Sorting Lists


6 min read

There are at least two common ways to sort lists in Python:

  • With sorted function that returns a new list
  • With list.sort method that modifies list in place

Which one is faster? Let’s find out!

sorted() vs list.sort()

I will start with a list of 1 000 000 randomly shuffled integers. Later on, I will also check if the order matters.

# sorting.py
from random import sample

# List of 1 000 000 integers randomly shuffled
MILLION_RANDOM_NUMBERS = sample(range(1_000_000), 1_000_000)


def test_sort():
    return MILLION_RANDOM_NUMBERS.sort()

def test_sorted():
    return sorted(MILLION_RANDOM_NUMBERS)
$ python -m timeit -s "from sorting import test_sort" "test_sort()"
1 loop, best of 5: 6 msec per loop

$ python -m timeit -s "from sorting import test_sorted" "test_sorted()"
1 loop, best of 5: 373 msec per loop

When benchmarked with Python 3.8, sort() is around 60 times as fast as sorted() when sorting 1 000 000 numbers (373/6≈62.167).

Update: As pointed out by a vigilant reader in the comments section, I’ve made a terrible blunder in my benchmarks! timeit runs the code multiple times, which means that:

  • The first time it runs, it sorts the random list in place.
  • The second and next time, it runs on the same list (that is now sorted)! And sorting an already sorted list is much faster, as I show you in the next paragraph.

We get completely wrong results because we compare calling list.sort() on an ordered list with calling sorted() on a random list.

Let’s fix my test functions and rerun benchmarks.

# sorting.py
from random import sample

# List of 1 000 000 integers randomly shuffled
MILLION_RANDOM_NUMBERS = sample(range(1_000_000), 1_000_000)

def test_sort():
    random_list = MILLION_RANDOM_NUMBERS[:]
    return random_list.sort()

def test_sorted():
    random_list = MILLION_RANDOM_NUMBERS[:]
    return sorted(random_list)

This time, I’m explicitly making a copy of the initial shuffled list and then sort that copy (new_list = old_list[:] is a great little snippet to copy a list in Python). Copying a list adds a small overhead to our test functions, but as long as we call the same code in both functions, that’s acceptable.

Let’s see the results:

$ python -m timeit -s "from sorting import test_sort" "test_sort()"
1 loop, best of 5: 352 msec per loop

$ python -m timeit -s "from sorting import test_sorted" "test_sorted()"
1 loop, best of 5: 385 msec per loop

Now, sorted is less than 10% slower (385/352≈1.094). Since we only run one loop, the exact numbers are not very reliable. I have rerun the same tests a couple more times, and the results were slightly different each time. sort took around 345-355 msec and sorted took around 379-394 msec (but it was always slower than sort). This difference comes mostly from the fact that sorted creates a new list (again, as kindly pointed out by a guest reader in the comments).

Initial order matters

What happens when our initial list is already sorted?

MILLION_NUMBERS = list(range(1_000_000))
$ python -m timeit -s "from sorting import test_sort" "test_sort()"
20 loops, best of 5: 12.1 msec per loop

$ python -m timeit -s "from sorting import test_sorted" "test_sorted()"
20 loops, best of 5: 16.6 msec per loop

Now, sorting takes much less time and the difference between sort and sorted grows to 37% (16.6/12.1≈1.372). Why is sorted 37% slower this time? Well, creating a new list takes the same amount of time as before. And since the time spent on sorting has shrunk, the impact of creating that new list got bigger.

If you want to run the benchmarks on your computer, make sure to adjust the test_sort and test_sorted functions, so they use the new MILLION_NUMBERS variable (instead of the MILLION_RANDOM_NUMBERS). Make sure you do this update for each of the following tests.

And if we try to sort a list of 1 000 000 numbers ordered in descending order:

DESCENDING_MILLION_NUMBERS = list(range(1_000_000, 0, -1))
$ python -m timeit -s "from sorting import test_sort" "test_sort()"
20 loops, best of 5: 11.7 msec per loop

$ python -m timeit -s "from sorting import test_sorted" "test_sorted()"
20 loops, best of 5: 18.1 msec per loop

The results are almost identical as before. The sorting algorithm is clever enough to optimize the sorting process for a descending list.

For our last test, let’s try to sort 1 000 000 numbers where 100 000 elements are shuffled, and the rest are ordered:

# 10% of numbers are random
MILLION_SLIGHTLY_RANDOM_NUMBERS = [*range(900_000), *sample(range(1_000_000), 100_000)]
$ python -m timeit -s "from sorting import test_sort" "test_sort()"
5 loops, best of 5: 61.2 msec per loop

$ python -m timeit -s "from sorting import test_sorted" "test_sorted()"
5 loops, best of 5: 71 msec per loop

Both functions get slower as the input list becomes more scrambled.

Using list.sort() is my preferred way of sorting lists - it saves some time (and memory) by not creating a new list. But that’s a double-edged sword! Sometimes you might accidentally overwrite the initial list without realizing it (as I did with my initial benchmarks 😅). So, if you want to preserve the initial list’s order, you have to use sorted instead. And sorted can be used with any iterable, while sort only works with lists. If you want to sort a set, then sorted is your only solution.

Conclusions

sort is slightly faster than sorted, because it doesn’t create a new list. But you might still stick with sorted if:

  • You don’t want to modify the original list. sort performs sorting in-place, so you can’t use it here.
  • You need to sort something else than a list. sort is only defined on lists, so if you want to sort a set or any other collection of items, you have to use sorted instead.

If you want to learn more, the Sorting HOW TO guide from Python documentation contains a lot of useful information.

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Hi, I'm Sebastian. I write and speak about Python - how to write better code, what cool tools and libraries I'm using, and what tips & tricks make my life easier.

Check out my latest series called Writing Faster Python, where I benchmark different code structures and give unsolicited advice on when to use them. And if you like MacOS apps and CLI tools (who doesn't?), check out my favorite Mac apps and CLI tools.

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