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

Answer 1

All that matters is whether you slice by rows or by columns. Slicing by rows can return a view because it is a contiguous segment of the original array. Slicing by column must return a copy because it is not a contiguous segment. For example:

A1 A2 A3
B1 B2 B3
C1 C2 C3

By default, it is stored in memory this way:

A1 A2 A3 B1 B2 B3 C1 C2 C3

So if you want to choose every second row, it is:

[A1 A2 A3] B1 B2 B3 [C1 C2 C3]

That can be described as {start: 0, size: 3, stride: 6}.

But if you want to choose every second column:

[A1] A2 [A3 B1] B2 [B3 C1] C2 [C3]

And there is no way to describe that using a single start, size, and stride. So there is no way to construct such a view.

If you want to be able to view every second column instead of every second row, you can construct your array in column-major aka Fortran order instead:

np.array(a, order='F')

Then it will be stored as such:

A1 B1 C1 A2 B2 C2 A3 B3 C3