Efficiency of using a Python list as a queue

Question

A coworker recently wrote a program in which he used a Python list as a queue. In other words, he used .append(x) when needing to insert items and .pop(0)

Answer 1

Some answers claimed a "10x" speed advantage for deque vs list-used-as-FIFO when both have 1000 entries, but that's a bit of an overbid:

$ python -mtimeit -s'q=range(1000)' 'q.append(23); q.pop(0)'
1000000 loops, best of 3: 1.24 usec per loop
$ python -mtimeit -s'import collections; q=collections.deque(range(1000))' 'q.append(23); q.popleft()'
1000000 loops, best of 3: 0.573 usec per loop

python -mtimeit is your friend -- a really useful and simple micro-benchmarking approach! With it you can of course also trivially explore performance in much-smaller cases:

$ python -mtimeit -s'q=range(100)' 'q.append(23); q.pop(0)'
1000000 loops, best of 3: 0.972 usec per loop
$ python -mtimeit -s'import collections; q=collections.deque(range(100))' 'q.append(23); q.popleft()'
1000000 loops, best of 3: 0.576 usec per loop

(not very different for 12 instead of 100 items btw), and in much-larger ones:

$ python -mtimeit -s'q=range(10000)' 'q.append(23); q.pop(0)'
100000 loops, best of 3: 5.81 usec per loop
$ python -mtimeit -s'import collections; q=collections.deque(range(10000))' 'q.append(23); q.popleft()'
1000000 loops, best of 3: 0.574 usec per loop

You can see that the claim of O(1) performance for deque is well founded, while a list is over twice as slow around 1,000 items, an order of magnitude around 10,000. You can also see that even in such cases you're only wasting 5 microseconds or so per append/pop pair and decide how significant that wastage is (though if that's all you're doing with that container, deque has no downside, so you might as well switch even if 5 usec more or less won't make an important difference).