Home Backend Development Python Tutorial Python中__init__和__new__的区别详解

Python中__init__和__new__的区别详解

Jun 16, 2016 am 08:43 AM
python

__init__ 方法是什么?

使用Python写过面向对象的代码的同学,可能对 __init__ 方法已经非常熟悉了,__init__ 方法通常用在初始化一个类实例的时候。例如:

# -*- coding: utf-8 -*-

class Person(object):
  """Silly Person"""

  def __init__(self, name, age):
    self.name = name
    self.age = age

  def __str__(self):
    return '<Person: %s(%s)>' % (self.name, self.age)

if __name__ == '__main__':
  piglei = Person('piglei', 24)
  print piglei

Copy after login

这样便是__init__最普通的用法了。但__init__其实不是实例化一个类的时候第一个被调用 的方法。当使用 Persion(name, age) 这样的表达式来实例化一个类时,最先被调用的方法 其实是 __new__ 方法。

__new__ 方法是什么?

__new__方法接受的参数虽然也是和__init__一样,但__init__是在类实例创建之后调用,而 __new__方法正是创建这个类实例的方法。

# -*- coding: utf-8 -*-

class Person(object):
  """Silly Person"""

  def __new__(cls, name, age):
    print '__new__ called.'
    return super(Person, cls).__new__(cls, name, age)

  def __init__(self, name, age):
    print '__init__ called.'
    self.name = name
    self.age = age

  def __str__(self):
    return '<Person: %s(%s)>' % (self.name, self.age)

if __name__ == '__main__':
  piglei = Person('piglei', 24)
  print piglei

Copy after login

执行结果:

piglei@macbook-pro:blog$ python new_and_init.py
__new__ called.
__init__ called.
<Person: piglei(24)>

Copy after login

通过运行这段代码,我们可以看到,__new__方法的调用是发生在__init__之前的。其实当 你实例化一个类的时候,具体的执行逻辑是这样的:

1.p = Person(name, age)

2.首先执行使用name和age参数来执行Person类的__new__方法,这个__new__方法会 返回Person类的一个实例(通常情况下是使用 super(Persion, cls).__new__(cls, ... ...) 这样的方式)

3.然后利用这个实例来调用类的__init__方法,上一步里面__new__产生的实例也就是 __init__里面的的 self

所以,__init__ 和 __new__ 最主要的区别在于:

1.__init__ 通常用于初始化一个新实例,控制这个初始化的过程,比如添加一些属性, 做一些额外的操作,发生在类实例被创建完以后。它是实例级别的方法。
2.__new__ 通常用于控制生成一个新实例的过程。它是类级别的方法。

但是说了这么多,__new__最通常的用法是什么呢,我们什么时候需要__new__?

__new__ 的作用

依照Python官方文档的说法,__new__方法主要是当你继承一些不可变的class时(比如int, str, tuple), 提供给你一个自定义这些类的实例化过程的途径。还有就是实现自定义的metaclass。

首先我们来看一下第一个功能,具体我们可以用int来作为一个例子:

假如我们需要一个永远都是正数的整数类型,通过集成int,我们可能会写出这样的代码。

class PositiveInteger(int):
  def __init__(self, value):
    super(PositiveInteger, self).__init__(self, abs(value))

i = PositiveInteger(-3)
print i

Copy after login

但运行后会发现,结果根本不是我们想的那样,我们任然得到了-3。这是因为对于int这种 不可变的对象,我们只有重载它的__new__方法才能起到自定义的作用。

这是修改后的代码:

class PositiveInteger(int):
  def __new__(cls, value):
    return super(PositiveInteger, cls).__new__(cls, abs(value))

i = PositiveInteger(-3)
print i

Copy after login

通过重载__new__方法,我们实现了需要的功能。

另外一个作用,关于自定义metaclass。其实我最早接触__new__的时候,就是因为需要自定义 metaclass,但鉴于篇幅原因,我们下次再来讲python中的metaclass和__new__的关系。

用__new__来实现单例

事实上,当我们理解了__new__方法后,我们还可以利用它来做一些其他有趣的事情,比如实现 设计模式中的 单例模式(singleton) 。

因为类每一次实例化后产生的过程都是通过__new__来控制的,所以通过重载__new__方法,我们 可以很简单的实现单例模式。

class Singleton(object):
  def __new__(cls):
    # 关键在于这,每一次实例化的时候,我们都只会返回这同一个instance对象
    if not hasattr(cls, 'instance'):
      cls.instance = super(Singleton, cls).__new__(cls)
    return cls.instance

obj1 = Singleton()
obj2 = Singleton()

obj1.attr1 = 'value1'
print obj1.attr1, obj2.attr1
print obj1 is obj2

Copy after login

输出结果:

value1 value1
True

可以看到obj1和obj2是同一个实例。

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
1 months ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. Best Graphic Settings
1 months ago By 尊渡假赌尊渡假赌尊渡假赌
Will R.E.P.O. Have Crossplay?
1 months ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

PHP and Python: Code Examples and Comparison PHP and Python: Code Examples and Comparison Apr 15, 2025 am 12:07 AM

PHP and Python have their own advantages and disadvantages, and the choice depends on project needs and personal preferences. 1.PHP is suitable for rapid development and maintenance of large-scale web applications. 2. Python dominates the field of data science and machine learning.

How is the GPU support for PyTorch on CentOS How is the GPU support for PyTorch on CentOS Apr 14, 2025 pm 06:48 PM

Enable PyTorch GPU acceleration on CentOS system requires the installation of CUDA, cuDNN and GPU versions of PyTorch. The following steps will guide you through the process: CUDA and cuDNN installation determine CUDA version compatibility: Use the nvidia-smi command to view the CUDA version supported by your NVIDIA graphics card. For example, your MX450 graphics card may support CUDA11.1 or higher. Download and install CUDAToolkit: Visit the official website of NVIDIACUDAToolkit and download and install the corresponding version according to the highest CUDA version supported by your graphics card. Install cuDNN library:

Detailed explanation of docker principle Detailed explanation of docker principle Apr 14, 2025 pm 11:57 PM

Docker uses Linux kernel features to provide an efficient and isolated application running environment. Its working principle is as follows: 1. The mirror is used as a read-only template, which contains everything you need to run the application; 2. The Union File System (UnionFS) stacks multiple file systems, only storing the differences, saving space and speeding up; 3. The daemon manages the mirrors and containers, and the client uses them for interaction; 4. Namespaces and cgroups implement container isolation and resource limitations; 5. Multiple network modes support container interconnection. Only by understanding these core concepts can you better utilize Docker.

Python vs. JavaScript: Community, Libraries, and Resources Python vs. JavaScript: Community, Libraries, and Resources Apr 15, 2025 am 12:16 AM

Python and JavaScript have their own advantages and disadvantages in terms of community, libraries and resources. 1) The Python community is friendly and suitable for beginners, but the front-end development resources are not as rich as JavaScript. 2) Python is powerful in data science and machine learning libraries, while JavaScript is better in front-end development libraries and frameworks. 3) Both have rich learning resources, but Python is suitable for starting with official documents, while JavaScript is better with MDNWebDocs. The choice should be based on project needs and personal interests.

MiniOpen Centos compatibility MiniOpen Centos compatibility Apr 14, 2025 pm 05:45 PM

MinIO Object Storage: High-performance deployment under CentOS system MinIO is a high-performance, distributed object storage system developed based on the Go language, compatible with AmazonS3. It supports a variety of client languages, including Java, Python, JavaScript, and Go. This article will briefly introduce the installation and compatibility of MinIO on CentOS systems. CentOS version compatibility MinIO has been verified on multiple CentOS versions, including but not limited to: CentOS7.9: Provides a complete installation guide covering cluster configuration, environment preparation, configuration file settings, disk partitioning, and MinI

How to operate distributed training of PyTorch on CentOS How to operate distributed training of PyTorch on CentOS Apr 14, 2025 pm 06:36 PM

PyTorch distributed training on CentOS system requires the following steps: PyTorch installation: The premise is that Python and pip are installed in CentOS system. Depending on your CUDA version, get the appropriate installation command from the PyTorch official website. For CPU-only training, you can use the following command: pipinstalltorchtorchvisiontorchaudio If you need GPU support, make sure that the corresponding version of CUDA and cuDNN are installed and use the corresponding PyTorch version for installation. Distributed environment configuration: Distributed training usually requires multiple machines or single-machine multiple GPUs. Place

How to choose the PyTorch version on CentOS How to choose the PyTorch version on CentOS Apr 14, 2025 pm 06:51 PM

When installing PyTorch on CentOS system, you need to carefully select the appropriate version and consider the following key factors: 1. System environment compatibility: Operating system: It is recommended to use CentOS7 or higher. CUDA and cuDNN:PyTorch version and CUDA version are closely related. For example, PyTorch1.9.0 requires CUDA11.1, while PyTorch2.0.1 requires CUDA11.3. The cuDNN version must also match the CUDA version. Before selecting the PyTorch version, be sure to confirm that compatible CUDA and cuDNN versions have been installed. Python version: PyTorch official branch

How to install nginx in centos How to install nginx in centos Apr 14, 2025 pm 08:06 PM

CentOS Installing Nginx requires following the following steps: Installing dependencies such as development tools, pcre-devel, and openssl-devel. Download the Nginx source code package, unzip it and compile and install it, and specify the installation path as /usr/local/nginx. Create Nginx users and user groups and set permissions. Modify the configuration file nginx.conf, and configure the listening port and domain name/IP address. Start the Nginx service. Common errors need to be paid attention to, such as dependency issues, port conflicts, and configuration file errors. Performance optimization needs to be adjusted according to the specific situation, such as turning on cache and adjusting the number of worker processes.

See all articles