Since 2015, China has slowly been exposed to Python. Since 2016, Python has become more popular in China, and it can now be regarded as "Python for all".
As we all know, Python is already included in the textbooks for primary school students, and the national second-level computer certificate also requires learning Python!
Because Python is simple and quick to get started with, it is the first choice language for many programmers to get started.
So what development can be achieved after learning Python? (Recommended learning: Python video tutorial)
web development
Douban, Zhihu, Lagou.com, etc. all use Python, web The development of development in China is also very good
Because Python's web development framework is the biggest advantage. If you use Python to build a website, you only need a few lines of code
Network Crawler
A large number of people who are learning Python now are learning crawlers. This is also one of the major advantages of Python. The first person to use Python to do web crawlers was Google.
Why use Python to write a crawler
Cross-platform, with good support for Linux and windows.
Scientific computing, numerical fitting: Numpy, Scipy
Visualization: 2d: Matplotlib (very beautiful drawings), 3d: Mayavi2
Complex network: Networkx
Statistics: Interface with R language: Rpy
Interactive terminal
Artificial intelligence
The development potential and financial prospects of artificial intelligence are not the same Let’s just say, this is something everyone knows, but at present, there are still relatively few jobs in artificial intelligence, and they are all highly educated people. In the future, it will definitely be the direction with the most development potential.
Server Operation and Maintenance
Operation and maintenance are also no strangers. The first group of people who learned Python were those working in operation, maintenance and testing, because Python is very useful to them. Work plays a big role, because using Python scripts for batch file deployment and operation adjustments has become a very good choice on Linux servers.
Data Analysis
On the Internet, you can know a lot of things, and they will analyze and recommend what you want based on what you watch and buy. . For example:
Taobao: It will recommend to you the products you have seen or the products you like that you want to buy.
Headline: Based on the category of the article you read, relevant articles are recommended to you.
Python has a complete ecological environment that is very conducive to data analysis and processing. For example, distributed computing, data visualization, database operations, etc. required for "big data" analysis can all be done through Python. Mature module completed.
For more Python related technical articles, please visit the Python Tutorial column to learn!
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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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