The Python Tutorial:Python教程笔记

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The Python Tutorial
linyk3简书:The Python Tutorial:Python教程笔记

Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
Python 是一门易学、功能强大的编程语言。它拥有高级数据结构，以及简单却有效的方法来实现面向对象编程。Python 优雅的语法和动态类型以及它天然的解释能力，使其成为大部分平台的很多领域里一门理想的脚本和快速应用开发语言。

The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python Web site, https://www.python.org/, and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.
Python 解释器及其扩展标准库都可以在Python官网 https://www.python.org/和所有的镜像网站上以源码或编译版本的形式免费获取和自由发布。官网上也提供很多第三方Python 模块，程序和工具以及附加文档和链接。

The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for customizable applications.
Python 解释器很容易通过C/C++（以及其他由C调用的语言）来扩展新的函数和数据类型。所以Python也很适合作为自定义应用的扩展语言。

This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well.
这个教程向读者通俗的介绍Python语言和系统的基本的概念和特征。有一个Python 解释器对于亲自动手做实验是很方便的，但是所有的实验都是独立的，所以这个教程也可以离线阅读。

For a description of standard objects and modules, see The Python Standard Library.The Python Language Reference gives a more formal definition of the language. To write extensions in C or C++, read Extending and Embedding the Python Interpreterand Python/C API Reference Manual. There are also several books covering Python in depth.
对于对象和模块的标准描述，请查看Python 标准库：The Python Standard Library.Python 语言参考:The Python Language Reference 给出了Python语言更加正式的定义。 用C或者C++来写相关扩展的话，请查看： Extending and Embedding the Python Interpreterand Python/C API Reference Manual.

This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. Instead, it introduces many of Python’s most noteworthy features, and will give you a good idea of the language’s flavor and style. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in The Python Standard Library.
这里也有几本书来深度学习Python.
这个教程不会尝试广泛的覆盖每一个特征，或者甚至每一个常用的特征。相反的，它介绍了很多Python最值得注意的特征，并且会提供你一个关于Python语言的韵味和风格好的建议。读完这个教程后，你将能够读和写Python模块和程序，你也能够准备好去学习Pyhton 标准库：The Python Standard Library里描述的Python库模块。

The Glossary is also worth going through.
这个词汇表Glossary也很值得去过一遍。

• 1. Whetting Your Appetite 欢迎你的使用
• 2. Using the Python Interpreter
  • 2.1. Invoking the Interpreter
    • 2.1.1. Argument Passing
    • 2.1.2. Interactive Mode
  • 2.2. The Interpreter and Its Environment
    • 2.2.1. Source Code Encoding
• 3. An Informal Introduction to Python
  • 3.1. Using Python as a Calculator
    • 3.1.1. Numbers
    • 3.1.2. Strings
    • 3.1.3. Lists
  • 3.2. First Steps Towards Programming
• 4. More Control Flow Tools
  • 4.1. if Statements
  • 4.2. for Statements
  • 4.3. The range() Function
  • 4.4. break and continue Statements, and else Clauses on Loops
  • 4.5. pass Statements
  • 4.6. Defining Functions
  • 4.7. More on Defining Functions
    • 4.7.1. Default Argument Values
    • 4.7.2. Keyword Arguments
    • 4.7.3. Arbitrary Argument Lists
    • 4.7.4. Unpacking Argument Lists
    • 4.7.5. Lambda Expressions
    • 4.7.6. Documentation Strings
    • 4.7.7. Function Annotations
  • 4.8. Intermezzo: Coding Style
• 5. Data Structures
  • 5.1. More on Lists
    • 5.1.1. Using Lists as Stacks
    • 5.1.2. Using Lists as Queues
    • 5.1.3. List Comprehensions
    • 5.1.4. Nested List Comprehensions
  • 5.2. The del statement
  • 5.3. Tuples and Sequences
  • 5.4. Sets
  • 5.5. Dictionaries
  • 5.6. Looping Techniques
  • 5.7. More on Conditions
  • 5.8. Comparing Sequences and Other Types
• 6. Modules
  • 6.1. More on Modules
    • 6.1.1. Executing modules as scripts
    • 6.1.2. The Module Search Path
    • 6.1.3. “Compiled” Python files
  • 6.2. Standard Modules
  • 6.3. The dir() Function
  • 6.4. Packages
    • 6.4.1. Importing * From a Package
    • 6.4.2. Intra-package References
    • 6.4.3. Packages in Multiple Directories
• 7. Input and Output
  • 7.1. Fancier Output Formatting
    • 7.1.1. Formatted String Literals
    • 7.1.2. The String format() Method
    • 7.1.3. Manual String Formatting
    • 7.1.4. Old string formatting
  • 7.2. Reading and Writing Files
    • 7.2.1. Methods of File Objects
    • 7.2.2. Saving structured data with json
• 8. Errors and Exceptions
  • 8.1. Syntax Errors
  • 8.2. Exceptions
  • 8.3. Handling Exceptions
  • 8.4. Raising Exceptions
  • 8.5. User-defined Exceptions
  • 8.6. Defining Clean-up Actions
  • 8.7. Predefined Clean-up Actions
• 9. Classes
  • 9.1. A Word About Names and Objects
  • 9.2. Python Scopes and Namespaces
    • 9.2.1. Scopes and Namespaces Example
  • 9.3. A First Look at Classes
    • 9.3.1. Class Definition Syntax
    • 9.3.2. Class Objects
    • 9.3.3. Instance Objects
    • 9.3.4. Method Objects
    • 9.3.5. Class and Instance Variables
  • 9.4. Random Remarks
  • 9.5. Inheritance
    • 9.5.1. Multiple Inheritance
  • 9.6. Private Variables
  • 9.7. Odds and Ends
  • 9.8. Iterators
  • 9.9. Generators
  • 9.10. Generator Expressions
• 10. Brief Tour of the Standard Library
  • 10.1. Operating System Interface
  • 10.2. File Wildcards
  • 10.3. Command Line Arguments
  • 10.4. Error Output Redirection and Program Termination
  • 10.5. String Pattern Matching
  • 10.6. Mathematics
  • 10.7. Internet Access
  • 10.8. Dates and Times
  • 10.9. Data Compression
  • 10.10. Performance Measurement
  • 10.11. Quality Control
  • 10.12. Batteries Included
• 11. Brief Tour of the Standard Library — Part II
  • 11.1. Output Formatting
  • 11.2. Templating
  • 11.3. Working with Binary Data Record Layouts
  • 11.4. Multi-threading
  • 11.5. Logging
  • 11.6. Weak References
  • 11.7. Tools for Working with Lists
  • 11.8. Decimal Floating Point Arithmetic
• 12. Virtual Environments and Packages
  • 12.1. Introduction
  • 12.2. Creating Virtual Environments
  • 12.3. Managing Packages with pip
• 13. What Now?
• 14. Interactive Input Editing and History Substitution
  • 14.1. Tab Completion and History Editing
  • 14.2. Alternatives to the Interactive Interpreter
• 15. Floating Point Arithmetic: Issues and Limitations
  • 15.1. Representation Error
• 16. Appendix
  • 16.1. Interactive Mode
    • 16.1.1. Error Handling
    • 16.1.2. Executable Python Scripts
    • 16.1.3. The Interactive Startup File
    • 16.1.4. The Customization Modules