = np.zeros((6,3,5)) * np.nan
x = [np.arange(6),1,2]
# select data in one dimension with others fixed
y_temp = a[x]
# set arbitrary index to nan
y_temp[1] = np.nan
ind_valid = ~np.isnan(y_temp)
# select non-nan values
y = y_temp[ind_valid]
# write input to output at corresponding indices
b[x][ind_valid] = y
print b[x][ind_valid]
# surprise, surprise :(
# [ nan nan nan nan nan nan]
# workaround (that will of course cost computation time, even if not much)
c = np.zeros(len(y_temp)) * np.nan
c[ind_valid] = y
b[x] = c
print b[x][ind_valid]
# and this is what we want to have
# [ 0.39719446 nan 0.39820488 0.68190824 0.86534558 0.69910395]
我认为数组b会在内存中保留一些块，并通过x索引它＆＃34;知道＆＃34;那些指数。然后，当使用ind_valid仅选择其中一些时，它也应该知道它们，并且能够准确地写入内存中的那些位地址。不知道，但也许它是......与python nested list unexpected behaviour类似？请解释，也许还提供一个很好的解决方案，而不是建议的解决方法！谢谢！