Row exchange in Numpy [duplicate]

Question

In Python I can exchange 2 variables by mean of multiple affectation; it works also with lists:

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:

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]=5 after the preceeding lines changes a3[0,0] but not a3[1,0].

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.

Answer 1

This works the way you intend it to:

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