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Question

有没有更简洁，有效或简单的pythonic方法来执行以下操作？

def product(list):
    p = 1
    for i in list:
        p *= i
    return p

编辑：

我实际上发现这比使用operator.mul快一点：

from operator import mul
# from functools import reduce # python3 compatibility

def with_lambda(list):
    reduce(lambda x, y: x * y, list)

def without_lambda(list):
    reduce(mul, list)

def forloop(list):
    r = 1
    for x in list:
        r *= x
    return r

import timeit

a = range(50)
b = range(1,50)#no zero
t = timeit.Timer("with_lambda(a)", "from __main__ import with_lambda,a")
print("with lambda:", t.timeit())
t = timeit.Timer("without_lambda(a)", "from __main__ import without_lambda,a")
print("without lambda:", t.timeit())
t = timeit.Timer("forloop(a)", "from __main__ import forloop,a")
print("for loop:", t.timeit())

t = timeit.Timer("with_lambda(b)", "from __main__ import with_lambda,b")
print("with lambda (no 0):", t.timeit())
t = timeit.Timer("without_lambda(b)", "from __main__ import without_lambda,b")
print("without lambda (no 0):", t.timeit())
t = timeit.Timer("forloop(b)", "from __main__ import forloop,b")
print("for loop (no 0):", t.timeit())

给我

('with lambda:', 17.755449056625366)
('without lambda:', 8.2084708213806152)
('for loop:', 7.4836349487304688)
('with lambda (no 0):', 22.570688009262085)
('without lambda (no 0):', 12.472226858139038)
('for loop (no 0):', 11.04065990447998)

Answer 1

不使用lambda：

from operator import mul
reduce(mul, list, 1)

更好，更快。使用python 2.7.5

from operator import mul
import numpy as np
import numexpr as ne
# from functools import reduce # python3 compatibility

a = range(1, 101)
%timeit reduce(lambda x, y: x * y, a)   # (1)
%timeit reduce(mul, a)                  # (2)
%timeit np.prod(a)                      # (3)
%timeit ne.evaluate("prod(a)")          # (4)

在以下配置中：

a = range(1, 101)  # A
a = np.array(a)    # B
a = np.arange(1, 1e4, dtype=int) #C
a = np.arange(1, 1e5, dtype=float) #D

python 2.7.5的结果

       | 1 | 2 | 3 | 4 |
------- + ----------- + ----------- + ----------- + ------ ----- +
 20.8 µs 13.3 µs 22.6 µs 39.6 µs     
 B 106 µs 95.3 µs 5.92 µs 26.1 µs
 C 4.34毫秒3.51毫秒16.7微秒38.9微秒
 D 46.6毫秒38.5毫秒180 µs 216 µs

结果：np.prod如果np.array用作数据结构，则速度最快（小型阵列为18x，大型阵列为250x）

使用python 3.3.2：

       | 1 | 2 | 3 | 4 |
------- + ----------- + ----------- + ----------- + ------ ----- +
 23.6 µs 12.3 µs 68.6 µs 84.9 µs     
 B 133 µs 107 µs 7.42 µs 27.5 µs
 C 4.79毫秒3.74毫秒18.6微秒40.9微秒
 D 48.4毫秒36.8毫秒187微秒214微秒

python 3更慢吗？