这7个 Python 装饰器都没玩过，别说 Python 精通啦

分享七个好用的装饰器，方便你撸代码。喜欢记得收藏、点赞、关注。

1、dispach

Python 天然支持多态，但使用 dispatch 可以让你的代码更加容易阅读。

安装：

pip install multipledispatch

使用：

>>> from multipledispatch import dispatch

>>> @dispatch(int, int)
... def add(x, y):
...     return x + y

>>> @dispatch(object, object)
... def add(x, y):
...     return "%s + %s" % (x, y)

>>> add(1, 2)
3

>>> add(1, 'hello')
'1 + hello'

2、click

click 可以很方便地让你实现命令行工具。

安装：

pip install click

使用：demo2.py :

import click

@click.command()
@click.option('--count', default=1, help='Number of greetings.')
@click.option('--name', prompt='Your name',
              help='The person to greet.')
def hello(count, name):
    """Simple program that greets NAME for a total of COUNT times."""
    for x in range(count):
        click.echo(f"Hello {name}!")

if __name__ == '__main__':
    hello()

运行结果：

❯ python demo2.py --count=3 --name=joih
Hello joih!
Hello joih!
Hello joih!
❯ python demo2.py --count=3
Your name: somenzz
Hello somenzz!
Hello somenzz!
Hello somenzz!

3、celery

分布式的任务队列，非 Celery 莫属。

from celery import Celery

app = Celery('tasks', broker='pyamqp://guest@localhost//')

@app.task
def add(x, y):
    return x + y

4、deprecated

这个相信大家在使用别的包时都遇到过，当要下线一个老版本的函数的时候就可以使用这个装饰器。

安装：

pip install Deprecated

使用：demo4.py

from deprecated import deprecated
@deprecated ("This function is deprecated, please do not use it")
def func1():
    pass

func1()

运行效果如下：

❯ python demo4.py
demo4.py:6: DeprecationWarning: Call to deprecated function (or staticmethod) func1. (This function is deprecated, please do not use it)
  func1()

5、deco.concurrent

安装：

pip install deco

使用 DECO 就像在 Python 程序中查找或创建两个函数一样简单。我们可以用 @concurrent 装饰需要并行运行的函数，用 @synchronized 装饰调用并行函数的函数，使用举例：

from deco import concurrent, synchronized 
@concurrent # We add this for the concurrent function
def process_url(url, data):
  #Does some work which takes a while
  return result

@synchronized # And we add this for the function which calls the concurrent function
def process_data_set(data):
  results = {}
  for url in urls:
    results[url] = process_url(url, data)
  return results

6、cachetools

缓存工具

安装：

pip install cachetools

使用：

from cachetools import cached, LRUCache, TTLCache

# speed up calculating Fibonacci numbers with dynamic programming
@cached(cache={})
def fib(n):
    return n if n < 2 else fib(n - 1) + fib(n - 2)

# cache least recently used Python Enhancement Proposals
@cached(cache=LRUCache(maxsize=32))
def get_pep(num):
    url = 'http://www.python.org/dev/peps/pep-%04d/' % num
    with urllib.request.urlopen(url) as s:
        return s.read()

# cache weather data for no longer than ten minutes
@cached(cache=TTLCache(maxsize=1024, ttl=600))
def get_weather(place):
    return owm.weather_at_place(place).get_weather()

7、retry

重试装饰器，支持各种各样的重试需求。

安装：

pip install tenacity

使用：

import random
from tenacity import retry

@retry
def do_something_unreliable():
    if random.randint(0, 10) > 1:
        raise IOError("Broken sauce, everything is hosed!!!111one")
    else:
        return "Awesome sauce!"

@retry(stop=stop_after_attempt(7))
def stop_after_7_attempts():
    print("Stopping after 7 attempts")
    raise Exception


@retry(stop=stop_after_delay(10))
def stop_after_10_s():
    print("Stopping after 10 seconds")
    raise Exception

@retry(stop=(stop_after_delay(10) | stop_after_attempt(5)))
def stop_after_10_s_or_5_retries():
    print("Stopping after 10 seconds or 5 retries")
    raise Exception

最后

本文分享了七个好用的装饰器，希望对你写代码有所帮助。