这里是一个例子:
list_ = [5, 'cat', 0xDEADBEEF, 4.0]
for offset in range(len(list_)):
result = 0
for elem in list_[offset:]:
result = func(result, elem)
return result
其中func
是不可交换的。
在上面的代码中,list_[offset:]
将创建一个新列表,但是我只需要查看list_
。我该如何优化呢?
答案 0 :(得分:1)
要复制切片,但每次迭代需要O(1)次,可以将collections.deque
与popleft
一起使用:
from collections import deque
dq = deque(list_)
for i in range(len(dq)):
print(dq)
dq.popleft()
结果:
deque([5, 'cat', 3735928559, 4.0])
deque(['cat', 3735928559, 4.0])
deque([3735928559, 4.0])
deque([4.0])
这应该比列表切片更有效:请参见deque.popleft() and list.pop(0). Is there performance difference?。还请注意列表切片在O( k )时间中进行,其中 k 是切片的长度。
答案 1 :(得分:0)
使用@jpp的答案所建议的collections.deque
有时有时会更快。。 slice和deque
解决方案的性能相似,并且比例如使用itertools.islice
或仅使用list_
上的普通索引。
我尝试使版本总体上大致相同,并使用了一个虚拟func
来计数循环:
from __future__ import print_function
from collections import deque
from itertools import islice
from timeit import repeat
import numpy as np
list_ = [5, 'cat', 0xDEADBEEF, 4.0]
list_3k = list_ * 3000
def func(x, y):
return x + 1
def f1():
"""list slice"""
result = 0
for offset in range(len(list_)):
for elem in list_[offset:]:
result = func(result, elem)
return result
def f2():
"""deque"""
dq = deque(list_)
result = 0
for i in range(len(dq)):
for elem in dq:
result = func(result, elem)
dq.popleft()
return result
def f3():
"""itertools slice"""
result = 0
for offset in range(len(list_)):
for elem in islice(list_, offset, None):
result = func(result, elem)
return result
def f4():
"""basics"""
result = 0
n = len(list_)
for offset in range(n):
j = offset
while j < n:
result = func(result, list_[j])
j += 1
return result
def timeit(fn, number):
print("{}: {} loops".format(fn.__name__, fn()))
times = repeat(fn, repeat=3, number=number)
print("{:.3f}s ± {:.3f}ms".format(np.mean(times), np.std(times)*1000))
if __name__ == "__main__":
fs = [f1, f2, f3, f4]
for f in fs:
timeit(f, number=1000000)
list_ = list_3k
print()
for f in fs:
timeit(f, number=3)
结果:
bash-3.2$ python3 foo.py
f1: 10 loops
2.161s ± 9.333ms
f2: 10 loops
2.134s ± 5.127ms
f3: 10 loops
2.340s ± 11.928ms
f4: 10 loops
2.315s ± 4.615ms
f1: 72006000 loops
23.073s ± 109.857ms
f2: 72006000 loops
23.495s ± 596.822ms
f3: 72006000 loops
24.432s ± 553.167ms
f4: 72006000 loops
40.509s ± 128.367ms