


Differences Between Static Method and Class Method in Python
Jul 07, 2025 am 02:35 AMThe main differences between @staticmethod and @classmethod are parameter passing, usage scenarios and inheritance performance. 1. When passing parameters, the static method does not automatically pass any implicit parameters, but the class method automatically receives the class object as the first parameter (cls). 2. In usage scenarios, static methods are suitable for tool functions that are independent of classes and instances. Class methods are suitable for factory methods or situations where class state needs to be accessed/modified. 3. In inheritance, the static method is still bound to the class at the time of definition when invoking it, and the class method will be automatically bound to the subclass that calls it. When selecting, you should decide which decorator to use based on whether the class or instance is needed and whether it is used to create the instance.
In Python, @staticmethod
and @classmethod
are two commonly used decorators. They both can define methods in classes, but there are obvious differences in purpose and behavior. Simply put:

- Static method is more like an ordinary function, just used to organize code in a class, and it does not receive automatically passed
self
orcls
parameters. - Class Method will automatically receive the class itself as the first parameter (usually named
cls
), which is suitable for factory methods or scenarios where class state needs to be accessed/modified.
Let’s take a look at the differences from several common usage perspectives.

1. Different parameters are passed
This is the most core difference between the two:
- Static method : No implicit parameters are automatically passed in.
- Class method : The first parameter is the class object (usually
cls
).
For example:

class MyClass: @staticmethod def static_method(x): print(f"Static method called with {x}") @classmethod def class_method(cls, x): print(f"Class method called with {x}, class is {cls}")
When called:
MyClass.static_method(5) # Output: Static method called with 5 MyClass.class_method(5) # Output: Class method called with 5, class is <class 'MyClass'>
It can be seen that the class method knows which class it belongs to, while the static method does not know this at all.
2. Use scenario comparison
Static method application situation:
- Methods do not need to access instance properties or class properties.
- It is just used to encapsulate a certain function and facilitate classification into a class.
- Such as tool functions, verification logic, format conversion, etc.
For example:
class Validator: @staticmethod def is_valid_email(email): return "@" in email and "." in email
Class method application situation:
- An instance of the class needs to be created (factory method).
- The status of the class needs to be accessed or modified.
- Want to keep a reference to the current class while inheriting.
for example:
class Person: instances = 0 @classmethod def create(cls): cls.instances = 1 return cls()
In this way, no matter how the subclass changes, the create()
method will return the correct class instance.
3. Performance differences in inheritance
When a child class inherits the parent class:
- Static method : It will not be bound to a subclass automatically, and the class defined is still used.
- Class method : will be automatically bound to the subclass that calls it.
Example:
class Base: @staticmethod def static(): print("Base static") @classmethod def klass(cls): print(f"{cls.__name__} klass") class Sub(Base): pass Sub.static() # Output: Base static Sub.klass() # Output: Sub klass
As you can see, the class method changes behavior according to the actual caller, while the static method is fixed.
Basically these differences. Although it looks similar, choosing the wrong decorator may cause unexpected problems when designing a class structure or doing framework development.
Let's briefly summarize:
- If the method does not need to access the instance or class, use
@staticmethod
- If the method needs to access the class itself, or use it to create an instance, use
@classmethod
It is not complicated but it is easy to ignore details. Pay attention to choosing according to actual needs.
The above is the detailed content of Differences Between Static Method and Class Method 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)

Hot Topics

Python's unittest and pytest are two widely used testing frameworks that simplify the writing, organizing and running of automated tests. 1. Both support automatic discovery of test cases and provide a clear test structure: unittest defines tests by inheriting the TestCase class and starting with test\_; pytest is more concise, just need a function starting with test\_. 2. They all have built-in assertion support: unittest provides assertEqual, assertTrue and other methods, while pytest uses an enhanced assert statement to automatically display the failure details. 3. All have mechanisms for handling test preparation and cleaning: un

PythonisidealfordataanalysisduetoNumPyandPandas.1)NumPyexcelsatnumericalcomputationswithfast,multi-dimensionalarraysandvectorizedoperationslikenp.sqrt().2)PandashandlesstructureddatawithSeriesandDataFrames,supportingtaskslikeloading,cleaning,filterin

Dynamic programming (DP) optimizes the solution process by breaking down complex problems into simpler subproblems and storing their results to avoid repeated calculations. There are two main methods: 1. Top-down (memorization): recursively decompose the problem and use cache to store intermediate results; 2. Bottom-up (table): Iteratively build solutions from the basic situation. Suitable for scenarios where maximum/minimum values, optimal solutions or overlapping subproblems are required, such as Fibonacci sequences, backpacking problems, etc. In Python, it can be implemented through decorators or arrays, and attention should be paid to identifying recursive relationships, defining the benchmark situation, and optimizing the complexity of space.

To implement a custom iterator, you need to define the __iter__ and __next__ methods in the class. ① The __iter__ method returns the iterator object itself, usually self, to be compatible with iterative environments such as for loops; ② The __next__ method controls the value of each iteration, returns the next element in the sequence, and when there are no more items, StopIteration exception should be thrown; ③ The status must be tracked correctly and the termination conditions must be set to avoid infinite loops; ④ Complex logic such as file line filtering, and pay attention to resource cleaning and memory management; ⑤ For simple logic, you can consider using the generator function yield instead, but you need to choose a suitable method based on the specific scenario.

Future trends in Python include performance optimization, stronger type prompts, the rise of alternative runtimes, and the continued growth of the AI/ML field. First, CPython continues to optimize, improving performance through faster startup time, function call optimization and proposed integer operations; second, type prompts are deeply integrated into languages ??and toolchains to enhance code security and development experience; third, alternative runtimes such as PyScript and Nuitka provide new functions and performance advantages; finally, the fields of AI and data science continue to expand, and emerging libraries promote more efficient development and integration. These trends indicate that Python is constantly adapting to technological changes and maintaining its leading position.

Python's socket module is the basis of network programming, providing low-level network communication functions, suitable for building client and server applications. To set up a basic TCP server, you need to use socket.socket() to create objects, bind addresses and ports, call .listen() to listen for connections, and accept client connections through .accept(). To build a TCP client, you need to create a socket object and call .connect() to connect to the server, then use .sendall() to send data and .recv() to receive responses. To handle multiple clients, you can use 1. Threads: start a new thread every time you connect; 2. Asynchronous I/O: For example, the asyncio library can achieve non-blocking communication. Things to note

The core answer to Python list slicing is to master the [start:end:step] syntax and understand its behavior. 1. The basic format of list slicing is list[start:end:step], where start is the starting index (included), end is the end index (not included), and step is the step size; 2. Omit start by default start from 0, omit end by default to the end, omit step by default to 1; 3. Use my_list[:n] to get the first n items, and use my_list[-n:] to get the last n items; 4. Use step to skip elements, such as my_list[::2] to get even digits, and negative step values ??can invert the list; 5. Common misunderstandings include the end index not

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
