问题
I don't come from a software/computer science background but I love to code in Python and can generally understand why things are faster. I am really curious to know why this for loop runs faster than the dictionary comprehension. Any insights?
Problem : Given a dictionary
awith these keys and values, return a dictionary with the values as keys and the keys as values. (challenge: do this in one line)
and the code
a = {'a':'hi','b':'hey','c':'yo'}
b = {}
for i,j in a.items():
b[j]=i
%% timeit 932 ns ± 37.2 ns per loop
b = {v: k for k, v in a.items()}
%% timeit 1.08 µs ± 16.4 ns per loop
回答1:
You are testing with way too small an input; while a dictionary comprehension doesn't have as much of a performance advantage against a for loop when compared to a list comprehension, for realistic problem sizes it can and does beat for loops, especially when targeting a global name.
Your input consists of just 3 key-value pairs. Testing with 1000 elements instead, we see that the timings are very close instead:
>>> import timeit
>>> from random import choice, randint; from string import ascii_lowercase as letters
>>> looped = '''\
... b = {}
... for i,j in a.items():
... b[j]=i
... '''
>>> dictcomp = '''b = {v: k for k, v in a.items()}'''
>>> def rs(): return ''.join([choice(letters) for _ in range(randint(3, 15))])
...
>>> a = {rs(): rs() for _ in range(1000)}
>>> len(a)
1000
>>> count, total = timeit.Timer(looped, 'from __main__ import a').autorange()
>>> (total / count) * 1000000 # microseconds per run
66.62004760000855
>>> count, total = timeit.Timer(dictcomp, 'from __main__ import a').autorange()
>>> (total / count) * 1000000 # microseconds per run
64.5464928005822
The difference is there, the dict comp is faster but only just at this scale. With 100 times as many key-value pairs the difference is a bit bigger:
>>> a = {rs(): rs() for _ in range(100000)}
>>> len(a)
98476
>>> count, total = timeit.Timer(looped, 'from __main__ import a').autorange()
>>> total / count * 1000 # milliseconds, different scale!
15.48140200029593
>>> count, total = timeit.Timer(dictcomp, 'from __main__ import a').autorange()
>>> total / count * 1000 # milliseconds, different scale!
13.674790799996117
which is not that big a difference when you consider both processed nearly 100k key-value pairs. Still, the for loop is clearly slower.
So why the speed difference with 3 elements? Because a comprehension (dictionary, set, list comprehensions or a generator expression) is under the hood implemented as a new function, and calling that function has a base cost the plain loop doesn't have to pay.
Here's the disassembly for the bytecode for both alternatives; note the MAKE_FUNCTION and CALL_FUNCTION opcodes in the top-level bytecode for the dict comprehension, there is a separate section for what that function then does, and there are actually very few differences in between the two approaches here:
>>> import dis
>>> dis.dis(looped)
1 0 BUILD_MAP 0
2 STORE_NAME 0 (b)
2 4 SETUP_LOOP 28 (to 34)
6 LOAD_NAME 1 (a)
8 LOAD_METHOD 2 (items)
10 CALL_METHOD 0
12 GET_ITER
>> 14 FOR_ITER 16 (to 32)
16 UNPACK_SEQUENCE 2
18 STORE_NAME 3 (i)
20 STORE_NAME 4 (j)
3 22 LOAD_NAME 3 (i)
24 LOAD_NAME 0 (b)
26 LOAD_NAME 4 (j)
28 STORE_SUBSCR
30 JUMP_ABSOLUTE 14
>> 32 POP_BLOCK
>> 34 LOAD_CONST 0 (None)
36 RETURN_VALUE
>>> dis.dis(dictcomp)
1 0 LOAD_CONST 0 (<code object <dictcomp> at 0x11d6ade40, file "<dis>", line 1>)
2 LOAD_CONST 1 ('<dictcomp>')
4 MAKE_FUNCTION 0
6 LOAD_NAME 0 (a)
8 LOAD_METHOD 1 (items)
10 CALL_METHOD 0
12 GET_ITER
14 CALL_FUNCTION 1
16 STORE_NAME 2 (b)
18 LOAD_CONST 2 (None)
20 RETURN_VALUE
Disassembly of <code object <dictcomp> at 0x11d6ade40, file "<dis>", line 1>:
1 0 BUILD_MAP 0
2 LOAD_FAST 0 (.0)
>> 4 FOR_ITER 14 (to 20)
6 UNPACK_SEQUENCE 2
8 STORE_FAST 1 (k)
10 STORE_FAST 2 (v)
12 LOAD_FAST 1 (k)
14 LOAD_FAST 2 (v)
16 MAP_ADD 2
18 JUMP_ABSOLUTE 4
>> 20 RETURN_VALUE
The material differences: the looped code uses LOAD_NAME for b each iteration, and STORE_SUBSCR to store the key-value pair in dict loaded. The dictionary comprehension uses MAP_ADD to achieve the same thing as STORE_SUBSCR but doesn't have to load that b name each time.
But with 3 iterations only, the MAKE_FUNCTION / CALL_FUNCTION combo the dict comprehension has to execute is the real drag on the performance:
>>> make_and_call = '(lambda i: None)(None)'
>>> dis.dis(make_and_call)
1 0 LOAD_CONST 0 (<code object <lambda> at 0x11d6ab270, file "<dis>", line 1>)
2 LOAD_CONST 1 ('<lambda>')
4 MAKE_FUNCTION 0
6 LOAD_CONST 2 (None)
8 CALL_FUNCTION 1
10 RETURN_VALUE
Disassembly of <code object <lambda> at 0x11d6ab270, file "<dis>", line 1>:
1 0 LOAD_CONST 0 (None)
2 RETURN_VALUE
>>> count, total = timeit.Timer(make_and_call).autorange()
>>> total / count * 1000000
0.12945385499915574
More than 0.1 μs to create a function object with one argument, and call it (with an extra LOAD_CONST for the None value we pass in)! And that's just about the difference between the looped and comprehension timings for 3 key-value pairs.
You can liken this to being surprised that a man with a shovel can dig a small hole faster than a backhoe can. The backhoe can certainly dig fast, but a man with a shovel can get started faster if you need to get the backhoe started and moved into position first!
Beyond a few key-value pairs (digging a bigger hole), the function create and call cost fades away into nothingness. At this point the dict comprehension and the explicit loop basically do the same thing:
- take the next key-value pair, pop those on the stack
- call the
dict.__setitem__hook via a bytecode operation with the top two items on the stack (eitherSTORE_SUBSCRorMAP_ADD. This doesn't count as a 'function call' as it's all internally handled in the interpreter loop.
This is different from a list comprehension, where the plain loop version would have to use list.append(), involving an attribute lookup, and a function call each loop iteration. The list comprehension speed advantage comes from this difference; see Python list comprehension expensive
What a dict comprehension does add, is that the target dictionary name only needs to be looked up once, when binding b to the the final dictionary object. If the target dictionary is a global instead of a local variable, the comprehension wins, hands down:
>>> a = {rs(): rs() for _ in range(1000)}
>>> len(a)
1000
>>> namespace = {}
>>> count, total = timeit.Timer(looped, 'from __main__ import a; global b', globals=namespace).autorange()
>>> (total / count) * 1000000
76.72348440100905
>>> count, total = timeit.Timer(dictcomp, 'from __main__ import a; global b', globals=namespace).autorange()
>>> (total / count) * 1000000
64.72114819916897
>>> len(namespace['b'])
1000
So just use a dict comprehension. The difference with < 30 elements to process is too small to care about, and the moment you are generating a global or have more items, the dict comprehension wins out anyway.
回答2:
This question, in some senses, is quite similar to Why is a list comprehension so much faster than appending to a list? which I've answered a long while ago. However, the reason that this behavior is surprising to you is obviously because your dictionary is way too small to overcome the cost of creating a new function frame and pushing/pulling it in stack. To understand that better let's go under the skin of tow snippets you have:
In [1]: a = {'a':'hi','b':'hey','c':'yo'}
...:
...: def reg_loop(a):
...: b = {}
...: for i,j in a.items():
...: b[j]=i
...:
In [2]: def dict_comp(a):
...: b = {v: k for k, v in a.items()}
...:
In [3]:
In [3]: %timeit reg_loop(a)
529 ns ± 7.89 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [4]:
In [4]: %timeit dict_comp(a)
656 ns ± 5.39 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [5]:
In [5]: import dis
In [6]: dis.dis(reg_loop)
4 0 BUILD_MAP 0
2 STORE_FAST 1 (b)
5 4 SETUP_LOOP 28 (to 34)
6 LOAD_FAST 0 (a)
8 LOAD_METHOD 0 (items)
10 CALL_METHOD 0
12 GET_ITER
>> 14 FOR_ITER 16 (to 32)
16 UNPACK_SEQUENCE 2
18 STORE_FAST 2 (i)
20 STORE_FAST 3 (j)
6 22 LOAD_FAST 2 (i)
24 LOAD_FAST 1 (b)
26 LOAD_FAST 3 (j)
28 STORE_SUBSCR
30 JUMP_ABSOLUTE 14
>> 32 POP_BLOCK
>> 34 LOAD_CONST 0 (None)
36 RETURN_VALUE
In [7]:
In [7]: dis.dis(dict_comp)
2 0 LOAD_CONST 1 (<code object <dictcomp> at 0x7fbada1adf60, file "<ipython-input-2-aac022159794>", line 2>)
2 LOAD_CONST 2 ('dict_comp.<locals>.<dictcomp>')
4 MAKE_FUNCTION 0
6 LOAD_FAST 0 (a)
8 LOAD_METHOD 0 (items)
10 CALL_METHOD 0
12 GET_ITER
14 CALL_FUNCTION 1
16 STORE_FAST 1 (b)
18 LOAD_CONST 0 (None)
20 RETURN_VALUE
On second disassembled code (dict comprehension) you have a MAKE_FUNCTION opcode which as it's also stated in documentation pushes a new function object on the stack. and later CALL_FUNCTION which Calls a callable object with positional arguments. and then:
pops all arguments and the callable object off the stack, calls the callable object with those arguments, and pushes the return value returned by the callable object.
All these operations have their costs but when the dictionary gets larger the cost of assigning the key-value items to the dictionary will become larger than creating a function under the hood. In other words cost of calling the __setitem__ method of the dictionary from a certain point will exceed the cost of creating and suspending a dictionary object on the fly.
Also, note that certainly there are multiple other operations (OP_CODES in this case) that play a crucial role in this game which I think worth investigating and considering which I'm gonna live it to you as a practice ;).
来源：https://stackoverflow.com/questions/52542742/why-is-this-loop-faster-than-a-dictionary-comprehension-for-creating-a-dictionar