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This article introduces the relevant knowledge of "how to create Python metaclass". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

What is the Python metaclass?

The Python metaclass is one of the advanced features related to Python's object-oriented programming concepts. It determines the behavior of the class and further helps it modify it.

Each class created with Python has a base Metaclass. Therefore, when you create a class, you will use metaclass indirectly. It happens implicitly, and you don't need to specify anything.

The metaclass associated with metaprogramming determines the ability of the program to operate on itself. Learning metaclasses may seem complex, but let's start with some concepts of classes and objects to make it easier to understand.

Classes and objects in Python

A class is a blueprint, a logical entity with objects. A simple class does not allocate any memory when it is declared; it occurs when an instance of a class is created.

Through the object you create, you can access the class. This class is used as a template only. The properties of an object essentially mean that we can interact with it at run time, pass parameters such as variables, store, modify, or interact with it.

You can use the _ _ class__ property to check the class of an object. Let's look at a simple example:

Class Demo: pass # This is a class named demo test=Demo () print (test.__class__) # shows class of objprint (type (test)) # alternate method

Output:

Python deals heavily with the concept of classes and objects and allows for easy and smooth application development. But what makes Python different from languages like Java and C? Everything in a Python can be defined as an object with properties and methods. The keynote speech is that a class in Python is just another object of a larger class.

Class defines rules for objects. Similarly, metaclasses are responsible for assigning behaviors to classes. We already know that a class is an object, just as every object has an instance, a class is an instance of a metaclass.

But there are also languages like Ruby and Objective-C that support metaclasses. So what makes Python Metaclass better, and why learn it? The answer is dynamic classes in Python. Let's take a closer look.

Dynamic classes in Python

Python is a dynamic programming language that allows classes to be created at run time. Unlike other languages such as C +, the latter only allows classes to be created at compile time. Python is superior to other static types of languages in terms of flexibility.

There is not much difference between dynamically and statically typed languages, but in Python it becomes more useful by providing metaprogramming.

But what if I told you that there is another key feature that distinguishes Python from other programming languages?

Languages such as Java or C + + have data types such as float,char,int, while Python treats each variable as an object. Each object belongs to a class, such as the int class or the str class. You can simply check the class of any variable using a built-in function called type ().

Number = 10993print ("Type associated is:", type (number)) name = "Aishwarya" print ("Type associated is:", type (name))

Output:

Type associated is:

Type associated is:

Now you know that everything in Python has a type associated with it. In the next topic, we will try to understand how metaclasses actually work.

How does the Python metaclass work?

Whenever a class is created, the default Metaclass type is called. The metaclass contains information such as the name, the base class set, and the attributes associated with the class. Therefore, when you instantiate a class, the class with these parameters is called. You can create metaclasses in two ways:

Type class

Custom metaclass

Let's move on to entering class and how to create a class.

Type class

Python has a built-in metaclass called type. Unlike Java or C, there are major data types. Every variable or object in Python has a class associated with it. Python uses the Type class behind the scenes to create all classes. In the previous topic, we saw how to use type () to examine the class of an object. Let's give an example of how to define a new type by creating a simple class.

Class Edureka (): obj = Edureka () print (type (obj)

Output:

Print (type (Edureka))

Output:

In the above code, we have a class named Edureka and an associated object. We created a new type called Edureka by simply creating a class called itself after that type. In the second code, when we check the type of the Edureka class, the result is "type."

Therefore, unless otherwise defined, metaclasses use type classes to create all other classes. We can access the Type class in two ways:

When we pass parameters through a type class, it uses the following syntax.

Type (_ _ name__, _ _ base__, attributes)

The name is a string with a class name

The foundation is a tuple that helps create subclasses

Property is a dictionary and assigns key-value pairs

Because a class in Python behaves like an object, its behavior can be changed in the same way. We can add or remove methods within a class, similar to the way objects are handled.

Now you know that Metaclass creates all the other classes in Python and defines their behavior using the type class. However, you must be wondering, is there any other way we can create metaclasses? So let's look at how to create a custom metaclass.

Custom metaclass in Python

Now we know and understand how type classes work. Now it's time to learn how to create custom metaclasses. We can modify the work of the class by performing actions or code injection. To do this, we can pass Metaclass as a keyword when creating a class definition. In addition, we can do this by simply inheriting classes instantiated by this Metaclass keyword.

When creating a new class, Python looks for the _ _ metaclass__ keyword. Just in case, if it doesn't exist. It follows the type class hierarchy.

After Python executes all dictionaries in the namespace, it invokes the type object, which creates the object of the class. There are two ways to create a custom metaclass.

Class EduFirst (type): def _ new__ (cls, name, base_cls, dict): passclass EduSecond (type): def _ init__ (self, name, base_cls, dict): pass

Let me explain these two methods in detail:

_ _ new _ _ (): used when the user wants to define a tuple dictionary before the class is created. It returns an instance of a class and can easily override / manage the object stream.

_ _ init _ _ (): call the object after it has been created and initialized.

What is the _ _ call__ in Python?

In formal Python documents, the _ _ call__ method can be used to define custom metaclasses. Similarly, when calling a class to define custom behavior, we can override other methods such as _ _ prepare__.

Just like how a class is like a template for creating an object, a metaclass is like a template for creating a class. Therefore, metaclasses are also called class factories.

See the next example:

Class Meta (type): def _ init__ (cls, name, base, dct): cls.attribute = 200class Test (metaclass = Meta): passTest.attribute

Output: 200

Metaclass allows custom classes. There are many other efficient and much simpler ways to achieve the same output through these methods. One such example is the use of decorators.

Decorator vs metaclass

Decorator is a popular feature of Python that allows you to add more functionality to your code. Decorators are callable objects that can help modify existing classes or even functions. During compilation, part of the code calls and modifies another part. This process is also known as metaprogramming.

Def decorator (cls): class NewClass (cls): attribute = 200return NewClass@decoratorClass Test1: pass@decorator Class Test2: passTest1.attribute Test2.attribute

Output: 200

Decorator in Python is a very useful and powerful tool to help you change the behavior of a function without actually changing any code. This is convenient when you want to modify part of the program while debugging rather than rewriting the function or changing the entire program. Instead, you just need to write a single-line decorator and let it handle the rest.

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