I recently read an article whose main focus was on the Python community. It talked about why Python applications are so ugly?
Although his point of view is correct in some cases, the question he asked "Dear Python, why are you so ugly" is really ridiculous.
Every assumption and comparison he describes seems very stupid, and the person who wrote the article there seems to be completely unimpressed by the design level of blogs and web applications built on Python.
In order to expose the truth, according to what he said, I collected a series of applications implemented in Python, which are definitely carefully designed.
Don’t judge based on my HTML layout, the real content is on the server side - Mahdi Yusuf
This “small” company recently sold for $1 billion. One of the important factors why it is so popular is its elegance. Instagram uses Python extensively.
Another beautiful app, are you sure it’s not written in Python? Wrong, it is written in Python.
Washington Post
News site may not be the most innovative design, but it is not ugly by definition of any word.
EveryBlock
EveryBlock was created by co-author of Django (Adrian Holovaty) , ugly? Not at all.
Mozilla
One of the largest sites using Python so far, Mozillaz has a good reputation in design and innovation
Courtside
This application was written by my team and I for Django Dash, Although I can't come up with any great designs, we can take the time to practice.
Youtube
Douban
(Translator is from China) Everyone knows about Douban. Abei said this: "From a personal preference, python is more concise and suits my taste." I don’t know how much time I have saved by using abbreviation in Python. I have been typing { } ; for more than ten years, and now it’s really hard to look back. "
Do I need to continue to introduce it?
I think Path also uses Python extensively in their web applications. This product has been praised for its design and innovation.
So, those who make statements that websites written in a specific language are ugly do not make any sense. The Python community is vibrant and has a lot of aspirations and interests beyond just the pretty stuff.
There is a saying that designers make websites look beautiful and concise. This is not something that back-end engineers can write in language.
If you are a programmer and want to learn design, you should read this book "Design for Hackers: Reverse Engineering Beauty".

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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