Python is very useful; for example, in operation and maintenance, where there is a lot of repetitive work, and management systems, monitoring systems, release systems, etc. need to be built to automate the work, such scenarios are very suitable for using Python . Python can be applied in various fields, mainly because it has strong third-party library support and is open source and runs cross-platform.
A few years ago, Python was only a niche language in China, but from 2011 to now, Python’s Baidu search index has increased by 10 Times, Python's popularity in China has been close to that of Java, and it has great potential to catch up.
Whether an industry is popular or not depends on how many (self) media and training institutions rush in to get crazy about it. If you search for the keyword "python" on Baidu, there will be 5 items about training. Advertisements, while Ruby, PHP, Go and other languages have none. Obviously, Python is a better business. I first came into contact with Python in 2010. At that time, except for Douban and Sohu, it was difficult to see large companies using Python. In the United States, Python has always been very popular, such as Google, Instagram, and Dropbox. , Youtube, Reddit, Quora and other star websites are using Python for core business. It can be seen from Google Trands that it has been on a stable trend. The upward trend in the past two years has been obvious. Obviously, the main contribution comes from China.
Is python useful? Of course it works! ! ! What are the uses of ? Let's look down.
The uses and applications of python
Web application development
Server-side programming, with rich web development frameworks such as Django and TurboGears, quickly completes the development of a website and web services. Typical examples include domestic Douban, Guoke.com, etc.; foreign Google, Dropbox, etc.
System network operation and maintenance
In the operation and maintenance work, there is a lot of repetitive work Places need to build management systems, monitoring systems, publishing systems, etc. to automate work and improve work efficiency. Python is a very suitable language for such scenarios.
Science and Number Computing
Python is widely used in scientific and digital computing, such as bioinformatics, physics, architecture, geographic information systems, image visualization analysis, life sciences, etc. Commonly used are numpy, SciPy, Biopython, SunPy, etc.
3D Game Development
Python has a good 3D rendering library and game development framework, there are many using Python Developed games such as Disney's Toontown and Blade of Darkness. Commonly used PyGame, Pykyra, etc. and a PyWeek competition
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Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

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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