Python Numpy 中的行交换
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Row exchange in Numpy
提问by JPG
In Python I can exchange 2 variables by mean of multiple affectation; it works also with lists:
在 Python 中，我可以通过多重影响来交换 2 个变量；它也适用于列表：
l1,l2=[1,2,3],[4,5,6]
l1,l2=l2,l1
print(l1,l2)
>>> [4, 5, 6] [1, 2, 3]
But when I want to exchange 2 rows of a numpy array (for example in the Gauss algorithm), it fails:
但是当我想交换 numpy 数组的 2 行时（例如在高斯算法中），它失败了：
import numpy as np
a3=np.array([[1,2,3],[4,5,6]])
print(a3)
a3[0,:],a3[1,:]=a3[1,:],a3[0,:]
print(a3)
>>> [[1 2 3]
[4 5 6]]
[[4 5 6]
[4 5 6]]
I thought that, for a strange reason, the two columns were now pointing to the same values; but it's not the case, since a3[0,0]=5after the preceeding lines changes a3[0,0] but not a3[1,0].
我认为，出于一个奇怪的原因，这两列现在指向相同的值；但事实并非如此，因为a3[0,0]=5在前面的行更改 a3[0,0] 而不是 a3[1,0] 之后。
I have found how to do with this problem: for example a3[0,:],a3[1,:]=a3[1,:].copy(),a3[0,:].copy()works. But can anyone explain why exchange with multiple affectation fails with numpy rows? My questions concerns the underlying work of Python and Numpy.
我已经找到了如何处理这个问题：例如a3[0,:],a3[1,:]=a3[1,:].copy(),a3[0,:].copy()有效。但是谁能解释为什么用 numpy 行进行多重伪装的交流会失败？我的问题涉及 Python 和 Numpy 的底层工作。
回答by Eelco Hoogendoorn
This works the way you intend it to:
这将按照您的预期工作：
a3[[0,1]] = a3[[1,0]]
The two separate assignments in the tuple assignment are not buffered with respect to eachother; one happens after the other, leading the overwriting your observe
元组分配中的两个单独分配没有相互缓冲；一个接一个发生，导致覆盖你的观察
