


Detailed explanation of metaclasses and their usage in Python
1. Use metaclass to verify subclasses
Whenever we define a new class, the metaclass will run Yazheng code to ensure that the new class conforms to the specified specifications.
After the Python system has processed the class statement of the subclass, it will call the __new__
method of the metaclass. The metaclass can obtain the name, parent and attributes of the subclass and grandchild class through the __new__
method.
This eliminates the need for us to put the verification code in the __init__
method of this class and wait until the object is built before verifying.
In the following example, a subclass with less than 3 edges is defined. Once the class statement ends, the metaclass verification code will reject the class.
class ValidatePolygon(type): def __new__(meta, name, bases, class_dict): # Don't validate the abstract Polygon class if bases != (object,): if class_dict['sides'] < 3: raise ValueError('Polygons need 3+ sides') return type.__new__(meta, name, bases, class_dict) class Polygon(object, metaclass=ValidatePolygon): sides = None # Specified by subclasses @classmethod def interior_angles(cls): return (cls.sides - 2) * 180 class Triangle(Polygon): sides = 3 print(Triangle.interior_angles())
2. Register subclasses with metaclasses
Every time you inherit a subclass from a base class, the metaclass of the base class can automatically run the registration code.
This is useful when a 'reverse lookup' is required to establish a mapping between a simple identifier and the corresponding class.
Still used is that after the class statement is executed, the __new__
method of the metaclass is automatically called.
import json registry = {} def register_class(target_class): registry[target_class.__name__] = target_class def deserialize(data): params = json.loads(data) name = params['class'] target_class = registry[name] return target_class(*params['args']) class Meta(type): def __new__(meta, name, bases, class_dict): cls = type.__new__(meta, name, bases, class_dict) register_class(cls) return cls class Serializable(object): def __init__(self, *args): self.args = args def serialize(self): return json.dumps({ 'class': self.__class__.__name__, 'args': self.args, }) def __repr__(self): return '%s(%s)' % ( self.__class__.__name__, ', '.join(str(x) for x in self.args)) class RegisteredSerializable(Serializable, metaclass=Meta): pass class Vector3D(RegisteredSerializable): def __init__(self, x, y, z): super().__init__(x, y, z) self.x, self.y, self.z = x, y, z v3 = Vector3D(10, -7, 3) print('Before: ', v3) data = v3.serialize() print('Serialized:', data) print('After: ', deserialize(data)) print(registry)
3. Use metaclasses to annotate class attributes
Using metaclasses is like placing a hook on the class statement. After the class statement is processed, the hook will be triggered immediately.
In the following, Filed.name
and Filed.name
are set with the help of metaclasses.
class Field(object): def __init__(self): # These will be assigned by the metaclass. self.name = None self.internal_name = None def __get__(self, instance, instance_type): if instance is None: return self return getattr(instance, self.internal_name, '') def __set__(self, instance, value): setattr(instance, self.internal_name, value) class Meta(type): def __new__(meta, name, bases, class_dict): for key, value in class_dict.items(): if isinstance(value, Field): value.name = key value.internal_name = '_' + key cls = type.__new__(meta, name, bases, class_dict) return cls class DatabaseRow(object, metaclass=Meta): pass class BetterCustomer(DatabaseRow): first_name = Field() last_name = Field() prefix = Field() suffix = Field() foo = BetterCustomer() print('Before:', repr(foo.first_name), foo.__dict__) foo.first_name = 'Euler' print('After: ', repr(foo.first_name), foo.__dict__)
The metaclass summary ends here, and I don’t fully understand it.
I hope pythoners who have a deep understanding of this can leave a message.
Code comes from:
The above is the detailed content of Detailed explanation of metaclasses and their usage in Python. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

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

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

Undress AI Tool
Undress images for free

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











PHP is mainly procedural programming, but also supports object-oriented programming (OOP); Python supports a variety of paradigms, including OOP, functional and procedural programming. PHP is suitable for web development, and Python is suitable for a variety of applications such as data analysis and machine learning.

PHP is suitable for web development and rapid prototyping, and Python is suitable for data science and machine learning. 1.PHP is used for dynamic web development, with simple syntax and suitable for rapid development. 2. Python has concise syntax, is suitable for multiple fields, and has a strong library ecosystem.

To run Python code in Sublime Text, you need to install the Python plug-in first, then create a .py file and write the code, and finally press Ctrl B to run the code, and the output will be displayed in the console.

PHP originated in 1994 and was developed by RasmusLerdorf. It was originally used to track website visitors and gradually evolved into a server-side scripting language and was widely used in web development. Python was developed by Guidovan Rossum in the late 1980s and was first released in 1991. It emphasizes code readability and simplicity, and is suitable for scientific computing, data analysis and other fields.

Python is more suitable for beginners, with a smooth learning curve and concise syntax; JavaScript is suitable for front-end development, with a steep learning curve and flexible syntax. 1. Python syntax is intuitive and suitable for data science and back-end development. 2. JavaScript is flexible and widely used in front-end and server-side programming.

Golang is better than Python in terms of performance and scalability. 1) Golang's compilation-type characteristics and efficient concurrency model make it perform well in high concurrency scenarios. 2) Python, as an interpreted language, executes slowly, but can optimize performance through tools such as Cython.

Writing code in Visual Studio Code (VSCode) is simple and easy to use. Just install VSCode, create a project, select a language, create a file, write code, save and run it. The advantages of VSCode include cross-platform, free and open source, powerful features, rich extensions, and lightweight and fast.

Running Python code in Notepad requires the Python executable and NppExec plug-in to be installed. After installing Python and adding PATH to it, configure the command "python" and the parameter "{CURRENT_DIRECTORY}{FILE_NAME}" in the NppExec plug-in to run Python code in Notepad through the shortcut key "F6".
