


Recommended advanced books for senior programmers to learn Python
Life is short, I Use Python!
Using python, side by side with the sun, this article shares with you some introductory books on the Python programming language, many of which are classics. The following books have been carefully read by many editors at the center and then compiled for everyone to study!
Here is the latest Python information and 0-basic introductory tutorial compiled by myself. Beginners and advanced friends are welcome.
1. "Learn Python in One Day and Learn it Well" [Singapore] Written by Jamie Chan; Translated by Wang Lei
2 , Python data analysis and digital operation, by Song Tianlong
3. Python programming case class, by Liu Chunmao, Pei Yulong, and Zhan Nana
4. Learn Python 3.5 from scratch, by Liu Universe by
5, Python Data Science Handbook, [US] Jake VanderPlas (written by Jake VanderPlas; Tao Junjie, translated by Chen Xiaoli)
6, Python crawler development and project practice, Written by Fan Chuanhui
7. Learning Python programming from scratch, Wu Huiru waiting
8. Learning Python web crawler from scratch, written by Luo Panjiang Qian
9. Python web crawler from entry to practice, written by Tang Song and Chen Zhiquan
10. Basics of Python data analysis, [US] Written by Clinton, W., Brownley; translated by Chen Guangxin
11. Python data processing, Zhang Liang, translated by Lu Jiaming
12. Mastering Python web crawlers: core technology, framework and project practice, written by Wei Wei
13. Python High-Performance Programming
14. 21-Day Learning Python
15. Python and Data Mining
16. Python Data Analysis and Mining Practice
17. The Programming Journey of Father and Son - Learning Python with Little Carter
18. Others
I hope you can improve your skills through these books.
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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.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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