我对这个问题的解决方案是将函数和线程包装在一个类中。不需要使用池、队列或c类型变量传递。它也是非阻塞的。而是检查状态。参见代码末尾如何使用它的示例。

import threading

class ThreadWorker():
    '''
    The basic idea is given a function create an object.
    The object can then run the function in a thread.
    It provides a wrapper to start it,check its status,and get data out the function.
    '''
    def __init__(self,func):
        self.thread = None
        self.data = None
        self.func = self.save_data(func)

    def save_data(self,func):
        '''modify function to save its returned data'''
        def new_func(*args, **kwargs):
            self.data=func(*args, **kwargs)

        return new_func

    def start(self,params):
        self.data = None
        if self.thread is not None:
            if self.thread.isAlive():
                return 'running' #could raise exception here

        #unless thread exists and is alive start or restart it
        self.thread = threading.Thread(target=self.func,args=params)
        self.thread.start()
        return 'started'

    def status(self):
        if self.thread is None:
            return 'not_started'
        else:
            if self.thread.isAlive():
                return 'running'
            else:
                return 'finished'

    def get_results(self):
        if self.thread is None:
            return 'not_started' #could return exception
        else:
            if self.thread.isAlive():
                return 'running'
            else:
                return self.data

def add(x,y):
    return x +y

add_worker = ThreadWorker(add)
print add_worker.start((1,2,))
print add_worker.status()
print add_worker.get_results()

FWIW，多处理模块使用Pool类提供了一个很好的接口。如果您希望坚持使用线程而不是进程，可以直接使用multiprocessing.pool.ThreadPool类作为替代。

def foo(bar, baz):
  print 'hello {0}'.format(bar)
  return 'foo' + baz

from multiprocessing.pool import ThreadPool
pool = ThreadPool(processes=1)

async_result = pool.apply_async(foo, ('world', 'foo')) # tuple of args for foo

# do some other stuff in the main process

return_val = async_result.get()  # get the return value from your function.

一种常见的解决方案是用装饰器来包装函数foo

result = queue.Queue()

def task_wrapper(*args):
    result.put(target(*args))

那么整个代码可能是这样的

result = queue.Queue()

def task_wrapper(*args):
    result.put(target(*args))

threads = [threading.Thread(target=task_wrapper, args=args) for args in args_list]

for t in threads:
    t.start()
    while(True):
        if(len(threading.enumerate()) < max_num):
            break
for t in threads:
    t.join()
return result

Note

一个重要的问题是返回值可能是无序的。 (事实上，返回值不一定保存到队列中，因为您可以选择任意线程安全的数据结构)

Kindall在Python3中的回答

class ThreadWithReturnValue(Thread):
    def __init__(self, group=None, target=None, name=None,
                 args=(), kwargs={}, *, daemon=None):
        Thread.__init__(self, group, target, name, args, kwargs, daemon)
        self._return = None 

    def run(self):
        try:
            if self._target:
                self._return = self._target(*self._args, **self._kwargs)
        finally:
            del self._target, self._args, self._kwargs 

    def join(self,timeout=None):
        Thread.join(self,timeout)
        return self._return

在Python 3.2+中，stdlib concurrent。futures模块为线程提供了一个更高级别的API，包括将返回值或异常从工作线程传递回主线程:

import concurrent.futures

def foo(bar):
    print('hello {}'.format(bar))
    return 'foo'

with concurrent.futures.ThreadPoolExecutor() as executor:
    future = executor.submit(foo, 'world!')
    return_value = future.result()
    print(return_value)

定义你的目标 1)采取一个论点q 2)用q.put(foo)替换return foo的任何语句;返回

一个函数

def func(a):
    ans = a * a
    return ans

将成为

def func(a, q):
    ans = a * a
    q.put(ans)
    return

然后你就可以这样做了

from Queue import Queue
from threading import Thread

ans_q = Queue()
arg_tups = [(i, ans_q) for i in xrange(10)]

threads = [Thread(target=func, args=arg_tup) for arg_tup in arg_tups]
_ = [t.start() for t in threads]
_ = [t.join() for t in threads]
results = [q.get() for _ in xrange(len(threads))]

你可以使用函数装饰器/包装器来实现它，这样你就可以使用现有的函数作为目标，而不需要修改它们，但要遵循这个基本方案。