Python (UK pronunciation: /ˈpaɪθən/ US pronunciation: /ˈpaɪθɑːn/) is a widely used interpreted, high-level programming, general-purpose programming language, created by Guido Van Rossu Created by Tom, the first edition was released in 1991. It can be regarded as an improvement of LISP (adding some advantages of other programming languages, such as object-oriented).
Python's design philosophy emphasizes code readability and concise syntax (especially the use of space indentation to divide code blocks instead of using braces or keywords).
Compared to C or Java, Python allows developers to express ideas with less code. Whether it is a small or large program, the language attempts to make the structure of the program clear and unambiguous.
Like dynamically typed programming languages such as Scheme, Ruby, Perl, and Tcl, Python has a dynamic type system and garbage collection functions, can automatically manage memory usage, and supports multiple programming paradigms, including object-oriented and imperative. , functional and procedural programming. It has a huge and extensive standard library.
The Python interpreter itself can run on almost all operating systems. One of Python's interpreters, CPython, is written in C and is a community-driven free software currently managed by the Python Software Foundation.
Python is a completely object-oriented language. Functions, modules, numbers, and strings are all objects. And it fully supports inheritance, overloading, derivation, and multiple inheritance, which is beneficial to enhance the reusability of source code. Python supports overloaded operators, so Python also supports generic design. Compared with Lisp, a traditional functional programming language, Python only provides limited support for functional programming. There are two standard libraries (functools, itertools) that provide functional programming tools similar to those in Haskell and Standard ML.
Although Python may be roughly classified as a "scripting language", it is actually widely used by some large-scale software development projects such as Zope, Mnet and BitTorrent, and Google.
Supporters of Python prefer to call it a high-end dynamic programming language. The reason is that "scripting language" generally refers to a language that only performs simple programming tasks, such as shell script, VBScript, etc., which can only handle simple tasks. The programming language is not comparable to Python.
Python itself is designed to be extensible. Not all features and functionality are integrated into the language core. Python provides a rich API and tools so that programmers can easily use C, C, and Cython to write extension modules. The Python compiler itself can also be integrated into other programs that require a scripting language.
Therefore, many people use Python as a "glue language". Use Python to integrate and encapsulate programs written in other languages. Many projects within Google, such as the Google Application Service Engine, use C to write parts with extremely high performance requirements, and then use Python or Java/Go to call the corresponding modules.
Alex Martelli, author of "Python Technical Manual" said: "It's hard to say, but in 2004, Python was already used internally at Google, and Google recruited many Python experts, but here They have decided to use Python before. Their purpose is to use Python as much as possible, and switch to C when necessary; use C when controlling hardware, and use Python during rapid development."
The design philosophy of Python is " Elegant, clear, simple. The philosophy of Python developers is "use one way, preferably only one way to do one thing", so it is very different from other languages that have obvious personal styles. When designing the Python language, if faced with multiple choices, Python developers will generally reject fancy syntax and choose syntax that clearly has no or little ambiguity. These guidelines are called "Python Maxims". Run import this within the Python interpreter to get the complete list.
Related recommendations: "Python Tutorial"
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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.

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