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.

Airdrops in the cryptocurrency field are a marketing promotion method for the project to distribute a certain number of tokens for free to community members or potential users. In this way, the project party hopes to increase the visibility of the tokens and attract more users to participate in the project, thereby expanding the size of the community and increasing the liquidity of the tokens. For users, airdrops provide opportunities to obtain project tokens without initial investment, and are one of the ways to get in touch with and understand new projects in the early stage.

@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 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.

DataclassesinPythonreduceboilerplatecodeforclassesmeanttostoredata.1.Theyautomaticallygeneratemethodslikeinit__,__repr__,and__eq__.2.Theyareidealforstructureddatamodels,DTOs,andconfigurationobjects.3.Theysupportcustomizationthroughdefaultvalues,field

To manage Linux user groups, you need to master the operation of viewing, creating, deleting, modifying, and user attribute adjustment. To view user group information, you can use cat/etc/group or getentgroup, use groups [username] or id [username] to view the group to which the user belongs; use groupadd to create a group, and use groupdel to specify the GID; use groupdel to delete empty groups; use usermod-aG to add users to the group, and use usermod-g to modify the main group; use usermod-g to remove users from the group by editing /etc/group or using the vigr command; use groupmod-n (change name) or groupmod-g (change GID) to modify group properties, and remember to update the permissions of relevant files.

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.