python - Numpy: views vs copy by slicing

Question

When I am doing the slicing, an unexpected thing happened that seems the first to be view but the second is copy.

First

First slice of row, then slice of column. It seems is a view.

>>> a = np.arange(12).reshape(3, 4)   
>>> a[0:3:2, :][:, [0, 2]] = 100
>>> a
array([[100,   1, 100,   3],
       [  4,   5,   6,   7],
       [100,   9, 100,  11]])

Second

But if I first slice of column, then slice of row, it seems a copy:

>>> a[:, [0, 2]][0:3:2, :] = 0
>>> a
array([[100,   1, 100,   3],
       [  4,   5,   6,   7],
       [100,   9, 100,  11]])

I am confused because the two methods finally will cause seem position to change, but why the second actually doesn't change the number?

Answer 1

The accepted answer by John Zwinck is actually false (I just figured this out the hard way!). The problem in the question is a combination of doing "lvalue indexing" with numpy's fancy indexing. The following doc explains exactly this case

https://scipy-cookbook.readthedocs.io/items/ViewsVsCopies.html

in the section "But fancy indexing does seem to return views sometimes, doesn't it?"