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)

我找到的大多数答案都很长，需要熟悉其他模块或高级python特性，除非他们已经熟悉答案所谈论的一切，否则会让人感到困惑。

简化方法的工作代码:

import threading

class ThreadWithResult(threading.Thread):
    def __init__(self, group=None, target=None, name=None, args=(), kwargs={}, *, daemon=None):
        def function():
            self.result = target(*args, **kwargs)
        super().__init__(group=group, target=function, name=name, daemon=daemon)

示例代码:

import time, random


def function_to_thread(n):
    count = 0
    while count < 3:
            print(f'still running thread {n}')
            count +=1
            time.sleep(3)
    result = random.random()
    print(f'Return value of thread {n} should be: {result}')
    return result


def main():
    thread1 = ThreadWithResult(target=function_to_thread, args=(1,))
    thread2 = ThreadWithResult(target=function_to_thread, args=(2,))
    thread1.start()
    thread2.start()
    thread1.join()
    thread2.join()
    print(thread1.result)
    print(thread2.result)

main()

解释: 我想大大简化事情，所以我创建了一个ThreadWithResult类，并让它继承threading.Thread。__init__中的嵌套函数函数调用我们想要保存值的线程函数，并将该嵌套函数的结果保存为实例属性self。线程执行完成后的结果。

创建this的实例与创建threading.Thread的实例是相同的。将希望在新线程上运行的函数传递给目标参数，将函数可能需要的任何参数传递给args参数，将任何关键字参数传递给kwargs参数。

e.g.

my_thread = ThreadWithResult(target=my_function, args=(arg1, arg2, arg3))

我认为这比绝大多数答案更容易理解，而且这种方法不需要额外的导入!我加入了time和random模块来模拟线程的行为，但它们并不是实现最初问题中所要求的功能所必需的。

我知道我是在这个问题被问到很久之后才回答的，但我希望这能在未来帮助更多的人!

编辑:我创建了保存线程结果的PyPI包，允许你访问上面相同的代码，并在项目中重用它(GitHub代码在这里)。PyPI包完全扩展了线程。线程类，因此您可以设置在线程上设置的任何属性。线程在ThreadWithResult类!

上面的原始答案介绍了这个子类背后的主要思想，但要了解更多信息，请参阅这里更详细的解释(来自模块docstring)。

快速使用示例:

pip3 install -U save-thread-result     # MacOS/Linux
pip  install -U save-thread-result     # Windows

python3     # MacOS/Linux
python      # Windows

from save_thread_result import ThreadWithResult

# As of Release 0.0.3, you can also specify values for
#`group`, `name`, and `daemon` if you want to set those
# values manually.
thread = ThreadWithResult(
    target = my_function,
    args   = (my_function_arg1, my_function_arg2, ...)
    kwargs = {my_function_kwarg1: kwarg1_value, my_function_kwarg2: kwarg2_value, ...}
)

thread.start()
thread.join()
if getattr(thread, 'result', None):
    print(thread.result)
else:
    # thread.result attribute not set - something caused
    # the thread to terminate BEFORE the thread finished
    # executing the function passed in through the
    # `target` argument
    print('ERROR! Something went wrong while executing this thread, and the function you passed in did NOT complete!!')

# seeing help about the class and information about the threading.Thread super class methods and attributes available:
help(ThreadWithResult)

考虑到@iman对@JakeBiesinger回答的评论，我重新组合了它，使其具有不同数量的线程:

from multiprocessing.pool import ThreadPool

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

numOfThreads = 3 
results = []

pool = ThreadPool(numOfThreads)

for i in range(0, numOfThreads):
    results.append(pool.apply_async(foo, ('world', 'foo'))) # tuple of args for foo)

# do some other stuff in the main process
# ...
# ...

results = [r.get() for r in results]
print results

pool.close()
pool.join()

我正在使用这个包装器，它可以轻松地将任何函数转换为在线程中运行-照顾它的返回值或异常。它不会增加队列开销。

def threading_func(f):
    """Decorator for running a function in a thread and handling its return
    value or exception"""
    def start(*args, **kw):
        def run():
            try:
                th.ret = f(*args, **kw)
            except:
                th.exc = sys.exc_info()
        def get(timeout=None):
            th.join(timeout)
            if th.exc:
                raise th.exc[0], th.exc[1], th.exc[2] # py2
                ##raise th.exc[1] #py3                
            return th.ret
        th = threading.Thread(None, run)
        th.exc = None
        th.get = get
        th.start()
        return th
    return start

用法示例

def f(x):
    return 2.5 * x
th = threading_func(f)(4)
print("still running?:", th.is_alive())
print("result:", th.get(timeout=1.0))

@threading_func
def th_mul(a, b):
    return a * b
th = th_mul("text", 2.5)

try:
    print(th.get())
except TypeError:
    print("exception thrown ok.")

线程模块注意事项

线程函数的舒适返回值和异常处理是“python”的常见需求，而且threading模块应该已经提供了——可能直接在标准Thread类中。对于简单的任务，ThreadPool有太多的开销——3个管理线程，很多官僚主义。不幸的是，线程的布局最初是从Java中复制的——例如，从仍然无用的构造函数参数组1 (!)

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

您可以在线程函数的作用域之上定义一个可变变量，并将结果添加到该变量中。(我还修改了代码，使其与python3兼容)

returns = {}
def foo(bar):
    print('hello {0}'.format(bar))
    returns[bar] = 'foo'

from threading import Thread
t = Thread(target=foo, args=('world!',))
t.start()
t.join()
print(returns)

返回{'world!”:“foo”}

如果使用函数input作为结果字典的键，则保证每个惟一的输入都在结果中给出一个条目