I won’t go into details about Python’s popularity. Everyone is an experienced programming expert, and they must know the most cutting-edge and hottest programming language better than I do.
A few days ago, I read an article "How to use Python to create a Douyin girl finder?", and the number of reads was even more than what was on the headlines. times. This shows the popularity of Python and its ingenuity and wide application. (Recommended learning: Python video tutorial)
It is also the explosion of Python. Many newcomers or programming enthusiasts will ask:
Is Python worth it? Take the time to learn?
How did you learn Python?
You are so busy at work, do you still have time to study another language?
But, to be honest, learning Python will only save you time and solve problems with twice the result with half the effort. For example, if you write a client-side plug-in gadget, don't use Java, C, etc., try Python. It can definitely save you more than half of your effort. Because the same function is implemented, the files of Python code are often only 1/5~1/3 of C, C and Java code.
So, many people learn Python just for convenience or to be curious about what Python is like, and they don’t think about relying on it to get a promotion, a salary increase, and marry Bai Fumei, at least around me. Friends are like this. Some friends learn Python because they want to teach it to their children in a systematic way after they learn it, so as to exercise their thinking and logical abilities from an early age. I find this quite interesting.
Some time ago, an article "Life is short, I use Python" also talked about many advantages of Python, such as powerful functions, simple use, obvious uniqueness of the language, and Massive third-party libraries.
If you want to start learning Python, it is recommended that you start with Python 3 instead of Python2. Don't think that the two are very similar. In fact, there is a huge difference, a cliff-like upgrade. Python3 has huge advantages in speed and asynchronousness. It has also expanded many libraries, and Python2 and 3 are not yet compatible.
If you really want to learn, it is best to systematically understand Python, its entire development and ecology.
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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