Even if you use Python related tools and knowledge to complete the implementation of the front-end page, to the writing of the back-end code, and then to the management of the database, one person can handle everything about a company website and truly realize full-stack development.
Full-stack is just a concept, and it can be divided into many categories. A true full-stack engineer covers web development, DBA, crawler, testing, operation and maintenance. What should you learn? It's quite a huge amount. What you need to learn in terms of web development: front-end knowledge including HTML5 CSS3 JS Jquery Ajax. The back-end needs to be at least proficient in using Django and tornado, and of course flask is better. (Recommended learning: Python video tutorial)
Python full-stack engineers are a relatively popular type of talent. The Python language itself has full-end development capabilities, so the full range of Python There are also many stack engineers.
For Python full-stack engineers, they need to master the following knowledge structure:
First, the basic syntax of the Python language. It is not difficult to master the basic syntax of the Python language. It can even be said to be very simple. Because Python is a scripting language, the syntax is relatively straightforward.
Second, Python is used for web development.
Python is one of the three traditional solutions for web development (also including Java and PHP). Web development is something that full-stack engineers must master. To do web development with Python, you need to learn the corresponding framework, such as Django.
Third, Python does data analysis. Data analysis in Python is a relatively common application scenario. To do data analysis in Python, you need to learn the corresponding algorithm and implementation process. Libraries that will be used include Numpy, matplotlib, etc.
Fourth, Python is used as a crawler. Python crawler application is a very common application, and there are a large number of cases for reference.
Fifth, Python is used for machine learning applications. Python is currently very commonly used in the field of machine learning (artificial intelligence related), so it is necessary for Python full-stack engineers to master machine learning-related content. Algorithms that need to be mastered include decision trees, naive Bayes, regression, kNN classification, etc. At the same time, basic knowledge of linear algebra and probability theory is required.
Of course Python also has its shortcomings, that is, it is slower, slower than C, C, and Java. This is a limitation because in many fields there are extreme requirements for the running speed of the language. For example, if you take the missile interception program, every second counts. Obviously python will be passed in the first step. But for other areas, such as the calculation of bridge parameters, I believe there is not much difference between 20 minutes and 18 minutes.
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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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