python: deque vs list performance comparison

Question

In python docs I can see that deque is a special collection highly optimized for poping/adding items from left or right sides. E.g. documentation says:

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Answer 1

I found my way to this question and thought I'd offer up an example with a little context.
A classic use-case for using a Deque might be rotating/shifting elements in a collection because (as others have mentioned), you get very good (O(1)) complexity for push/pop operations on both ends because these operations are just moving references around as opposed to a list which has to physically move objects around in memory.

So here are 2 very similar-looking implementations of a rotate-left function:

def rotate_with_list(items, n):
    l = list(items)
    for _ in range(n):
        l.append(l.pop(0))
    return l

from collections import deque
def rotate_with_deque(items, n):
    d = deque(items)
    for _ in range(n):
        d.append(d.popleft())
    return d

Note: This is such a common use of a deque that the deque has a built-in rotate method, but I'm doing it manually here for the sake of visual comparison.

Now let's %timeit.

In [1]: def rotate_with_list(items, n):
   ...:     l = list(items)
   ...:     for _ in range(n):
   ...:         l.append(l.pop(0))
   ...:     return l
   ...: 
   ...: from collections import deque
   ...: def rotate_with_deque(items, n):
   ...:     d = deque(items)
   ...:     for _ in range(n):
   ...:         d.append(d.popleft())
   ...:     return d
   ...: 

In [2]: items = range(100000)

In [3]: %timeit rotate_with_list(items, 800)
100 loops, best of 3: 17.8 ms per loop

In [4]: %timeit rotate_with_deque(items, 800)
The slowest run took 5.89 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 527 µs per loop

In [5]: %timeit rotate_with_list(items, 8000)
10 loops, best of 3: 174 ms per loop

In [6]: %timeit rotate_with_deque(items, 8000)
The slowest run took 8.99 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 1.1 ms per loop

In [7]: more_items = range(10000000)

In [8]: %timeit rotate_with_list(more_items, 800)
1 loop, best of 3: 4.59 s per loop

In [9]: %timeit rotate_with_deque(more_items, 800)
10 loops, best of 3: 109 ms per loop

Pretty interesting how both data structures expose an eerily similar interface but have drastically different performance :)