python - 什么更有效地迭代列表并调用函数或将列表传递给函数

Question

我有一个充满字典的列表，我想为每个字典应用一个函数。什么是更有效或最佳实践，以迭代列表并将字典传递给每次迭代的函数。就像这里显示的那样:

dict1 = {"foo": 8, "bar": 4, "baz":2}
dict2 = {"foo": 3, "bar": 6, "baz":3}
...

dict_list = [dict1, dict2, ...]

def function_iterate(dict):
    ...

for dict in dict_list:
    function_iterate(dict)

还是传递列表并在函数内迭代更好:

dict1 = {"foo": 8, "bar": 4, "baz":2}
dict2 = {"foo": 3, "bar": 6, "baz":3}
...
dict_list = [dict1, dict2, ...]


def function_iterate(dict_list):
    for dict in dict_list:
        ...

function_iterate(dict_list)

或者您可能知道其他方法。如果两种方法都同样有效，你更喜欢哪一种，为什么？

Answer 1

在性能方面，发送整个列表会更快，因为 python 中的函数调用很昂贵(性能方面)。

时序测试示例 -

代码-

l = [{1:2},{2:3},{3:4}]

def func1(d):
        for k in d:
                d[k] += 1

def func2(l):
        for d in l:
                for k in d:
                        d[k] += 1

计时结果-

In [29]: %%timeit
   ....: for d in l:
   ....:     func1(d)
   ....:
The slowest run took 7.57 times longer than the fastest. This could mean that an intermediate result is being cached
1000000 loops, best of 3: 1.16 µs per loop

In [30]: %%timeit
   ....: func2(l)
   ....:
The slowest run took 9.25 times longer than the fastest. This could mean that an intermediate result is being cached
1000000 loops, best of 3: 988 ns per loop

In [31]: %%timeit
   ....: [func1(d) for d in l]
   ....:
100000 loops, best of 3: 1.98 µs per loop

In [33]: %%timeit
   ....: list(map(func1,l))
   ....:
The slowest run took 4.42 times longer than the fastest. This could mean that an intermediate result is being cached
100000 loops, best of 3: 2.5 µs per loop

---

为了可读性，map() 会更好，因为它更具可读性。示例 -

对于 Python 3.x -

list(map(function, list_of_dicts))

对于 Python 2.x -

map(function, list_of_dicts)