On 21 Feb., 07:38, Carl Banks wrote:
> Numpy arrays can share underlying data like that when you take
> slices. For instance, this probably works the way you want:
>
> a = numpy.array([1,2,3,4,5,6])
> b = a[:3]
> c = a[3:]
>
> None of the actual data was copied here.
Hmm, that might be worth loo
On 21 Feb., 04:40, Steven D'Aprano wrote:
>
> Additionally, Python lists are over-allocated so that appends are fast. A
> list of (say) 1000 items might be over-allocated to (say) 1024 items, so
> that you can do 24 appends before the array is full and the array needs
> to be resized. This means t
On 21 Feb., 02:41, Steven D'Aprano wrote:
> What the OP is doing is quite different:
>
> (1) copy l1[:10]
> (2) assign the name l2 to it
> (3) resize l1 in place to the first 10 items.
>
> What the OP wants is:
>
> (1) assign the name l2 to l1[:10] without copying
> (2) resize l1 in place to the f
On 21 Feb., 02:30, Steven D'Aprano wrote:
> Python lists are arrays of pointers to objects, so copying a slice is
> fast: it doesn't have to copy the objects, just pointers. Deleting from
> the end of the list is also quick, because you don't have to move memory,
> just clear some pointers and cha
Hello,
I recently read about augmented assignments and that (with l1, l2
being lists)
l1.extend(l2)
is more efficient than
l1 = l1 + l2
because unnecessary copy operations can be avoided. Now my question is
if there's a similar thing for breaking a list into two parts. Let's
say I want
