如上所述，多处理池比基本线程要慢得多。使用一些回答中提出的队列是一种非常有效的替代方法。我已经将它与字典一起使用，以便能够运行许多小线程，并通过将它们与字典结合来恢复多个答案:

#!/usr/bin/env python3

import threading
# use Queue for python2
import queue
import random

LETTERS = 'abcdefghijklmnopqrstuvwxyz'
LETTERS = [ x for x in LETTERS ]

NUMBERS = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

def randoms(k, q):
    result = dict()
    result['letter'] = random.choice(LETTERS)
    result['number'] = random.choice(NUMBERS)
    q.put({k: result})

threads = list()
q = queue.Queue()
results = dict()

for name in ('alpha', 'oscar', 'yankee',):
    threads.append( threading.Thread(target=randoms, args=(name, q)) )
    threads[-1].start()
_ = [ t.join() for t in threads ]
while not q.empty():
    results.update(q.get())

print(results)

join总是返回None，我认为你应该子类化Thread来处理返回代码等。

另一个不需要更改现有代码的解决方案:

import Queue             # Python 2.x
#from queue import Queue # Python 3.x

from threading import Thread

def foo(bar):
    print 'hello {0}'.format(bar)     # Python 2.x
    #print('hello {0}'.format(bar))   # Python 3.x
    return 'foo'

que = Queue.Queue()      # Python 2.x
#que = Queue()           # Python 3.x

t = Thread(target=lambda q, arg1: q.put(foo(arg1)), args=(que, 'world!'))
t.start()
t.join()
result = que.get()
print result             # Python 2.x
#print(result)           # Python 3.x

它也可以很容易地调整到多线程环境:

import Queue             # Python 2.x
#from queue import Queue # Python 3.x
from threading import Thread

def foo(bar):
    print 'hello {0}'.format(bar)     # Python 2.x
    #print('hello {0}'.format(bar))   # Python 3.x
    return 'foo'

que = Queue.Queue()      # Python 2.x
#que = Queue()           # Python 3.x

threads_list = list()

t = Thread(target=lambda q, arg1: q.put(foo(arg1)), args=(que, 'world!'))
t.start()
threads_list.append(t)

# Add more threads here
...
threads_list.append(t2)
...
threads_list.append(t3)
...

# Join all the threads
for t in threads_list:
    t.join()

# Check thread's return value
while not que.empty():
    result = que.get()
    print result         # Python 2.x
    #print(result)       # Python 3.x

我知道这个线程是旧的....但我也遇到了同样的问题…如果你愿意使用thread.join()

import threading

class test:

    def __init__(self):
        self.msg=""

    def hello(self,bar):
        print('hello {}'.format(bar))
        self.msg="foo"


    def main(self):
        thread = threading.Thread(target=self.hello, args=('world!',))
        thread.start()
        thread.join()
        print(self.msg)

g=test()
g.main()

我见过的一种方法是将一个可变对象(如列表或字典)传递给线程的构造函数，同时传递一个索引或其他某种类型的标识符。然后线程可以将结果存储在该对象的专用槽中。例如:

def foo(bar, result, index):
    print 'hello {0}'.format(bar)
    result[index] = "foo"

from threading import Thread

threads = [None] * 10
results = [None] * 10

for i in range(len(threads)):
    threads[i] = Thread(target=foo, args=('world!', results, i))
    threads[i].start()

# do some other stuff

for i in range(len(threads)):
    threads[i].join()

print " ".join(results)  # what sound does a metasyntactic locomotive make?

如果你真的想要join()返回被调用函数的返回值，你可以用Thread子类来实现，如下所示:

from threading import Thread

def foo(bar):
    print 'hello {0}'.format(bar)
    return "foo"

class ThreadWithReturnValue(Thread):
    def __init__(self, group=None, target=None, name=None,
                 args=(), kwargs={}, Verbose=None):
        Thread.__init__(self, group, target, name, args, kwargs, Verbose)
        self._return = None
    def run(self):
        if self._Thread__target is not None:
            self._return = self._Thread__target(*self._Thread__args,
                                                **self._Thread__kwargs)
    def join(self):
        Thread.join(self)
        return self._return

twrv = ThreadWithReturnValue(target=foo, args=('world!',))

twrv.start()
print twrv.join()   # prints foo

这有点麻烦，因为一些名称混乱，它访问特定于线程实现的“私有”数据结构……但它确实有效。

对于Python 3:

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

    def run(self):
        if self._target is not None:
            self._return = self._target(*self._args,
                                                **self._kwargs)
    def join(self, *args):
        Thread.join(self, *args)
        return self._return

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.