它不在语言核心中，但Twisted是一个非常成熟的库，可以做你想要的事情。它引入Deferred对象，您可以将回调或错误处理程序(“errbacks”)附加到该对象。Deferred基本上是一个“承诺”，即一个函数最终会有一个结果。

喜欢的东西:

import threading

thr = threading.Thread(target=foo, args=(), kwargs={})
thr.start() # Will run "foo"
....
thr.is_alive() # Will return whether foo is running currently
....
thr.join() # Will wait till "foo" is done

有关详细信息，请参阅https://docs.python.org/library/threading.html上的文档。

有什么理由不使用线程吗?您可以使用线程类。 使用isAlive()函数代替finished()函数。result()函数可以join()线程并检索结果。并且，如果可以的话，重写run()和__init__函数来调用构造函数中指定的函数，并将值保存到类实例的某个地方。

您可以使用Python 2.6中添加的多处理模块。您可以使用进程池，然后通过以下方式异步获取结果:

apply_async(func[, args[, kwds[, callback]]])

例如:

from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    pool = Pool(processes=1)              # Start a worker processes.
    result = pool.apply_async(f, [10], callback) # Evaluate "f(10)" asynchronously calling callback when finished.

这只是一种选择。这个模块提供了很多工具来实现你想要的。此外，它将很容易从这做一个装饰。

我的解决方案是:

import threading

class TimeoutError(RuntimeError):
    pass

class AsyncCall(object):
    def __init__(self, fnc, callback = None):
        self.Callable = fnc
        self.Callback = callback

    def __call__(self, *args, **kwargs):
        self.Thread = threading.Thread(target = self.run, name = self.Callable.__name__, args = args, kwargs = kwargs)
        self.Thread.start()
        return self

    def wait(self, timeout = None):
        self.Thread.join(timeout)
        if self.Thread.isAlive():
            raise TimeoutError()
        else:
            return self.Result

    def run(self, *args, **kwargs):
        self.Result = self.Callable(*args, **kwargs)
        if self.Callback:
            self.Callback(self.Result)

class AsyncMethod(object):
    def __init__(self, fnc, callback=None):
        self.Callable = fnc
        self.Callback = callback

    def __call__(self, *args, **kwargs):
        return AsyncCall(self.Callable, self.Callback)(*args, **kwargs)

def Async(fnc = None, callback = None):
    if fnc == None:
        def AddAsyncCallback(fnc):
            return AsyncMethod(fnc, callback)
        return AddAsyncCallback
    else:
        return AsyncMethod(fnc, callback)

并完全按要求工作:

@Async
def fnc():
    pass

您可以实现一个装饰器来使您的函数异步，尽管这有点棘手。多处理模块充满了小怪癖和看似任意的限制——尽管如此，更有理由将它封装在友好的界面后面。

from inspect import getmodule
from multiprocessing import Pool


def async(decorated):
    r'''Wraps a top-level function around an asynchronous dispatcher.

        when the decorated function is called, a task is submitted to a
        process pool, and a future object is returned, providing access to an
        eventual return value.

        The future object has a blocking get() method to access the task
        result: it will return immediately if the job is already done, or block
        until it completes.

        This decorator won't work on methods, due to limitations in Python's
        pickling machinery (in principle methods could be made pickleable, but
        good luck on that).
    '''
    # Keeps the original function visible from the module global namespace,
    # under a name consistent to its __name__ attribute. This is necessary for
    # the multiprocessing pickling machinery to work properly.
    module = getmodule(decorated)
    decorated.__name__ += '_original'
    setattr(module, decorated.__name__, decorated)

    def send(*args, **opts):
        return async.pool.apply_async(decorated, args, opts)

    return send

下面的代码说明了装饰器的用法:

@async
def printsum(uid, values):
    summed = 0
    for value in values:
        summed += value

    print("Worker %i: sum value is %i" % (uid, summed))

    return (uid, summed)


if __name__ == '__main__':
    from random import sample

    # The process pool must be created inside __main__.
    async.pool = Pool(4)

    p = range(0, 1000)
    results = []
    for i in range(4):
        result = printsum(i, sample(p, 100))
        results.append(result)

    for result in results:
        print("Worker %i: sum value is %i" % result.get())

在实际的情况下，我将详细介绍装饰器，提供一些方法来关闭它以进行调试(同时保持未来的接口在适当的位置)，或者可能是处理异常的工具;但我认为这充分说明了原理。

Just

import threading, time

def f():
    print "f started"
    time.sleep(3)
    print "f finished"

threading.Thread(target=f).start()

你可以使用eventlet。它允许您编写看似同步的代码，但却可以在网络上异步操作。

下面是一个超级小爬虫的例子:

urls = ["http://www.google.com/intl/en_ALL/images/logo.gif",
     "https://wiki.secondlife.com/w/images/secondlife.jpg",
     "http://us.i1.yimg.com/us.yimg.com/i/ww/beta/y3.gif"]

import eventlet
from eventlet.green import urllib2

def fetch(url):

  return urllib2.urlopen(url).read()

pool = eventlet.GreenPool()

for body in pool.imap(fetch, urls):
  print "got body", len(body)

这对我来说很有用，你可以调用这个函数，它会把自己分派到一个新的线程上。

from thread import start_new_thread

def dowork(asynchronous=True):
    if asynchronous:
        args = (False)
        start_new_thread(dowork,args) #Call itself on a new thread.
    else:
        while True:
            #do something...
            time.sleep(60) #sleep for a minute
    return

从Python 3.5开始，可以对异步函数使用增强的生成器。

import asyncio
import datetime

增强的生成器语法:

@asyncio.coroutine
def display_date(loop):
    end_time = loop.time() + 5.0
    while True:
        print(datetime.datetime.now())
        if (loop.time() + 1.0) >= end_time:
            break
        yield from asyncio.sleep(1)


loop = asyncio.get_event_loop()
# Blocking call which returns when the display_date() coroutine is done
loop.run_until_complete(display_date(loop))
loop.close()

新的async/await语法:

async def display_date(loop):
    end_time = loop.time() + 5.0
    while True:
        print(datetime.datetime.now())
        if (loop.time() + 1.0) >= end_time:
            break
        await asyncio.sleep(1)


loop = asyncio.get_event_loop()
# Blocking call which returns when the display_date() coroutine is done
loop.run_until_complete(display_date(loop))
loop.close()

你可以使用并发。期货(在Python 3.2中添加)。

import time
from concurrent.futures import ThreadPoolExecutor


def long_computation(duration):
    for x in range(0, duration):
        print(x)
        time.sleep(1)
    return duration * 2


print('Use polling')
with ThreadPoolExecutor(max_workers=1) as executor:
    future = executor.submit(long_computation, 5)
    while not future.done():
        print('waiting...')
        time.sleep(0.5)

    print(future.result())

print('Use callback')
executor = ThreadPoolExecutor(max_workers=1)
future = executor.submit(long_computation, 5)
future.add_done_callback(lambda f: print(f.result()))

print('waiting for callback')

executor.shutdown(False)  # non-blocking

print('shutdown invoked')

你可以使用过程。如果你想永远运行它，在你的函数中使用while(比如networking):

from multiprocessing import Process
def foo():
    while 1:
        # Do something

p = Process(target = foo)
p.start()

如果你只想运行一次，可以这样做:

from multiprocessing import Process
def foo():
    # Do something

p = Process(target = foo)
p.start()
p.join()

2021年的原生Python异步调用方式，Python 3.9也适用于Jupyter / Ipython内核

Camabeh的答案是从Python 3.3开始的。

异步def display_date(循环): End_time = loop.time() + 5.0 而真正的: print (datetime.datetime.now ()) If (loop.time() + 1.0) >= end_time: 打破 等待asyncio.sleep (1) Loop = asyncio.get_event_loop() #阻塞调用，当display_date()协程完成时返回 loop.run_until_complete (display_date(循环) loop.close ()

这将在Jupyter笔记本/ Jupyter实验室工作，但抛出一个错误:

RuntimeError: This event loop is already running

由于Ipython使用事件循环，我们需要一些嵌套异步循环，这在Python中还没有实现。幸运的是，有nest_asyncio来处理这个问题。你所需要做的就是:

!pip install nest_asyncio # use ! within Jupyter Notebook, else pip install in shell
import nest_asyncio
nest_asyncio.apply()

(基于此线程)

只有在调用loop.close()时，它才会抛出另一个错误，因为它可能指向Ipython的主循环。

RuntimeError: Cannot close a running event loop

一旦有人回答了这个github问题，我就会更新这个答案。

Python 3.7及以后版本中更新的asyncio运行方法是使用asyncio.run()，而不是创建循环并调用loop.run_until_complete()并关闭它:

import asyncio
import datetime

async def display_date(delay):
    loop = asyncio.get_running_loop()
    end_time = loop.time() + delay
    while True:
        print("Blocking...", datetime.datetime.now())
        await asyncio.sleep(1)
        if loop.time() > end_time:
            print("Done.")
            break


asyncio.run(display_date(5))