In Python, nested classes are used between logically closely related classes to improve structural clarity. For example, when nesting Engine classes in Car class, you can define it through class Car: class Engine: and access it through Car.Engine(). Its advantages include namespace management, readability improvement and auxiliary design patterns, but attention should be paid to avoid unrelated nesting, multi-layer nesting and over-encapsulation to avoid increasing maintenance difficulties.
In Python, nested classes (Nested classes) are not a common usage of the language itself, but it is completely legal. You can define another class in one class, such a class is called an embedded class or a nested class. Although not commonly used, it can bring structural clarity in certain specific scenarios.

When will nested classes be used?
Nested classes are usually used in cases where they logically belong to a certain external class. For example, if you have a Car
class and it contains an Engine
class inside, nesting Engine
inside Car
can make the structure more intuitive.

class Car: class Engine: def start(self): print("Engine started") def __init__(self): self.engine = self.Engine() def start_car(self): self.engine.start()
This writing is not mandatory, but is suitable for organizing code structures, especially in large projects.
How to access nested classes
Nested classes are essentially just an attribute of an external class. You can access it through an external class:

car = Car() engine = Car.Engine()
You can also access after external class instantiation:
car = Car() engine = Car.Engine()
It should be noted that nested classes do not automatically obtain properties or methods of external classes unless you explicitly pass or bind them.
Some notes for using nested classes
- Namespace management : Nested classes help avoid global namespace pollution, especially when a class is only used in a specific context.
- Improve readability : Reasonable use can make the code logic clearer, especially for classes with dependencies.
- Inheritance and Access Control : Python does not have a strict private nested class mechanism, and all nested classes are public unless you manually add
_
means "protected". - Debugging and Maintenance : If the nesting level is too deep or the structure is complex, it may increase the cost of understanding.
Under what circumstances is it not recommended to use?
- If there is no strong logical association between the two classes, there is no need to force nesting.
- Don't nest for "encapsulation", as it will make the code difficult to maintain.
- Avoid multi-layer nesting, which can mess up the structure.
Generally speaking, nested classes are not a must-have technique, but in some design patterns or module divisions, it can be a useful tool. Use it properly can improve the organization of the code, but it may be counterproductive if used improperly.
Basically that's it.
The above is the detailed content of Nested classes in python. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

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.

The key to dealing with API authentication is to understand and use the authentication method correctly. 1. APIKey is the simplest authentication method, usually placed in the request header or URL parameters; 2. BasicAuth uses username and password for Base64 encoding transmission, which is suitable for internal systems; 3. OAuth2 needs to obtain the token first through client_id and client_secret, and then bring the BearerToken in the request header; 4. In order to deal with the token expiration, the token management class can be encapsulated and automatically refreshed the token; in short, selecting the appropriate method according to the document and safely storing the key information is the key.

A common method to traverse two lists simultaneously in Python is to use the zip() function, which will pair multiple lists in order and be the shortest; if the list length is inconsistent, you can use itertools.zip_longest() to be the longest and fill in the missing values; combined with enumerate(), you can get the index at the same time. 1.zip() is concise and practical, suitable for paired data iteration; 2.zip_longest() can fill in the default value when dealing with inconsistent lengths; 3.enumerate(zip()) can obtain indexes during traversal, meeting the needs of a variety of complex scenarios.

InPython,iteratorsareobjectsthatallowloopingthroughcollectionsbyimplementing__iter__()and__next__().1)Iteratorsworkviatheiteratorprotocol,using__iter__()toreturntheiteratorand__next__()toretrievethenextitemuntilStopIterationisraised.2)Aniterable(like

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.

TypehintsinPythonsolvetheproblemofambiguityandpotentialbugsindynamicallytypedcodebyallowingdeveloperstospecifyexpectedtypes.Theyenhancereadability,enableearlybugdetection,andimprovetoolingsupport.Typehintsareaddedusingacolon(:)forvariablesandparamete

To create modern and efficient APIs using Python, FastAPI is recommended; it is based on standard Python type prompts and can automatically generate documents, with excellent performance. After installing FastAPI and ASGI server uvicorn, you can write interface code. By defining routes, writing processing functions, and returning data, APIs can be quickly built. FastAPI supports a variety of HTTP methods and provides automatically generated SwaggerUI and ReDoc documentation systems. URL parameters can be captured through path definition, while query parameters can be implemented by setting default values ??for function parameters. The rational use of Pydantic models can help improve development efficiency and accuracy.
