我很惊讶没有人提到属性是描述符类的绑定方法，Adam Donohue和NeilenMarais在他们的帖子中得到了这个想法——getter和setter是函数，可以用于:

验证 改变数据 鸭子类型(强迫类型到另一种类型)

这提供了一种聪明的方法来隐藏实现细节和代码cruft，如正则表达式，类型转换，尝试..块、断言或计算值除外。

一般来说，在对象上执行CRUD可能相当简单，但请考虑将持久化到关系数据库的数据示例。ORM可以在属性类中定义的绑定到fget, fset, fdel的方法中隐藏特定SQL白话的实现细节，该属性类将管理可怕的if ..elif . .在OO代码中是如此丑陋的梯子——暴露了简单而优雅的自我。variable = something，为使用ORM的开发人员消除细节。

如果有人认为属性只是束缚和纪律语言(即Java)的一些沉闷的残余，那么他们就没有理解描述符的意义。

在大多数情况下，我宁愿两者都不使用。属性的问题是它们使类不那么透明。特别是，如果要从setter引发异常，这是一个问题。例如，如果您有一个帐户。电子邮件属性:

class Account(object):
    @property
    def email(self):
        return self._email

    @email.setter
    def email(self, value):
        if '@' not in value:
            raise ValueError('Invalid email address.')
        self._email = value

这样，类的用户就不会期望给属性赋值会导致异常:

a = Account()
a.email = 'badaddress'
--> ValueError: Invalid email address.

结果，异常可能得不到处理，或者在调用链中传播得太高而无法正确处理，或者导致向程序用户提供非常无用的回溯(遗憾的是，这在python和java世界中太常见了)。

我也会避免使用getter和setter:

因为提前为所有属性定义它们非常耗时， 使代码的数量不必要地变长，这使得理解和维护代码更加困难， 如果只在需要时为属性定义它们，类的接口将会改变，损害类的所有用户

而不是属性和getter /setter，我更喜欢在定义良好的地方执行复杂的逻辑，例如在验证方法中:

class Account(object):
    ...
    def validate(self):
        if '@' not in self.email:
            raise ValueError('Invalid email address.')

或类似帐户。保存方法。

请注意，我并不是想说在任何情况下属性都是有用的，只是说如果您可以使您的类足够简单和透明，以至于您不需要它们，那么您可能会更好。

@property和传统的getter和setter都有各自的优点。这取决于您的用例。

@property的优点

You don't have to change the interface while changing the implementation of data access. When your project is small, you probably want to use direct attribute access to access a class member. For example, let's say you have an object foo of type Foo, which has a member num. Then you can simply get this member with num = foo.num. As your project grows, you may feel like there needs to be some checks or debugs on the simple attribute access. Then you can do that with a @property within the class. The data access interface remains the same so that there is no need to modify client code. Cited from PEP-8: For simple public data attributes, it is best to expose just the attribute name, without complicated accessor/mutator methods. Keep in mind that Python provides an easy path to future enhancement, should you find that a simple data attribute needs to grow functional behavior. In that case, use properties to hide functional implementation behind simple data attribute access syntax. Using @property for data access in Python is regarded as Pythonic: It can strengthen your self-identification as a Python (not Java) programmer. It can help your job interview if your interviewer thinks Java-style getters and setters are anti-patterns.

传统getter和setter的优点

Traditional getters and setters allow for more complicated data access than simple attribute access. For example, when you are setting a class member, sometimes you need a flag indicating where you would like to force this operation even if something doesn't look perfect. While it is not obvious how to augment a direct member access like foo.num = num, You can easily augment your traditional setter with an additional force parameter: def Foo: def set_num(self, num, force=False): ... Traditional getters and setters make it explicit that a class member access is through a method. This means: What you get as the result may not be the same as what is exactly stored within that class. Even if the access looks like a simple attribute access, the performance can vary greatly from that. Unless your class users expect a @property hiding behind every attribute access statement, making such things explicit can help minimize your class users surprises. As mentioned by @NeilenMarais and in this post, extending traditional getters and setters in subclasses is easier than extending properties. Traditional getters and setters have been widely used for a long time in different languages. If you have people from different backgrounds in your team, they look more familiar than @property. Also, as your project grows, if you may need to migrate from Python to another language that doesn't have @property, using traditional getters and setters would make the migration smoother.

警告

Neither @property nor traditional getters and setters makes the class member private, even if you use double underscore before its name: class Foo: def __init__(self): self.__num = 0 @property def num(self): return self.__num @num.setter def num(self, num): self.__num = num def get_num(self): return self.__num def set_num(self, num): self.__num = num foo = Foo() print(foo.num) # output: 0 print(foo.get_num()) # output: 0 print(foo._Foo__num) # output: 0

以下是摘自《有效的Python: 90种具体方法来编写更好的Python》(一本令人惊叹的书)的节选。我强烈推荐)。

Things to Remember ✦ Define new class interfaces using simple public attributes and avoid defining setter and getter methods. ✦ Use @property to define special behavior when attributes are accessed on your objects, if necessary. ✦ Follow the rule of least surprise and avoid odd side effects in your @property methods. ✦ Ensure that @property methods are fast; for slow or complex work—especially involving I/O or causing side effects—use normal methods instead. One advanced but common use of @property is transitioning what was once a simple numerical attribute into an on-the-fly calculation. This is extremely helpful because it lets you migrate all existing usage of a class to have new behaviors without requiring any of the call sites to be rewritten (which is especially important if there’s calling code that you don’t control). @property also provides an important stopgap for improving interfaces over time. I especially like @property because it lets you make incremental progress toward a better data model over time. @property is a tool to help you address problems you’ll come across in real-world code. Don’t overuse it. When you find yourself repeatedly extending @property methods, it’s probably time to refactor your class instead of further paving over your code’s poor design. ✦ Use @property to give existing instance attributes new functionality. ✦ Make incremental progress toward better data models by using @property. ✦ Consider refactoring a class and all call sites when you find yourself using @property too heavily.

在复杂的项目中，我更喜欢使用带有显式setter函数的只读属性(或getter):

class MyClass(object):
...        
@property
def my_attr(self):
    ...

def set_my_attr(self, value):
    ...

在长期存在的项目中，调试和重构比编写代码本身花费更多的时间。使用@property有几个缺点。Setter，使调试更加困难:

1) python允许为现有对象创建新属性。这使得下面的打印错误很难追踪:

my_object.my_atttr = 4.

如果你的目标是一个复杂的算法，那么你将花费相当多的时间试图找出它不收敛的原因(注意上面一行中额外的“t”)

2) setter有时可能演变成一个复杂而缓慢的方法(例如击中数据库)。对于另一个开发人员来说，很难弄清楚为什么下面的函数非常慢。他可能会花很多时间分析do_something()方法，而my_object。My_attr = 4。其实是减速的原因:

def slow_function(my_object):
    my_object.my_attr = 4.
    my_object.do_something()

我很惊讶没有人提到属性是描述符类的绑定方法，Adam Donohue和NeilenMarais在他们的帖子中得到了这个想法——getter和setter是函数，可以用于:

验证 改变数据 鸭子类型(强迫类型到另一种类型)

这提供了一种聪明的方法来隐藏实现细节和代码cruft，如正则表达式，类型转换，尝试..块、断言或计算值除外。

一般来说，在对象上执行CRUD可能相当简单，但请考虑将持久化到关系数据库的数据示例。ORM可以在属性类中定义的绑定到fget, fset, fdel的方法中隐藏特定SQL白话的实现细节，该属性类将管理可怕的if ..elif . .在OO代码中是如此丑陋的梯子——暴露了简单而优雅的自我。variable = something，为使用ORM的开发人员消除细节。

如果有人认为属性只是束缚和纪律语言(即Java)的一些沉闷的残余，那么他们就没有理解描述符的意义。