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

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.

FunctionhidinginC occurswhenaderivedclassdefinesafunctionwiththesamenameasabaseclassfunction,makingthebaseversioninaccessiblethroughthederivedclass.Thishappenswhenthebasefunctionisn’tvirtualorsignaturesdon’tmatchforoverriding,andnousingdeclarationis

@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

There are mainly the following methods to obtain stack traces in C: 1. Use backtrace and backtrace_symbols functions on Linux platform. By including obtaining the call stack and printing symbol information, the -rdynamic parameter needs to be added when compiling; 2. Use CaptureStackBackTrace function on Windows platform, and you need to link DbgHelp.lib and rely on PDB file to parse the function name; 3. Use third-party libraries such as GoogleBreakpad or Boost.Stacktrace to cross-platform and simplify stack capture operations; 4. In exception handling, combine the above methods to automatically output stack information in catch blocks

The reason why Python's private variables cannot be accessed directly is that the name is rewritten to the form of _ClassName__variable to avoid unexpected access; 1. It can be forced to access through _ClassName__variable, but it is not recommended; 2. It is also recommended to use getter method or @property to provide access interface; 3. You can use __dict__ to view the internal variables of the object during debugging.

Assert is an assertion tool used in Python for debugging, and throws an AssertionError when the condition is not met. Its syntax is assert condition plus optional error information, which is suitable for internal logic verification such as parameter checking, status confirmation, etc., but cannot be used for security or user input checking, and should be used in conjunction with clear prompt information. It is only available for auxiliary debugging in the development stage rather than substituting exception handling.

The observer pattern is a behavioral design pattern used to establish a one-to-many dependency between objects. It maintains a set of Observers through Subject and automatically notifies all observers when their state changes. The specific implementation steps are as follows: 1. Define the Observer interface, including the update() method; 2. Implement the Subject class, use the container to manage the observer list, and provide attach, detach and notify methods; 3. Create the ConcreteObserver class to implement specific update logic. Notes include: using smart pointers to avoid memory leaks; detaching destroyed observers in a timely manner; considering thread-safe operations; and controlling notification order based on demand.