它是什么?
>>> type(NotImplemented) <type 'NotImplementedType'>
NotImplemented 是Python在内置命名空间中的六个常数之一。其他有False、True、None、Ellipsis 和 __debug__。和 Ellipsis很像,NotImplemented 能被重新赋值(覆盖)。对它赋值,甚至改变属性名称, 不会产生 SyntaxError。所以它不是一个真正的“真”常数。当然,我们应该永远不改变它。 但是为了完整性:
>>> None = 'hello' ... SyntaxError: can't assign to keyword >>> NotImplemented NotImplemented >>> NotImplemented = 'do not' >>> NotImplemented 'do not'
它有什么用?什么时候用?
NotImplemented 是个特殊值,它能被二元特殊方法返回(比如__eq__() 、 __lt__() 、 __add__() 、 __rsub__() 等),表明某个类型没有像其他类型那样实现这些操作。同样,它或许会被原地处理(in place)的二元特殊方法返回(比如__imul__()、__iand__()等)。还有,它的实际值为True:
>>> bool(NotImplemented) True
你也许会问自己,“但我认为当这个操作没有实现时,我应该产生个NotImpementedError”。我们会看些例子,关于为什么当实现二元特殊方法时不是这么回事儿。
让我们看看NotImplemented常数的用法,通过__eq__()对于两个非常基本(且没用)的类 A 和 B 的编码。[对于这个简单的例子,为了避免干扰,不会实现__ne__() ,但是总的说来,每次实现__eq__() 时, __ne__()也应该被实现,除非,有个足够充分的理由去不实现它。]
# example.py class A(object): def __init__(self, value): self.value = value def __eq__(self, other): if isinstance(other, A): print('Comparing an A with an A') return other.value == self.value if isinstance(other, B): print('Comparing an A with a B') return other.value == self.value print('Could not compare A with the other class') return NotImplemented class B(object): def __init__(self, value): self.value = value def __eq__(self, other): if isinstance(other, B): print('Comparing a B with another B') return other.value == self.value print('Could not compare B with the other class') return NotImplemented
现在,在解释器中:
>>> from example import A, B >>> a1 = A(1) >>> b1 = B(1)
我们现在可以实验下对于 __eq__() 不同的调用,看看发生了什么。作为提醒,在Python中,a == b会调用a.__eq__(b):
>>> a1 == a1 Comparing an A with an A True
正如所望,a1等于a1(自己),使用类A中的__eq__()来进行这个比较的。比较b1和它自己也会产生类似结果:
>>> b1 == b1 Comparing a B with another B True
现在,那要是我们比较a1和b1呢?由于在A的__eq__()会检查other是不是B的一个实例,我们想要a1.__eq__(b1)去处理这个比较并返回True:
>>> a1 == b1 Comparing an A with a B True
就是这样。现在,如果我们比较b1和a1(即调用b1.__eq__(a1)),我们会想要返回NotImplemented。这是因为B的__eq__()只和其他B的实例进行比较。来看看发生了什么:
>>> b1 == a1 Could not compare B against the other class Comparing an A with a B True
聪明!b1.__eq__(a1)方法返回NotImplemented,这样会导致调用A中的__eq__()方法。而且由于在A中的__eq__()定义了A和B之间的比较,所以就得到了正确的结果(True)。
这就是返回了NotImplemented的所做的。NotImplemented告诉运行时,应该让其他对象来完成某个操作。在表达b1 == a1中,b1.__eq__(a1)返回了NotImplemented,这说明Python试着用a1.__eq__(b1)。由于a1足够可以返回True,因此这个表达可以成功。如果A中的__eq__()也返回NotImplemented,那么运行时会退化到使用内置的比较行为,即比较对象的标识符(在CPython中,是对象在内存中的地址)。
注意:如果在调用b1.__eq__(a1)时抛出NotImpementedError,而不进行处理,就会中断代码的执行。而NotImplemented无法抛出,仅仅是用来进一步测试是否有其他方法可供调用。

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


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