List comprehension is basically just a “syntactic sugar” for the regular for loop. In this case the reason that it performs better is because it doesn’t need to load the append attribute of the list and call it as a function at each iteration. In other words and in general, list comprehensions perform faster because suspending and resuming a function’s frame, or multiple functions in other cases, is slower than creating a list on demand.
Consider the following examples :
In [1]: def f1():
...: l = []
...: for i in range(5):
...: l.append(i)
...:
...:
...: def f2():
...: [i for i in range(5)]
...:
In [3]: import dis
In [4]: dis.dis(f1)
2 0 BUILD_LIST 0
2 STORE_FAST 0 (l)
3 4 LOAD_GLOBAL 0 (range)
6 LOAD_CONST 1 (5)
8 CALL_FUNCTION 1
10 GET_ITER
>> 12 FOR_ITER 14 (to 28)
14 STORE_FAST 1 (i)
4 16 LOAD_FAST 0 (l)
18 LOAD_METHOD 1 (append)
20 LOAD_FAST 1 (i)
22 CALL_METHOD 1
24 POP_TOP
26 JUMP_ABSOLUTE 12
>> 28 LOAD_CONST 0 (None)
30 RETURN_VALUE
In [5]:
In [5]: dis.dis(f2)
8 0 LOAD_CONST 1 (<code object <listcomp> at 0x7f397abc0d40, file "<ipython-input-1-45c11e415ee9>", line 8>)
2 LOAD_CONST 2 ('f2.<locals>.<listcomp>')
4 MAKE_FUNCTION 0
6 LOAD_GLOBAL 0 (range)
8 LOAD_CONST 3 (5)
10 CALL_FUNCTION 1
12 GET_ITER
14 CALL_FUNCTION 1
16 POP_TOP
18 LOAD_CONST 0 (None)
20 RETURN_VALUE
Disassembly of <code object <listcomp> at 0x7f397abc0d40, file "<ipython-input-1-45c11e415ee9>", line 8>:
8 0 BUILD_LIST 0
2 LOAD_FAST 0 (.0)
>> 4 FOR_ITER 8 (to 14)
6 STORE_FAST 1 (i)
8 LOAD_FAST 1 (i)
10 LIST_APPEND 2
12 JUMP_ABSOLUTE 4
>> 14 RETURN_VALUE
In [6]:
You can see that on offset 18 in the first function we have an append attribute while there’s no such thing in second function using list comprehension. All those extra bytecodes will make the appending approach slower and since in this case you’ll have loading of the append attribute in each iteration, in the end it will make the code to take approximately twice as slower as the second function using only list comprehension.