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[](https://www.geeksforgeeks.org/)  - Sign In - [Courses]() - [Tutorials]() - [Interview Prep]() - [Python Tutorial](https://www.geeksforgeeks.org/python/python-programming-language-tutorial/) - [Data Types](https://www.geeksforgeeks.org/python/python-data-types/) - [Interview Questions](https://www.geeksforgeeks.org/python/python-interview-questions/) - [Examples](https://www.geeksforgeeks.org/python/python-programming-examples/) - [Quizzes](https://www.geeksforgeeks.org/python/python-quizzes/) - [DSA Python](https://www.geeksforgeeks.org/dsa/python-data-structures-and-algorithms/) - [Data Science](https://www.geeksforgeeks.org/data-science/data-science-with-python-tutorial/) - [NumPy](https://www.geeksforgeeks.org/python/numpy-tutorial/) - [Pandas](https://www.geeksforgeeks.org/pandas/pandas-tutorial/) - [Practice](https://www.geeksforgeeks.org/dsa/geeksforgeeks-practice-best-online-coding-platform/) # Metaprogramming with Metaclasses in Python Last Updated : 12 Apr, 2025 Metaprogramming in [Python](https://www.geeksforgeeks.org/python/python-programming-language-tutorial/) lets us write code that can modify or generate other code at runtime. One of the key tools for achieving this is metaclasses, which allow us to control the creation and behavior of classes. ## What Are Metaclasses? [Metaclasses](https://www.geeksforgeeks.org/python/python-metaclasses/) are classes that define how other classes are created. While regular classes act as blueprints for creating [objects](https://www.geeksforgeeks.org/python/python-classes-and-objects/), metaclasses serve as blueprints for creating classes themselves. When we create a class in Python, it is, in fact, an instance of a metaclass. By default, every class in Python is an instance of the built-in type metaclass. This whole meta thing can be summarized as - ****Metaclass create Classes and Classes creates objects****.  The metaclass is responsible for the generation of classes, so we can write our custom metaclasses to modify the way classes are generated by performing extra actions or injecting code. Usually, we do not need custom metaclasses but sometimes it's necessary. ## Creating custom Metaclass To create our custom metaclass, our custom metaclass has to inherit ****type**** metaclass and usually override - - [\_\_new\_\_()](https://www.geeksforgeeks.org/python/__new__-in-python/): It's a method which is called before \_\_init\_\_(). It creates the object and returns it. We can override this method to control how the objects are created. - [\_\_init\_\_()](https://www.geeksforgeeks.org/python/__init__-in-python/): This method just initialize the created object passed as a parameter We can create classes using the [type()](https://www.geeksforgeeks.org/python/python-type-function/) function directly. It can be called in following ways - 1. When called with only one argument, it returns the type. We have seen it before in the above examples. 2. When called with three parameters, it creates a class. Following arguments are passed to it - 1. Class name 2. Tuple having base classes inherited by class 3. ****Class Dictionary:**** It serves as a local namespace for the class, populated with class methods and variables Consider this example - Python `` ``` def test(self): ``` ``` print("This is Test class method!") ``` ``` ​ ``` ``` class Base: ``` ``` def myfun(self): ``` ``` print("This is inherited method!") ``` ``` ​ ``` ``` # Creating Test class dynamically using type() ``` ``` Test = type('Test', (Base, ), dict(x="ankit", my_method=test)) ``` ``` ​ ``` ``` print("Type of Test class: ", type(Test)) ``` ``` ​ ``` ``` obj = Test() ``` ``` print("Type of obj: ", type(obj)) ``` ``` ​ ``` ``` obj.myfun() ``` ``` ​ ``` ``` obj.my_method() ``` ``` ​ ``` ``` print(obj.x) ``` **Output** ``` Type of Test class: <class 'type'> Type of obj: <class '__main__.Test'> This is inherited method! This is Test class method! ankit ``` ****Explanation:**** - ****test**** Method prints "This is Test class method!". - ****Base**** Class has myfun method that prints "This is inherited method!". - ****Dynamic Test Class**** is created with ****type()****, inherits from ****Base****, adds ****x="ankit"**** and ****my\_method=test****. - Prints types of ****Test**** and ****obj****, calls ****myfun()**** and ****my\_method()****, then prints ****x**** ("ankit"). Now let's create a metaclass without using ****type()**** directly. In the following example, we will be creating a metaclass *****MultiBases***** which will check if the class being created has inherited from more than one base class. If so, it will raise an error. Python `` ``` class MultiBases(type): ``` ``` # overriding __new__ method ``` ``` def __new__(cls, clsname, bases, clsdict): ``` ``` # if no of base classes is greater than 1 raise error ``` ``` if len(bases)>1: ``` ``` raise TypeError("Inherited multiple base classes!!!") ``` ``` ``` ``` # else execute __new__ method of super class, ie. call __init__ of type class ``` ``` return super().__new__(cls, clsname, bases, clsdict) ``` ``` ​ ``` ``` class Base(metaclass=MultiBases): ``` ``` pass ``` ``` ​ ``` ``` class A(Base): ``` ``` pass ``` ``` ​ ``` ``` class B(Base): ``` ``` pass ``` ``` ​ ``` ``` class C(A, B): ``` ``` pass ``` ****Output:**** ``` Traceback (most recent call last): File "main.py", line 20, in <module> class C(A, B): File "main.py", line 6, in __new__ raise TypeError("Inherited multiple base classes!!!") TypeError: Inherited multiple base classes!!! ``` ****Explanation:**** This code defines a custom metaclass ****MultiBases**** that restricts classes from inheriting from more than one base class. In the ****\_\_new\_\_**** method, it checks if the class has more than one base class and raises a ****TypeError**** if so. The ****Base**** class uses this metaclass, and while ****A**** and ****B**** (subclasses of Base) don't raise any errors, ****C**** (inheriting from both ****A**** and ****B****) triggers a ****TypeError****. ## Solving problems with metaclass Some problems in Python can be solved using [decorators](https://www.geeksforgeeks.org/python/decorators-in-python/) or ****metaclasses****. While decorators provide a simple solution for many tasks, there are certain situations where only ****metaclasses**** can provide a more efficient or scalable solution. ### Problem: Debugging Class Methods The task is to debug class methods, meaning every time a method is executed, it should print its fully qualified name before executing its body. ### Solution 1: Using Method Decorators Here's an implementation using method decorators to debug the methods of a class: Python `` ``` from functools import wraps ``` ``` ​ ``` ``` def debug(func): ``` ``` '''decorator for debugging passed function''' ``` ``` ``` ``` @wraps(func) ``` ``` def wrapper(*args, **kwargs): ``` ``` print("Full name of this method:", func.__qualname__) ``` ``` return func(*args, **kwargs) ``` ``` return wrapper ``` ``` ​ ``` ``` def debugmethods(cls): ``` ``` '''class decorator make use of debug decorator ``` ``` to debug class methods ''' ``` ``` ``` ``` # check in class dictionary for any callable(method) if exist, replace it with debugged version ``` ``` for key, val in vars(cls).items(): ``` ``` if callable(val): ``` ``` setattr(cls, key, debug(val)) ``` ``` return cls ``` ``` ​ ``` ``` # sample class ``` ``` @debugmethods ``` ``` class Calc: ``` ``` def add(self, x, y): ``` ``` return x+y ``` ``` def mul(self, x, y): ``` ``` return x*y ``` ``` def div(self, x, y): ``` ``` return x/y ``` ``` ``` ``` mycal = Calc() ``` ``` print(mycal.add(2, 3)) ``` ``` print(mycal.mul(5, 2)) ``` **Output** ``` Full name of this method: Calc.add 5 Full name of this method: Calc.mul 10 ``` ****Explanation:**** - ****debug Decorator:**** This decorator prints the fully qualified name of the method (****func.\_\_qualname\_\_****) and then calls the method. - ****debugmethods Class Decorator:**** This decorator scans the class dictionary for callable methods and applies the ****debug**** decorator to them. - ****Calc Class:**** The ****Calc**** class uses the ****@debugmethods**** decorator, so its methods (****add****, ****mul****, and ****div****) will print their fully qualified names when called. ### Solution 2: Using a Metaclass Now, let's look at a metaclass-based solution that automatically applies the debugging functionality to all subclasses of a base class. Python `` ``` from functools import wraps ``` ``` ​ ``` ``` def debug(func): ``` ``` '''decorator for debugging passed function''' ``` ``` ``` ``` @wraps(func) ``` ``` def wrapper(*args, **kwargs): ``` ``` print("Full name of this method:", func.__qualname__) ``` ``` return func(*args, **kwargs) ``` ``` return wrapper ``` ``` ​ ``` ``` def debugmethods(cls): ``` ``` '''class decorator make use of debug decorator to debug class methods ''' ``` ``` ``` ``` for key, val in vars(cls).items(): ``` ``` if callable(val): ``` ``` setattr(cls, key, debug(val)) ``` ``` return cls ``` ``` ​ ``` ``` class debugMeta(type): ``` ``` '''meta class which feed created class object to debugmethod to get debug functionality enabled objects''' ``` ``` ``` ``` def __new__(cls, clsname, bases, clsdict): ``` ``` obj = super().__new__(cls, clsname, bases, clsdict) ``` ``` obj = debugmethods(obj) ``` ``` return obj ``` ``` ``` ``` # base class with metaclass 'debugMeta' now all the subclass of this will have debugging applied ``` ``` class Base(metaclass=debugMeta):pass ``` ``` ​ ``` ``` # inheriting Base ``` ``` class Calc(Base): ``` ``` def add(self, x, y): ``` ``` return x+y ``` ``` ``` ``` # inheriting Calc ``` ``` class Calc_adv(Calc): ``` ``` def mul(self, x, y): ``` ``` return x*y ``` ``` ​ ``` ``` # Now Calc_adv object showing debugging behaviour ``` ``` mycal = Calc_adv() ``` ``` print(mycal.mul(2, 3)) ``` **Output** ``` Full name of this method: Calc_adv.mul 6 ``` ****Explanation:**** - ****debug Decorator:**** Same as in the first solution, it prints the fully qualified name of the method and calls it. - ****debugmethods Class Decorator:**** This applies the ****debug**** decorator to all methods of a class. - ****debugMeta Metaclass:**** The ****debugMeta**** metaclass overrides the ****\_\_new\_\_**** method to automatically apply the debugmethods decorator to the class when it is created. This ensures that all methods in any class using this metaclass will have debugging enabled without needing to explicitly apply the decorator. - ****Base Class:**** The ****Base**** class uses the ****debugMeta**** metaclass, so all subclasses will automatically have debugging applied to their methods. ## When to use Metaclasses Most of the time we do not use metaclasses, it's usually used for something complicated, but a few cases where we use metaclasses are - - As we have seen in the above example, metaclasses propagate down the inheritance hierarchies. It will affect all the subclasses as well. If we have such a situation, then we should use metaclasses. - If we want to change class automatically, when it is created, we use metaclasses - For API development, we might use metaclasses As quoted by Tim Peters > Metaclasses are deeper magic that 99% of users should never worry about. If you wonder whether you need them, you don't (the people who actually need them know with certainty that they need them, and don't need an explanation about why). 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Metaprogramming in [Python](https://www.geeksforgeeks.org/python/python-programming-language-tutorial/) lets us write code that can modify or generate other code at runtime. One of the key tools for achieving this is metaclasses, which allow us to control the creation and behavior of classes. ## What Are Metaclasses? [Metaclasses](https://www.geeksforgeeks.org/python/python-metaclasses/) are classes that define how other classes are created. While regular classes act as blueprints for creating [objects](https://www.geeksforgeeks.org/python/python-classes-and-objects/), metaclasses serve as blueprints for creating classes themselves. When we create a class in Python, it is, in fact, an instance of a metaclass. By default, every class in Python is an instance of the built-in type metaclass. This whole meta thing can be summarized as - ****Metaclass create Classes and Classes creates objects****.  The metaclass is responsible for the generation of classes, so we can write our custom metaclasses to modify the way classes are generated by performing extra actions or injecting code. Usually, we do not need custom metaclasses but sometimes it's necessary. ## Creating custom Metaclass To create our custom metaclass, our custom metaclass has to inherit ****type**** metaclass and usually override - - [\_\_new\_\_()](https://www.geeksforgeeks.org/python/__new__-in-python/): It's a method which is called before \_\_init\_\_(). It creates the object and returns it. We can override this method to control how the objects are created. - [\_\_init\_\_()](https://www.geeksforgeeks.org/python/__init__-in-python/): This method just initialize the created object passed as a parameter We can create classes using the [type()](https://www.geeksforgeeks.org/python/python-type-function/) function directly. It can be called in following ways - 1. When called with only one argument, it returns the type. We have seen it before in the above examples. 2. When called with three parameters, it creates a class. Following arguments are passed to it - 1. Class name 2. Tuple having base classes inherited by class 3. ****Class Dictionary:**** It serves as a local namespace for the class, populated with class methods and variables Consider this example - **Output** ``` Type of Test class: <class 'type'> Type of obj: <class '__main__.Test'> This is inherited method! This is Test class method! ankit ``` ****Explanation:**** - ****test**** Method prints "This is Test class method!". - ****Base**** Class has myfun method that prints "This is inherited method!". - ****Dynamic Test Class**** is created with ****type()****, inherits from ****Base****, adds ****x="ankit"**** and ****my\_method=test****. - Prints types of ****Test**** and ****obj****, calls ****myfun()**** and ****my\_method()****, then prints ****x**** ("ankit"). Now let's create a metaclass without using ****type()**** directly. In the following example, we will be creating a metaclass *****MultiBases***** which will check if the class being created has inherited from more than one base class. If so, it will raise an error. ****Output:**** ``` Traceback (most recent call last): File "main.py", line 20, in <module> class C(A, B): File "main.py", line 6, in __new__ raise TypeError("Inherited multiple base classes!!!") TypeError: Inherited multiple base classes!!! ``` ****Explanation:**** This code defines a custom metaclass ****MultiBases**** that restricts classes from inheriting from more than one base class. In the ****\_\_new\_\_**** method, it checks if the class has more than one base class and raises a ****TypeError**** if so. The ****Base**** class uses this metaclass, and while ****A**** and ****B**** (subclasses of Base) don't raise any errors, ****C**** (inheriting from both ****A**** and ****B****) triggers a ****TypeError****. ## Solving problems with metaclass Some problems in Python can be solved using [decorators](https://www.geeksforgeeks.org/python/decorators-in-python/) or ****metaclasses****. While decorators provide a simple solution for many tasks, there are certain situations where only ****metaclasses**** can provide a more efficient or scalable solution. ### Problem: Debugging Class Methods The task is to debug class methods, meaning every time a method is executed, it should print its fully qualified name before executing its body. ### Solution 1: Using Method Decorators Here's an implementation using method decorators to debug the methods of a class: **Output** ``` Full name of this method: Calc.add 5 Full name of this method: Calc.mul 10 ``` ****Explanation:**** - ****debug Decorator:**** This decorator prints the fully qualified name of the method (****func.\_\_qualname\_\_****) and then calls the method. - ****debugmethods Class Decorator:**** This decorator scans the class dictionary for callable methods and applies the ****debug**** decorator to them. - ****Calc Class:**** The ****Calc**** class uses the ****@debugmethods**** decorator, so its methods (****add****, ****mul****, and ****div****) will print their fully qualified names when called. ### Solution 2: Using a Metaclass Now, let's look at a metaclass-based solution that automatically applies the debugging functionality to all subclasses of a base class. **Output** ``` Full name of this method: Calc_adv.mul 6 ``` ****Explanation:**** - ****debug Decorator:**** Same as in the first solution, it prints the fully qualified name of the method and calls it. - ****debugmethods Class Decorator:**** This applies the ****debug**** decorator to all methods of a class. - ****debugMeta Metaclass:**** The ****debugMeta**** metaclass overrides the ****\_\_new\_\_**** method to automatically apply the debugmethods decorator to the class when it is created. This ensures that all methods in any class using this metaclass will have debugging enabled without needing to explicitly apply the decorator. - ****Base Class:**** The ****Base**** class uses the ****debugMeta**** metaclass, so all subclasses will automatically have debugging applied to their methods. ## When to use Metaclasses Most of the time we do not use metaclasses, it's usually used for something complicated, but a few cases where we use metaclasses are - - As we have seen in the above example, metaclasses propagate down the inheritance hierarchies. It will affect all the subclasses as well. If we have such a situation, then we should use metaclasses. - If we want to change class automatically, when it is created, we use metaclasses - For API development, we might use metaclasses As quoted by Tim Peters > Metaclasses are deeper magic that 99% of users should never worry about. If you wonder whether you need them, you don't (the people who actually need them know with certainty that they need them, and don't need an explanation about why).