What are some good Python projects on GitHub?
Among the community of developers and programmers, Python is the most popular and in-demand programming language. Around 73 million developers may access an open-source community using Git repositories through GitHub. Python projects are highly sought after to effectively boost programming language expertise, and GitHub can help with that. From building a straightforward password generator to automating repetitive jobs and mining Twitter Data, the repository has something for everyone.
让我们来看一些当前流行的GitHub开源Python项目。
Google Images Download
Hundreds of Google photos may be searched for and downloaded with this command-line Python tool. The script has the ability to search for words and phrases and, if desired, download picture assets. Python versions 2.x and 3.x are compatible with Google Pictures Download. You can study the project's source code to improve your programming abilities and comprehend how it applies in actual situations.
DeepFaceLab
的翻译为中文为:DeepFaceLab
“Iperov”开发了用于人脸交换的开源DeepFaceLab技术。它提供了一个必要且简单的流程,任何人都可以使用,而无需完全理解深度学习框架或创建模型。该系统提供了一种灵活且松散的耦合结构,用户可以在自己的流程中添加更多功能,而无需编写冗长的样板代码。
空气流动
The Python open-source project Airflow offers a variety of REST API endpoints across the objects and is available on GitHub. JSON is accepted as input, and JSON is also returned as output. Backward compatibility with Python programs is included in the Airflow APIs.
Xonsh
的中文翻译为:Xonsh
像Unix这样的命令行解释器对于交互式程序是必需的。这些操作系统使用shell脚本来控制执行。现在,如果你的shell能够理解一种更可扩展的编程语言,而不是不得不妥协,那不是更实用吗?这就是Xonsh(发音为"Konk")的用武之地。
它是一个运行在Python之上的提示符shell语言。这个跨平台语言拥有庞大的标准库和各种变量类型,使得编写脚本变得简单。Xonsh还使用了一个名为vox的虚拟环境管理系统。
ML-Agents
一个名为Unity机器学习代理工具包(ML-Agents)的开源项目使得使用模拟和游戏作为智能代理的训练场成为可能。通过易于使用的Python API,可以使用强化学习、模仿学习、神经进化或其他机器学习技术来教授代理。支持各种环境设置和训练情境,可定制的Unity SDK以及内置的模仿学习支持仅是其众多功能之一。
XSStrike
的中文翻译为:XSStrike
The Python programming language's XSStrike project is one of the most popular ones on GitHub and is well-known for its ability to identify and counteract XSS assaults. A fast crawler, an intelligent payload generator, four handwritten parsers, and a fuzzing engine are among its further features.
NeutralTalk
的中文翻译为:中立对话
Using NeutralTalk, you can hone your understanding of multimodal recurrent neural networks. It is an image description-focused Python and NumPy project.
自然语言处理和计算机视觉经常被用于创建图片标题的方法中。该系统具有理解情境并提供照片中显示信息的描述的能力。
NeutralTalk2 可用于找到最新的字幕代码。这个项目比上一个项目更快,因为使用了轻量级且高级的编程语言 Lua 来创建它。
Manim
的翻译为:Manim
Manim是一个用于创建图形化数学教程的工具。它运行在Python 3.7上,并且主要利用编程来生成精确的动画。Manim使用Python以编程方式创建动画,允许完全控制每个动画的执行方式。
TensorFlow项目
与开源机器学习框架一起,TensorFlow项目是受欢迎的开源Python GitHub项目之一。它提供了高性能数值计算的指导,具有可适应的架构和简单的计算部署,适用于多个平台。
地图模型导入器
Using vast maps, the Maps Models Importer imports 3D models. Only a Blender add-on makes up this experimental technology, and 3D content programs like Google Maps are needed to complete the process. Learn how to import models from Google Maps with the help of this project.
in conclusion
In conclusion, Python’s popularity in the developer community is obvious, and GitHub provides an open source platform for engineers to collaborate and develop their capabilities. The most popular open source Python projects on GitHub demonstrate Python’s flexibility in different areas, including deep learning, data mining, and game development. From Google Image downloads to TensorFlow, these projects provide exciting opportunities to practice programming skills, explore new technologies, and collaborate with a large community of engineers. As demand for Python continues to grow, these projects will undoubtedly continue to evolve and inspire new possibilities in programming.
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Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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