In which year was python officially released?
The founder of Python - Guido, a Dutchman, received a master's degree in mathematics and computer science from the University of Amsterdam. Guido hopes to have a language that can fully call the computer's functional interface like C language, and can be easily programmed like shell. So python was born. What I value most is the high efficiency of Python: because the Python language has rich and powerful class libraries, the development efficiency of Python can be significantly improved!
python time History:
In 1991, the first Python compiler was born. It is implemented in C language and can call C language library files. From its birth, Python has had: classes, functions, exception handling, core data types including tables and dictionaries, and a module-based expansion system.
Python 1.0 - January 1994 Added lambda, map, filter and reduce.
1999 Zope 1, the ancestor of Python's web framework, was released
Python 2.0 - 2000 /10/16, added a memory recycling mechanism, forming the basis of the current Python language framework
Python 2.4 – 2004/11/30, the same year the most popular WEB framework Django was born
Python 2.5 - 2006/09/19
Python 2.6 - 2008/10/1
Python 2.7 - 2010/07/03
Python 3.0 - 2008/12/03
Python 3.1 - 2009/06/27
Python 3.2 - 2011/02/20
Python 3.3 - 2012/09/29
Python 3.4 - 2014/03/16
Python 3.5 - 2015/09/13
Python 3.6 - 2016/12/23
In November 2014, Python 2.7 will be released in 2020 End of support was announced in 2017, and version 2.8 will not be released. When Python was first released, there were some flaws in the design. For example, the Unicode standard appeared later than Python, so the support for Unicode has not been complete, and the characters supported by ASCII encoding are limited. Example: Poor Chinese support Python3 is a major upgrade compared to earlier versions of Python. Py3 was not designed with downward compatibility in mind, so many early versions of Python programs cannot run on Py3. In order to take care of the earlier versions, a transitional version 2.6 was launched - basically using the syntax and libraries of Python 2.x, while considering the migration to Python 3.0, allowing the use of some Python 3.0 syntax and functions. In 2010, the compatible version 2.7 was launched. A large number of Python 3 features were reverse-migrated to Python 2.7. 2.7 was much better than 2.6. It also had a large number of features and libraries in 3 and took care of the original Python development crowd.
Related recommendations: "Python Video Tutorial"
Python 3.0 version was officially released on December 3, 2008, python development The author also stated that Python 2.7 is expected to cease maintenance in April 2020! Therefore, Python3 will be home from now on, and Python2 will permanently withdraw from the stage of history!
Python 3 is considered the future of Python and is the version of the language currently under development. Python 3 was released in late 2008 as a major overhaul to address and correct inherent design flaws in previous versions of the language. The focus of Python 3 development was to clean up the code base and remove redundancy, making it clear that there was only one way to perform a given task. Python3 is not backward compatible, which means that Python2 code does not support running in Python3! Major modifications to Python 3.0 include changing the print statement to a built-in function, improving the way integers are split, and providing more support for Unicode. As can be seen from the number of Python packages that support Python 3, Python 3 has become more and more popular. More and more adoption is coming. More and more people are using Python3, and Python3 is bound to become the protagonist in the future!
The above is the detailed content of In which year was python officially released?. 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

AI Hentai Generator
Generate AI Hentai for free.

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 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.

Efficient training of PyTorch models on CentOS systems requires steps, and this article will provide detailed guides. 1. Environment preparation: Python and dependency installation: CentOS system usually preinstalls Python, but the version may be older. It is recommended to use yum or dnf to install Python 3 and upgrade pip: sudoyumupdatepython3 (or sudodnfupdatepython3), pip3install--upgradepip. CUDA and cuDNN (GPU acceleration): If you use NVIDIAGPU, you need to install CUDATool

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.

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.

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:

When selecting a PyTorch version under CentOS, the following key factors need to be considered: 1. CUDA version compatibility GPU support: If you have NVIDIA GPU and want to utilize GPU acceleration, you need to choose PyTorch that supports the corresponding CUDA version. You can view the CUDA version supported by running the nvidia-smi command. CPU version: If you don't have a GPU or don't want to use a GPU, you can choose a CPU version of PyTorch. 2. Python version PyTorch

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

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
