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Metaprogramming with Metaclasses in Python

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  • Difficulty Level : Hard
  • Last Updated : 11 Oct, 2021

At first, the word Metaprogramming seems like a very funky and alien thing but if you have ever worked with decorators or metaclasses, you were doing metaprogramming there all along. In a nutshell, we can say metaprogramming is the code that manipulates code.
In this article, we are going to discuss Metaclasses, why and when we should use them, and what are the alternatives. This is a fairly advance Python topic and the following prerequisite is expected – 

Note: This article considers Python 3.3 and above 


In Python, everything has some type associated with it. For example, if we have a variable having an integer value then its type is int. You can get the type of anything using the type() function. 


num = 23
print("Type of num is:", type(num))
lst = [1, 2, 4]
print("Type of lst is:", type(lst))
name = "Atul"
print("Type of name is:", type(name))


Type of num is: <class 'int'>
Type of lst is: <class 'list'>
Type of name is: <class 'str'>

Every type in Python is defined by Class. So in the above example, unlike C++ or Java where int, char, float are primary data types, in Python they are objects of int class or str class. So we can make a new type by creating a class of that type. For example, we can create a new type of Student by creating a Student class. 


class Student:
stu_obj = Student()
# Print type of object of Student class
print("Type of stu_obj is:", type(stu_obj))


Type of stu_obj is: <class '__main__.Student'>

A Class is also an object, and just like any other object, it’s an instance of something called Metaclass. A special class type creates these Class objects. The type class is default metaclass which is responsible for making classes. In the above example, if we try to find out the type of Student class, it comes out to be a type


class Student:
# Print type of Student class
print("Type of Student class is:", type(Student))


Type of Student class is: <class 'type'>

Because Classes are also an object, they can be modified in the same way. We can add or subtract fields or methods in class in the same way we did with other objects. For example – 


# Defined class without any
# class methods and variables
class test:pass
# Defining method variables
test.x = 45
# Defining class methods = lambda self: print('Hello')
# creating object
myobj = test()



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. 
There are problems for which metaclass and non-metaclass-based solutions are available (which are often simpler) but in some cases, only metaclass can solve the problem. We will discuss such a problem in this article.

Creating custom Metaclass

To create our custom metaclass, our custom metaclass has to inherit type metaclass and usually override – 

  • __new__(): 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__(): This method just initialize the created object passed as a parameter

We can create classes using the type() 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 –  


def test_method(self):
    print("This is Test class method!")
# creating a base class
class Base:
    def myfun(self):
        print("This is inherited method!")
# Creating Test class dynamically using
# type() method directly
Test = type('Test', (Base, ), dict(x="atul", my_method=test_method))
# Print type of Test
print("Type of Test class: ", type(Test))
# Creating instance of Test class
test_obj = Test()
print("Type of test_obj: ", type(test_obj))
# calling inherited method
# calling Test class method
# printing variable


Type of Test class:  <class 'type'>
Type of test_obj:  <class '__main__.Test'>
This is inherited method!
This is Test class method!

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. 


# our metaclass
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)
# metaclass can be specified by 'metaclass' keyword argument
# now MultiBase class is used for creating classes
# this will be propagated to all subclasses of Base
class Base(metaclass=MultiBases):
# no error is raised
class A(Base):
# no error is raised
class B(Base):
# This will raise an error!
class C(A, B):


Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
  File "<stdin>", line 8, in __new__
TypeError: Inherited multiple base classes!!!

Solving problems with metaclass

There are some problems which can be solved by decorators (easily) as well as by metaclasses. But there are a few problems whose results can only be achieved by metaclasses. For example, consider a very simple problem of code repetition. 
We want to debug class methods, what we want is that whenever the class method executes, it should print its fully qualified name before executing its body.

The very first solution that comes to our mind is using method decorators, following is the sample code – 


from functools import wraps
def debug(func):
    '''decorator for debugging passed function'''
    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
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))


Full name of this method: Calc.add
Full name of this method: Calc.mul

This solution works fine but there is one problem, what if we want to apply this method decorator to all subclasses which inherit this Calc class. In that case, we have to separately apply the method decorator to every subclass just like we did with the Calc class.
The problem is if we have many such subclasses, then in that case we won’t like adding a decorator to each one separately. If we know beforehand that every subclass must have this debug property, then we should look up to the metaclass-based solution.
Have a look at this metaclass based solution, the idea is that classes will be created normally and then immediately wrapped up by debug method decorator – 


from functools import wraps
def debug(func):
    '''decorator for debugging passed function'''
    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))


Full name of this method: Calc_adv.mul

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). 


This article is contributed by Atul Kumar. If you like GeeksforGeeks and would like to contribute, you can also write an article using or mail your article to See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.

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