答案取决于版本和情况。最近版本的Python（从3.3开始）的最一般的答案首先由J.F.Sebastian在下面描述。1它使用Pool.starmap方法，接受一系列参数元组。然后，它会自动将每个元组中的参数解包，并将它们传递给给定的函数：

import multiprocessing
from itertools import product

def merge_names(a, b):
    return '{} & {}'.format(a, b)

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with multiprocessing.Pool(processes=3) as pool:
        results = pool.starmap(merge_names, product(names, repeat=2))
    print(results)

# Output: ['Brown & Brown', 'Brown & Wilson', 'Brown & Bartlett', ...

对于早期版本的Python，您需要编写一个助手函数来显式地解包参数。如果要与一起使用，还需要编写一个包装器，将Pool转换为上下文管理器。（感谢穆恩指出了这一点。）

import multiprocessing
from itertools import product
from contextlib import contextmanager

def merge_names(a, b):
    return '{} & {}'.format(a, b)

def merge_names_unpack(args):
    return merge_names(*args)

@contextmanager
def poolcontext(*args, **kwargs):
    pool = multiprocessing.Pool(*args, **kwargs)
    yield pool
    pool.terminate()

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with poolcontext(processes=3) as pool:
        results = pool.map(merge_names_unpack, product(names, repeat=2))
    print(results)

# Output: ['Brown & Brown', 'Brown & Wilson', 'Brown & Bartlett', ...

在更简单的情况下，使用固定的第二个参数，也可以使用partial，但仅在Python 2.7+中使用。

import multiprocessing
from functools import partial
from contextlib import contextmanager

@contextmanager
def poolcontext(*args, **kwargs):
    pool = multiprocessing.Pool(*args, **kwargs)
    yield pool
    pool.terminate()

def merge_names(a, b):
    return '{} & {}'.format(a, b)

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with poolcontext(processes=3) as pool:
        results = pool.map(partial(merge_names, b='Sons'), names)
    print(results)

# Output: ['Brown & Sons', 'Wilson & Sons', 'Bartlett & Sons', ...

1.这大部分都是由他的答案激发的，而他的答案很可能应该被接受。但由于这本书一直停留在顶端，似乎最好为未来读者改进它。

pool.map是否有支持多个参数的变体？

Python 3.3包含pool.starmap（）方法：

#!/usr/bin/env python3
from functools import partial
from itertools import repeat
from multiprocessing import Pool, freeze_support

def func(a, b):
    return a + b

def main():
    a_args = [1,2,3]
    second_arg = 1
    with Pool() as pool:
        L = pool.starmap(func, [(1, 1), (2, 1), (3, 1)])
        M = pool.starmap(func, zip(a_args, repeat(second_arg)))
        N = pool.map(partial(func, b=second_arg), a_args)
        assert L == M == N

if __name__=="__main__":
    freeze_support()
    main()

对于旧版本：

#!/usr/bin/env python2
import itertools
from multiprocessing import Pool, freeze_support

def func(a, b):
    print a, b

def func_star(a_b):
    """Convert `f([1,2])` to `f(1,2)` call."""
    return func(*a_b)

def main():
    pool = Pool()
    a_args = [1,2,3]
    second_arg = 1
    pool.map(func_star, itertools.izip(a_args, itertools.repeat(second_arg)))

if __name__=="__main__":
    freeze_support()
    main()

输出

1 1
2 1
3 1

注意这里是如何使用itertools.izip（）和itertools.crepeat（）的。

由于@unsubu提到的错误，您不能在Python 2.6上使用functools.partial（）或类似功能，因此应该显式定义简单包装函数func_tar（）。另请参阅uptimebox建议的解决方法。

如何获取多个参数：

def f1(args):
    a, b, c = args[0] , args[1] , args[2]
    return a+b+c

if __name__ == "__main__":
    import multiprocessing
    pool = multiprocessing.Pool(4) 

    result1 = pool.map(f1, [ [1,2,3] ])
    print(result1)

将所有参数存储为元组数组。

该示例表示，通常调用函数为：

def mainImage(fragCoord: vec2, iResolution: vec3, iTime: float) -> vec3:

而是传递一个元组并解压缩参数：

def mainImage(package_iter) -> vec3:
    fragCoord = package_iter[0]
    iResolution = package_iter[1]
    iTime = package_iter[2]

预先使用循环构建元组：

package_iter = []
iResolution = vec3(nx, ny, 1)
for j in range((ny-1), -1, -1):
    for i in range(0, nx, 1):
        fragCoord: vec2 = vec2(i, j)
        time_elapsed_seconds = 10
        package_iter.append((fragCoord, iResolution, time_elapsed_seconds))

然后通过传递元组数组来执行所有using map：

array_rgb_values = []

with concurrent.futures.ProcessPoolExecutor() as executor:
    for val in executor.map(mainImage, package_iter):
        fragColor = val
        ir = clip(int(255* fragColor.r), 0, 255)
        ig = clip(int(255* fragColor.g), 0, 255)
        ib = clip(int(255* fragColor.b), 0, 255)

        array_rgb_values.append((ir, ig, ib))

我知道Python有*和**用于开箱，但我还没有尝试过。

使用高级库并发期货也比使用低级多处理库更好。

对我来说，以下是一个简单明了的解决方案：

from multiprocessing.pool import ThreadPool
from functools import partial
from time import sleep
from random import randint

def dosomething(var,s):
    sleep(randint(1,5))
    print(var)
    return var + s

array = ["a", "b", "c", "d", "e"]
with ThreadPool(processes=5) as pool:
    resp_ = pool.map(partial(dosomething,s="2"), array)
    print(resp_)

输出：

a
b
d
e
c
['a2', 'b2', 'c2', 'd2', 'e2']

在J.F.Sebastian的回答中了解了itertools之后，我决定更进一步，编写一个关注并行化的parmap包，在Python 2.7和Python 3.2（以及更高版本）中提供可以接受任意数量位置参数的map和starmap函数。

安装

pip install parmap

如何并行化：

import parmap
# If you want to do:
y = [myfunction(x, argument1, argument2) for x in mylist]
# In parallel:
y = parmap.map(myfunction, mylist, argument1, argument2)

# If you want to do:
z = [myfunction(x, y, argument1, argument2) for (x,y) in mylist]
# In parallel:
z = parmap.starmap(myfunction, mylist, argument1, argument2)

# If you want to do:
listx = [1, 2, 3, 4, 5, 6]
listy = [2, 3, 4, 5, 6, 7]
param = 3.14
param2 = 42
listz = []
for (x, y) in zip(listx, listy):
        listz.append(myfunction(x, y, param1, param2))
# In parallel:
listz = parmap.starmap(myfunction, zip(listx, listy), param1, param2)

我已经将parmap上传到PyPI和GitHub存储库。

例如，问题的答案如下：

import parmap

def harvester(case, text):
    X = case[0]
    text+ str(X)

if __name__ == "__main__":
    case = RAW_DATASET  # assuming this is an iterable
    parmap.map(harvester, case, "test", chunksize=1)