什么是python的装饰器?
网络上的定义:
装饰器就是一函数,用来包装函数的函数,用来修饰原函数,将其重新赋值给原来的标识符,并永久的丧失原函数的引用。
最能说明装饰器的例子如下:
#-*- coding: UTF-8 -*-
import time
def foo():
print 'in foo()'
# 定义一个计时器,传入一个,并返回另一个附加了计时功能的方法
def timeit(func):
# 定义一个内嵌的包装函数,给传入的函数加上计时功能的包装
def wrapper():
start = time.clock()
func()
end =time.clock()
print 'used:', end - start
# 将包装后的函数返回
return wrapper
foo = timeit(foo)
foo()
python中提供了一个@符号的语法糖,用来简化上面的代码,他们的作用一样
import time
def timeit(func):
def wrapper():
start = time.clock()
func()
end =time.clock()
print 'used:', end - start
return wrapper
@timeit
def foo():
print 'in foo()'
foo()
这2段的代码是一样的,等价的。
内置的3个装饰器,他们分别是staticmethod,classmethod,property,他们的作用是分别把类中定义的方法变成静态方法,类方法和属性,如下:
class Rabbit(object):
def __init__(self, name):
self._name = name
@staticmethod
def newRabbit(name):
return Rabbit(name)
@classmethod
def newRabbit2(cls):
return Rabbit('')
@property
def name(self):
return self._name
装饰器的嵌套:
就一个规律:嵌套的顺序和代码的顺序是相反的。
也是来看一个例子:
#!/usr/bin/python
# -*- coding: utf-8 -*-
def makebold(fn):
def wrapped():
return "" + fn() + ""
return wrapped
def makeitalic(fn):
def wrapped():
return "" + fn() + ""
return wrapped
@makebold
@makeitalic
def hello():
return "hello world"
print hello()
返回的结果是:
hello world
为什么是这个结果呢?
1.首先hello函数经过makeitalic 函数的装饰,变成了这个结果hello world
2.然后再经过makebold函数的装饰,变成了hello world,这个理解起来很简单。

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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