Polymorphism is a core concept in Python object-oriented programming, referring to "one interface, multiple implementations", allowing for unified processing of different types of objects. 1. Polymorphism is implemented through method rewriting. Subclasses can redefine parent class methods. For example, the spoke() method of Animal class has different implementations in Dog and Cat subclasses. 2. The practical uses of polymorphism include simplifying the code structure and enhancing scalability, such as calling the draw() method uniformly in the graphical drawing program, or handling the common behavior of different characters in game development. 3. Python implementation polymorphism needs to satisfy: the parent class defines a method, and the child class overrides the method, but does not require inheritance of the same parent class. As long as the object implements the same method, this is called the "duck type". 4. Things to note include the maintenance

A class method is a method defined in Python through the @classmethod decorator. Its first parameter is the class itself (cls), which is used to access or modify the class state. It can be called through a class or instance, which affects the entire class rather than a specific instance; for example, in the Person class, the show_count() method counts the number of objects created; when defining a class method, you need to use the @classmethod decorator and name the first parameter cls, such as the change_var(new_value) method to modify class variables; the class method is different from the instance method (self parameter) and static method (no automatic parameters), and is suitable for factory methods, alternative constructors, and management of class variables. Common uses include:

Parameters are placeholders when defining a function, while arguments are specific values ??passed in when calling. 1. Position parameters need to be passed in order, and incorrect order will lead to errors in the result; 2. Keyword parameters are specified by parameter names, which can change the order and improve readability; 3. Default parameter values ??are assigned when defined to avoid duplicate code, but variable objects should be avoided as default values; 4. args and *kwargs can handle uncertain number of parameters and are suitable for general interfaces or decorators, but should be used with caution to maintain readability.

Iterators are objects that implement __iter__() and __next__() methods. The generator is a simplified version of iterators, which automatically implement these methods through the yield keyword. 1. The iterator returns an element every time he calls next() and throws a StopIteration exception when there are no more elements. 2. The generator uses function definition to generate data on demand, saving memory and supporting infinite sequences. 3. Use iterators when processing existing sets, use a generator when dynamically generating big data or lazy evaluation, such as loading line by line when reading large files. Note: Iterable objects such as lists are not iterators. They need to be recreated after the iterator reaches its end, and the generator can only traverse it once.

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@property is a decorator in Python used to masquerade methods as properties, allowing logical judgments or dynamic calculation of values ??when accessing properties. 1. It defines the getter method through the @property decorator, so that the outside calls the method like accessing attributes; 2. It can control the assignment behavior with .setter, such as the validity of the check value, if the .setter is not defined, it is read-only attribute; 3. It is suitable for scenes such as property assignment verification, dynamic generation of attribute values, and hiding internal implementation details; 4. When using it, please note that the attribute name is different from the private variable name to avoid dead loops, and is suitable for lightweight operations; 5. In the example, the Circle class restricts radius non-negative, and the Person class dynamically generates full_name attribute

Python's magicmethods (or dunder methods) are special methods used to define the behavior of objects, which start and end with a double underscore. 1. They enable objects to respond to built-in operations, such as addition, comparison, string representation, etc.; 2. Common use cases include object initialization and representation (__init__, __repr__, __str__), arithmetic operations (__add__, __sub__, __mul__) and comparison operations (__eq__, ___lt__); 3. When using it, make sure that their behavior meets expectations. For example, __repr__ should return expressions of refactorable objects, and arithmetic methods should return new instances; 4. Overuse or confusing things should be avoided.

Python operator overloading is a technique that defines operational behaviors such as , -, ==, etc. for custom classes. It allows class objects to support standard operators, making the code more concise and natural. To overload operators, you need to implement corresponding special methods, such as __add__ corresponds to , __sub__ corresponds to - and __eq__ corresponds to ==. For example, implementing __add__ in the Point class can allow two Point objects to add up and return a new object. When using it, please note: 1. __add__ should return a new object instead of modifying itself; 2. NotImplemented can be returned for non-similar objects; 3. Reverse operations should be implemented __radd__, etc.; 4. The return type should be reasonable to avoid logical confusion; 5. It is best to appear in pairs of comparison operators.