如果您的程序还没有事件循环，请使用sched模块，它实现了一个通用的事件调度器。

import sched, time

def do_something(scheduler): 
    # schedule the next call first
    scheduler.enter(60, 1, do_something, (scheduler,))
    print("Doing stuff...")
    # then do your stuff

my_scheduler = sched.scheduler(time.time, time.sleep)
my_scheduler.enter(60, 1, do_something, (my_scheduler,))
my_scheduler.run()

如果您已经在使用事件循环库，如asyncio、trio、tkinter、PyQt5、gobject、kivy等，则只需使用现有事件循环库的方法来调度任务。

它和cron之间的主要区别是异常会永久地杀死守护进程。您可能希望使用异常捕获器和记录器进行包装。

你可能会考虑Twisted，它是一个实现了Reactor Pattern的Python网络库。

from twisted.internet import task, reactor

timeout = 60.0 # Sixty seconds

def doWork():
    #do work here
    pass

l = task.LoopingCall(doWork)
l.start(timeout) # call every sixty seconds

reactor.run()

虽然“While True: sleep(60)”可能会工作，Twisted可能已经实现了许多你最终需要的功能(如bobince指出的守护进程化、日志记录或异常处理)，并且可能是一个更健壮的解决方案

我认为更简单的方法是:

import time

def executeSomething():
    #code here
    time.sleep(60)

while True:
    executeSomething()

这样，你的代码被执行，然后等待60秒，然后再次执行，等待，执行，等等…… 没有必要把事情复杂化:D

我以前也遇到过类似的问题。也许http://cronus.readthedocs.org会有帮助?

对于v0.2，下面的代码片段可以工作

import cronus.beat as beat

beat.set_rate(2) # run twice per second
while beat.true():
    # do some time consuming work here
    beat.sleep() # total loop duration would be 0.5 sec

像这样将你的时间循环锁定到系统时钟上:

import time
starttime = time.time()
while True:
    print("tick")
    time.sleep(60.0 - ((time.time() - starttime) % 60.0))

如果你想要一种非阻塞的方式来周期性地执行你的函数，而不是阻塞无限循环，我会使用线程计时器。这样，您的代码可以继续运行并执行其他任务，并且仍然每n秒调用一次您的函数。我经常使用这种技术打印长时间、CPU/磁盘/网络密集型任务的进度信息。

下面是我在类似问题中发布的代码，带有start()和stop()控件:

from threading import Timer

class RepeatedTimer(object):
    def __init__(self, interval, function, *args, **kwargs):
        self._timer     = None
        self.interval   = interval
        self.function   = function
        self.args       = args
        self.kwargs     = kwargs
        self.is_running = False
        self.start()

    def _run(self):
        self.is_running = False
        self.start()
        self.function(*self.args, **self.kwargs)

    def start(self):
        if not self.is_running:
            self._timer = Timer(self.interval, self._run)
            self._timer.start()
            self.is_running = True

    def stop(self):
        self._timer.cancel()
        self.is_running = False

用法:

from time import sleep

def hello(name):
    print "Hello %s!" % name

print "starting..."
rt = RepeatedTimer(1, hello, "World") # it auto-starts, no need of rt.start()
try:
    sleep(5) # your long-running job goes here...
finally:
    rt.stop() # better in a try/finally block to make sure the program ends!

特点:

只有标准库，没有外部依赖 即使计时器已经启动/停止，也可以安全地多次调用Start()和stop() 要调用的函数可以有位置参数和命名参数 您可以随时更改间隔，它将在下次运行后生效。args、kwargs甚至function也一样!

下面是MestreLion对代码的更新，它可以避免随着时间的推移而漂移。

这里的RepeatedTimer类按照OP的请求每隔“间隔”秒调用给定函数;调度并不取决于函数执行的时间。我喜欢这个解决方案，因为它没有外部库依赖关系;这是纯python。

import threading 
import time

class RepeatedTimer(object):
  def __init__(self, interval, function, *args, **kwargs):
    self._timer = None
    self.interval = interval
    self.function = function
    self.args = args
    self.kwargs = kwargs
    self.is_running = False
    self.next_call = time.time()
    self.start()

  def _run(self):
    self.is_running = False
    self.start()
    self.function(*self.args, **self.kwargs)

  def start(self):
    if not self.is_running:
      self.next_call += self.interval
      self._timer = threading.Timer(self.next_call - time.time(), self._run)
      self._timer.start()
      self.is_running = True

  def stop(self):
    self._timer.cancel()
    self.is_running = False

示例用法(摘自MestreLion的回答):

from time import sleep

def hello(name):
    print "Hello %s!" % name

print "starting..."
rt = RepeatedTimer(1, hello, "World") # it auto-starts, no need of rt.start()
try:
    sleep(5) # your long-running job goes here...
finally:
    rt.stop() # better in a try/finally block to make sure the program ends!

我用这个方法使每小时产生60个事件，其中大多数事件在整分钟后的相同秒数内发生:

import math
import time
import random

TICK = 60 # one minute tick size
TICK_TIMING = 59 # execute on 59th second of the tick
TICK_MINIMUM = 30 # minimum catch up tick size when lagging

def set_timing():

    now = time.time()
    elapsed = now - info['begin']
    minutes = math.floor(elapsed/TICK)
    tick_elapsed = now - info['completion_time']
    if (info['tick']+1) > minutes:
        wait = max(0,(TICK_TIMING-(time.time() % TICK)))
        print ('standard wait: %.2f' % wait)
        time.sleep(wait)
    elif tick_elapsed < TICK_MINIMUM:
        wait = TICK_MINIMUM-tick_elapsed
        print ('minimum wait: %.2f' % wait)
        time.sleep(wait)
    else:
        print ('skip set_timing(); no wait')
    drift = ((time.time() - info['begin']) - info['tick']*TICK -
        TICK_TIMING + info['begin']%TICK)
    print ('drift: %.6f' % drift)

info['tick'] = 0
info['begin'] = time.time()
info['completion_time'] = info['begin'] - TICK

while 1:

    set_timing()

    print('hello world')

    #random real world event
    time.sleep(random.random()*TICK_MINIMUM)

    info['tick'] += 1
    info['completion_time'] = time.time()

根据实际情况，你可能会得到长度的刻度:

60,60,62,58,60,60,120,30,30,60,60,60,60,60...etc.

但在60分钟结束时，你会有60个滴答;而且它们中的大多数都将出现在您喜欢的正确偏移时间。

在我的系统中，我得到了< 1/20秒的典型漂移，直到需要纠正。

该方法的优点是具有较好的时钟漂移分辨率;这可能会导致问题，如果你做的事情，比如每tick追加一个项目，你希望每小时追加60个项目。未能考虑漂移可能导致次要指标，如移动平均线，将数据考虑得过于深入过去，从而导致错误的输出。

一个可能的答案是:

import time
t=time.time()

while True:
    if time.time()-t>10:
        #run your task here
        t=time.time()

例如:显示当前本地时间

import datetime
import glib
import logger

def get_local_time():
    current_time = datetime.datetime.now().strftime("%H:%M")
    logger.info("get_local_time(): %s",current_time)
    return str(current_time)

def display_local_time():
    logger.info("Current time is: %s", get_local_time())
    return True

# call every minute
glib.timeout_add(60*1000, display_local_time)

我使用Tkinter after()方法，它不会“窃取游戏”(就像之前提出的sched模块)，即它允许其他东西并行运行:

import Tkinter

def do_something1():
  global n1
  n1 += 1
  if n1 == 6: # (Optional condition)
    print "* do_something1() is done *"; return
  # Do your stuff here
  # ...
  print "do_something1() "+str(n1)
  tk.after(1000, do_something1)

def do_something2(): 
  global n2
  n2 += 1
  if n2 == 6: # (Optional condition)
    print "* do_something2() is done *"; return
  # Do your stuff here
  # ...
  print "do_something2() "+str(n2)
  tk.after(500, do_something2)

tk = Tkinter.Tk(); 
n1 = 0; n2 = 0
do_something1()
do_something2()
tk.mainloop()

Do_something1()和do_something2()可以以任意的间隔速度并行运行。在这里，第2个将以两倍的速度执行。还要注意，我使用了一个简单的计数器作为终止任一函数的条件。你可以使用任何你喜欢的条件，或者不使用，如果你想让一个函数运行到程序终止(例如一个时钟)。

import time, traceback

def every(delay, task):
  next_time = time.time() + delay
  while True:
    time.sleep(max(0, next_time - time.time()))
    try:
      task()
    except Exception:
      traceback.print_exc()
      # in production code you might want to have this instead of course:
      # logger.exception("Problem while executing repetitive task.")
    # skip tasks if we are behind schedule:
    next_time += (time.time() - next_time) // delay * delay + delay

def foo():
  print("foo", time.time())

every(5, foo)

如果你想在不阻塞剩余代码的情况下这样做，你可以使用这个让它在自己的线程中运行:

import threading
threading.Thread(target=lambda: every(5, foo)).start()

该解决方案结合了其他解决方案中很少结合的几个特性:

Exception handling: As far as possible on this level, exceptions are handled properly, i. e. get logged for debugging purposes without aborting our program. No chaining: The common chain-like implementation (for scheduling the next event) you find in many answers is brittle in the aspect that if anything goes wrong within the scheduling mechanism (threading.Timer or whatever), this will terminate the chain. No further executions will happen then, even if the reason of the problem is already fixed. A simple loop and waiting with a simple sleep() is much more robust in comparison. No drift: My solution keeps an exact track of the times it is supposed to run at. There is no drift depending on the execution time (as in many other solutions). Skipping: My solution will skip tasks if one execution took too much time (e. g. do X every five seconds, but X took 6 seconds). This is the standard cron behavior (and for a good reason). Many other solutions then simply execute the task several times in a row without any delay. For most cases (e. g. cleanup tasks) this is not wished. If it is wished, simply use next_time += delay instead.

    ''' tracking number of times it prints'''
import threading

global timeInterval
count=0
def printit():
  threading.Timer(timeInterval, printit).start()
  print( "Hello, World!")
  global count
  count=count+1
  print(count)
printit

if __name__ == "__main__":
    timeInterval= int(input('Enter Time in Seconds:'))
    printit()

以下是MestreLion代码的改编版本。 除了原来的函数，这段代码:

1)添加用于在特定时间触发计时器的first_interval(调用者需要计算first_interval并传递进来)

2)在原代码中解决一个竞态条件。在原始代码中，如果控制线程未能取消正在运行的计时器(“停止计时器，并取消计时器动作的执行。这只会在计时器仍处于等待阶段时起作用。”引用自https://docs.python.org/2/library/threading.html)，计时器将无休止地运行。

class RepeatedTimer(object):
def __init__(self, first_interval, interval, func, *args, **kwargs):
    self.timer      = None
    self.first_interval = first_interval
    self.interval   = interval
    self.func   = func
    self.args       = args
    self.kwargs     = kwargs
    self.running = False
    self.is_started = False

def first_start(self):
    try:
        # no race-condition here because only control thread will call this method
        # if already started will not start again
        if not self.is_started:
            self.is_started = True
            self.timer = Timer(self.first_interval, self.run)
            self.running = True
            self.timer.start()
    except Exception as e:
        log_print(syslog.LOG_ERR, "timer first_start failed %s %s"%(e.message, traceback.format_exc()))
        raise

def run(self):
    # if not stopped start again
    if self.running:
        self.timer = Timer(self.interval, self.run)
        self.timer.start()
    self.func(*self.args, **self.kwargs)

def stop(self):
    # cancel current timer in case failed it's still OK
    # if already stopped doesn't matter to stop again
    if self.timer:
        self.timer.cancel()
    self.running = False

我最终使用了时间表模块。API很好。

import schedule
import time

def job():
    print("I'm working...")

schedule.every(10).minutes.do(job)
schedule.every().hour.do(job)
schedule.every().day.at("10:30").do(job)
schedule.every(5).to(10).minutes.do(job)
schedule.every().monday.do(job)
schedule.every().wednesday.at("13:15").do(job)
schedule.every().minute.at(":17").do(job)

while True:
    schedule.run_pending()
    time.sleep(1)

这里是另一个不使用任何额外库的解决方案。

def delay_until(condition_fn, interval_in_sec, timeout_in_sec):
    """Delay using a boolean callable function.

    `condition_fn` is invoked every `interval_in_sec` until `timeout_in_sec`.
    It can break early if condition is met.

    Args:
        condition_fn     - a callable boolean function
        interval_in_sec  - wait time between calling `condition_fn`
        timeout_in_sec   - maximum time to run

    Returns: None
    """
    start = last_call = time.time()
    while time.time() - start < timeout_in_sec:
        if (time.time() - last_call) > interval_in_sec:
            if condition_fn() is True:
                break
            last_call = time.time()

如果漂移不是一个问题

import threading, time

def print_every_n_seconds(n=2):
    while True:
        print(time.ctime())
        time.sleep(n)
    
thread = threading.Thread(target=print_every_n_seconds, daemon=True)
thread.start()

异步输出。

#Tue Oct 16 17:29:40 2018
#Tue Oct 16 17:29:42 2018
#Tue Oct 16 17:29:44 2018

如果正在运行的任务需要相当多的时间，那么间隔就变成2秒+任务时间，所以如果您需要精确的调度，那么这并不适合您。

注意daemon=True标志意味着这个线程不会阻止应用程序关闭。例如，在运行测试等待此头停止后，pytest将无限期挂起的问题。

另一种灵活性解决方案是Apscheduler。

pip install apscheduler

from apscheduler.schedulers.background import BlockingScheduler
def print_t():
  pass

sched = BlockingScheduler()
sched.add_job(print_t, 'interval', seconds =60) #will do the print_t work for every 60 seconds

sched.start()

另外，apscheduler提供了如下所示的许多调度程序。

BlockingScheduler: use when the scheduler is the only thing running in your process BackgroundScheduler: use when you’re not using any of the frameworks below, and want the scheduler to run in the background inside your application AsyncIOScheduler: use if your application uses the asyncio module GeventScheduler: use if your application uses gevent TornadoScheduler: use if you’re building a Tornado application TwistedScheduler: use if you’re building a Twisted application QtScheduler: use if you’re building a Qt application

简单地使用

import time

while True:
    print("this will run after every 30 sec")
    #Your code here
    time.sleep(30)

我认为这取决于你想做什么，你的问题没有详细说明。

对我来说，我想在一个已经多线程的进程中做一个昂贵的操作。所以我让leader流程检查时间，只有她做昂贵的操作(检查点深度学习模型)。为了做到这一点，我增加了计数器，以确保5秒、10秒、15秒过去，每5秒保存一次(或使用math.floor的模块化算术):

def print_every_5_seconds_have_passed_exit_eventually():
    """
    https://stackoverflow.com/questions/3393612/run-certain-code-every-n-seconds
    https://stackoverflow.com/questions/474528/what-is-the-best-way-to-repeatedly-execute-a-function-every-x-seconds
    :return:
    """
    opts = argparse.Namespace(start=time.time())
    next_time_to_print = 0
    while True:
        current_time_passed = time.time() - opts.start
        if current_time_passed >= next_time_to_print:
            next_time_to_print += 5
            print(f'worked and {current_time_passed=}')
            print(f'{current_time_passed % 5=}')
            print(f'{math.floor(current_time_passed % 5) == 0}')

starting __main__ at __init__
worked and current_time_passed=0.0001709461212158203
current_time_passed % 5=0.0001709461212158203
True
worked and current_time_passed=5.0
current_time_passed % 5=0.0
True
worked and current_time_passed=10.0
current_time_passed % 5=0.0
True
worked and current_time_passed=15.0
current_time_passed % 5=0.0
True

对我来说，检查if语句是我所需要的。在我已经复杂的多处理器多gpu代码中拥有线程，调度器并不是我想要添加的复杂性，如果我可以避免它，似乎我可以。检查worker id很容易确保只有一个进程在做这件事。

注意，我使用True print语句来确保模块化的算术技巧有效，因为检查确切的时间显然是行不通的!但令我惊喜的是，地板竟然起了作用。

计时计数可以做到高精度(即< 1毫秒)，因为它与系统时钟同步。它不会随着时间的推移而漂移，也不受代码执行时间长度的影响(当然，前提是它小于间隔时间)。

一个简单的阻塞的例子:

from timed_count import timed_count

for count in timed_count(60):
    # Execute code here exactly every 60 seconds
    ...

你可以通过在线程中运行它来让它变得不阻塞:

from threading import Thread
from timed_count import timed_count

def periodic():
    for count in timed_count(60):
        # Execute code here exactly every 60 seconds
        ...

thread = Thread(target=periodic)
thread.start()