April 30, 2010

Efficiency of list comprehensions

I'm psyched about the awesome comments on my previous entry, Python by example: list comprehensions. Originally this entry was just a response to those comments, but people who stumbled across this entry on the interwebz found the response format too confusing, so I've restructured it for posterity.

Efficiency of the more common usage

Let's look at the efficiency of list comprehensions in the more common usage, where the comprehension's list result is actually relevant (or, in compiler-speak, live-out).

Using the following program, you can see the time spent in each implementation and the corresponding bytecode sequence:

import dis
import inspect
import timeit

programs = dict(
result = []
for i in range(20):
    result.append(i * 2)
result = []
add = result.append
for i in range(20):
    add(i * 2)
    comprehension='result = [i * 2 for i in range(20)]',

for name, text in programs.iteritems():
    print name, timeit.Timer(stmt=text).timeit()
    code = compile(text, '<string>', 'exec')
loop 11.1495118141
  2           0 BUILD_LIST               0
              3 STORE_NAME               0 (result)

  3           6 SETUP_LOOP              37 (to 46)
              9 LOAD_NAME                1 (range)
             12 LOAD_CONST               0 (20)
             15 CALL_FUNCTION            1
             18 GET_ITER
        >>   19 FOR_ITER                23 (to 45)
             22 STORE_NAME               2 (i)

  4          25 LOAD_NAME                0 (result)
             28 LOAD_ATTR                3 (append)
             31 LOAD_NAME                2 (i)
             34 LOAD_CONST               1 (2)
             37 BINARY_MULTIPLY
             38 CALL_FUNCTION            1
             41 POP_TOP
             42 JUMP_ABSOLUTE           19
        >>   45 POP_BLOCK
        >>   46 LOAD_CONST               2 (None)
             49 RETURN_VALUE
loop_faster 8.36096310616
  2           0 BUILD_LIST               0
              3 STORE_NAME               0 (result)

  3           6 LOAD_NAME                0 (result)
              9 LOAD_ATTR                1 (append)
             12 STORE_NAME               2 (add)

  4          15 SETUP_LOOP              34 (to 52)
             18 LOAD_NAME                3 (range)
             21 LOAD_CONST               0 (20)
             24 CALL_FUNCTION            1
             27 GET_ITER
        >>   28 FOR_ITER                20 (to 51)
             31 STORE_NAME               4 (i)

  5          34 LOAD_NAME                2 (add)
             37 LOAD_NAME                4 (i)
             40 LOAD_CONST               1 (2)
             43 BINARY_MULTIPLY
             44 CALL_FUNCTION            1
             47 POP_TOP
             48 JUMP_ABSOLUTE           28
        >>   51 POP_BLOCK
        >>   52 LOAD_CONST               2 (None)
             55 RETURN_VALUE
comprehension 7.08145213127
  1           0 BUILD_LIST               0
              3 DUP_TOP
              4 STORE_NAME               0 (_[1])
              7 LOAD_NAME                1 (range)
             10 LOAD_CONST               0 (20)
             13 CALL_FUNCTION            1
             16 GET_ITER
        >>   17 FOR_ITER                17 (to 37)
             20 STORE_NAME               2 (i)
             23 LOAD_NAME                0 (_[1])
             26 LOAD_NAME                2 (i)
             29 LOAD_CONST               1 (2)
             32 BINARY_MULTIPLY
             33 LIST_APPEND
             34 JUMP_ABSOLUTE           17
        >>   37 DELETE_NAME              0 (_[1])
             40 STORE_NAME               3 (result)
             43 LOAD_CONST               2 (None)
             46 RETURN_VALUE

List comprehensions perform better here because you don’t need to load the append attribute off of the list (loop program, bytecode 28) and call it as a function (loop program, bytecode 38). Instead, in a comprehension, a specialized LIST_APPEND bytecode is generated for a fast append onto the result list (comprehension program, bytecode 33).

In the loop_faster program, you avoid the overhead of the append attribute lookup by hoisting it out of the loop and placing the result in a fastlocal (bytecode 9-12), so it loops more quickly; however, the comprehension uses a specialized LIST_APPEND bytecode instead of incurring the overhead of a function call, so it still trumps.

Using list comprehensions for side effects

I want to address a point that was brought up in the previous entry as to the efficiency of for loops versus list comprehensions when used purely for side effects, but I'll discuss the subjective bit first, since that's the least sciency part.


Simple test – if you did need the result would the comprehension be easily understood? If the answer is yes then removing the assignment on the left hand side doesn’t magically make it less readable…

Michael Foord

First of all, thanks to Michael for his excellent and thought provoking comment!

My response is that removing the use of the result does indeed make it less readable, precisely because you're using a result-producing control flow construct where the result is not needed. I suppose I'm positing that it's inherently confusing to do that with your syntax: there's a looping form that doesn't produce a result, so that should be used instead. It's expressing your semantic intention via syntax.

For advanced Pythonistas it's easy for figure out what's going on at a glance, but comprehension-as-loop definitely has a "there's more than one way to do it" smell about it, which also makes it less amenable to people learning the language.

With a viable comprehension-as-loop option, every time a user goes to write a loop that doesn't require a result they now ask themselves, "Can I fit this into the list comprehension form?" Those mental branches are, to me, what "one way to do it" is designed to avoid. When I read Perl code, I take "mental exceptions" all the time because the author didn't use the construct that I would have used in the same situation. Minimizing that is a good thing, so I maintain that "no result needed" should automatically imply a loop construct.


Consider two functions, comprehension and loop:

def loop():
    accum = []
    for i in range(20):
    return accum

def comprehension():
    accum = []
    [accum.append(i) for i in range(20)]
    return accum

N.B. This example is comparing the efficiency of a list comprehension where the result of the comprehension is ignored to a for loop that produces no result, as is discussed in the referenced entry, Python by example: list comprehensions.

Michael Foord comments:

Your alternative for the single line, easily readable, list comprehension is four lines that are less efficient because the loop happens in the interpreter rather than in C.

However, the disassembly, obtained via dis.dis(func) looks like the following for the loop:

2           0 BUILD_LIST               0
            3 STORE_FAST               0 (accum)

3           6 SETUP_LOOP              33 (to 42)
            9 LOAD_GLOBAL              0 (range)
           12 LOAD_CONST               1 (20)
           15 CALL_FUNCTION            1
           18 GET_ITER
      >>   19 FOR_ITER                19 (to 41)
           22 STORE_FAST               1 (i)

4          25 LOAD_FAST                0 (accum)
           28 LOAD_ATTR                1 (append)
           31 LOAD_FAST                1 (i)
           34 CALL_FUNCTION            1
           37 POP_TOP
           38 JUMP_ABSOLUTE           19
      >>   41 POP_BLOCK

5     >>   42 LOAD_FAST                0 (accum)
           45 RETURN_VALUE

And it looks like the following for the comprehension:

2           0 BUILD_LIST               0
            3 STORE_FAST               0 (accum)

3           6 BUILD_LIST               0
            9 DUP_TOP
           10 STORE_FAST               1 (_[1])
           13 LOAD_GLOBAL              0 (range)
           16 LOAD_CONST               1 (20)
           19 CALL_FUNCTION            1
           22 GET_ITER
      >>   23 FOR_ITER                22 (to 48)
           26 STORE_FAST               2 (i)
           29 LOAD_FAST                1 (_[1])
           32 LOAD_FAST                0 (accum)
           35 LOAD_ATTR                1 (append)
           38 LOAD_FAST                2 (i)
           41 CALL_FUNCTION            1
           44 LIST_APPEND
           45 JUMP_ABSOLUTE           23
      >>   48 DELETE_FAST              1 (_[1])
           51 POP_TOP

4          52 LOAD_FAST                0 (accum)
           55 RETURN_VALUE

By looking at the bytecode instructions, we see that the list comprehension is, at a language level, actually just "syntactic sugar" for the for loop, as mentioned by nes — they both lower down into the same control flow construct at a virtual machine level, at least in CPython.

The primary difference between the two disassemblies is that a superfluous list comprehension result is stored into fastlocal 1, which is loaded (bytecode 29) and appended to (bytecode 44) each iteration, creating some additional overhead — it's simply deleted in bytecode 48. Unless the POP_BLOCK operation (bytecode 41) of the loop disassembly is very expensive (I haven't looked into its implementation), the comprehension disassembly is guaranteed to be less efficient.

Because of this, I believe that Michael was mistaken in referring to an overhead that results from use of a for loop versus a list comprehension for CPython. It would be interesting to perform a survey of the list comprehension optimization techniques used in various Python implementations, but optimization seems difficult outside of something like a special Cython construct, because LOAD_GLOBAL range could potentially be changed from the builtin range function. Various issues of this kind are discussed in the (very interesting) paper The effect of unrolling and inlining for Python bytecode optimizations.

Learning Python by example: list comprehensions

My friend, who is starting to learn Python 2.x, asked me what this snippet did:

def collapse(seq):
    # Preserve order.
    uniq = []
    [uniq.append(item) for item in seq if not uniq.count(item)]
    return uniq

This is not a snippet that should be emulated (i.e. it's bad); however, it makes me happy: there are so many things that can be informatively corrected!

What is a list comprehension?

A list comprehension is a special brackety syntax to perform a transform operation with an optional filter clause that always produces a new sequence (list) object as a result. To break it down visually, you perform:

new_range = [i * i          for i in range(5)   if i % 2 == 0]

Which corresponds to:

*result*  = [*transform*    *iteration*         *filter*     ]

The filter piece answers the question, "should this item be transformed?" If the answer is yes, then the transform piece is evaluated and becomes an element in the result. The iteration [*] order is preserved in the result.

Go ahead and figure out what you expect new_range to be in the prior example. You can double check me in the Python shell, but I think it comes out to be:

>>> new_range = [i * i for i in range(5) if i % 2 == 0]
>>> print new_range
[0, 4, 16]

If it still isn't clicking, we can try to make the example less noisy by getting rid of the transform and filter — can you tell what this will produce?

>>> new_range = [i for i in range(5)]

So what's wrong with that first snippet?

As we observed in the previous section, a list comprehension always produces a result list, where the elements of the result list are the transformed elements of the iteration. That means, if there's no filter piece, there are exactly as many result elements as there were iteration elements.

Weird thing number one about the snippet — the list comprehension result is unused. It's created, mind you — list comprehension always create a value, even if you don't care what it is — but it just goes off to oblivion. (In technical terms, it becomes garbage.) When you don't need the result, just use a for loop! This is better:

def colapse(seq):
    """Preserve order."""
    uniq = []
    for item in seq:
        if not uniq.count(item):
    return uniq

It's two more lines, but it's less weird looking and wasteful. "Better for everybody who reads and runs your code," means you should do it.

Moral of the story: a list comprehension isn't just, "shorthand for a loop." It's shorthand for a transform from an input sequence to an output sequence with an optional filter. If it gets too complex or weird looking, just make a loop. It's not that hard and readers of your code will thank you.

Weird thing number two: the transform, list.append(item), produces None as its output value, because the return value from list.append is always None. Therefore, the result, even though it isn't kept anywhere, is a list of None values of the same length as seq (notice that there's no filter clause).

Weird thing number three: list.count(item) iterates over every element in the list looking for things that == to item. If you think through the case where you call collapse on an entirely unique sequence, you can tell that the collapse algorithm is O(n2). In fact, it's even worse than it may seem at first glance, because count will keep going all the way to the end of uniq, even if it finds item in the first index of uniq. What the original author really wanted was item not in uniq, which bails out early if it finds item in uniq.

Also worth mentioning for the computer-sciency folk playing along at home: if all elements of the sequence are comparable, you can bring that down to O(n * log n) by using a "shadow" sorted sequence and bisecting to test for membership. If the sequence is hashable you can bring it down to O(n), perhaps by using the set datatype if you are in Python >= 2.3. Note that the common cases of strings, numbers, and tuples (any built-in immutable datatype, for that matter) are hashable.

From Python history

It's interesting to note that Python Enhancement Proposal (PEP) #270 considered putting a uniq function into the language distribution, but withdrew it with the following statement:

Removing duplicate elements from a list is a common task, but there are only two reasons I can see for making it a built-in. The first is if it could be done much faster, which isn't the case. The second is if it makes it significantly easier to write code. The introduction of sets.py eliminates this situation since creating a sequence without duplicates is just a matter of choosing a different data structure: a set instead of a list.

Remember that sets can only contain hashable elements (same policy as dictionary keys) and are therefore not suitable for all uniq-ifying tasks, as mentioned in the last paragraph of the previous section.



"Iteration" is just a fancy word for "step through the sequence, element by element, and give that element a name." In our case we're giving the name i.