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The Python Tutorial
linyk3簡(jiǎn)書(shū):The Python Tutorial:Python教程筆記

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 是一門(mén)易學(xué)、功能強(qiáng)大的編程語(yǔ)言。它擁有高級(jí)數(shù)據(jù)結(jié)構(gòu)，以及簡(jiǎn)單卻有效的方法來(lái)實(shí)現(xiàn)面向?qū)ο缶幊獭ython 優(yōu)雅的語(yǔ)法和動(dòng)態(tài)類型以及它天然的解釋能力，使其成為大部分平臺(tái)的很多領(lǐng)域里一門(mén)理想的腳本和快速應(yīng)用開(kāi)發(fā)語(yǔ)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 解釋器及其擴(kuò)展標(biāo)準(zhǔn)庫(kù)都可以在Python官網(wǎng) https://www.python.org/和所有的鏡像網(wǎng)站上以源碼或編譯版本的形式免費(fèi)獲取和自由發(fā)布。官網(wǎng)上也提供很多第三方Python 模塊，程序和工具以及附加文檔和鏈接。

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 解釋器很容易通過(guò)C/C++（以及其他由C調(diào)用的語(yǔ)言）來(lái)擴(kuò)展新的函數(shù)和數(shù)據(jù)類型。所以Python也很適合作為自定義應(yīng)用的擴(kuò)展語(yǔ)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.
這個(gè)教程向讀者通俗的介紹Python語(yǔ)言和系統(tǒng)的基本的概念和特征。有一個(gè)Python 解釋器對(duì)于親自動(dòng)手做實(shí)驗(yàn)是很方便的，但是所有的實(shí)驗(yàn)都是獨(dú)立的，所以這個(gè)教程也可以離線閱讀。

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.
對(duì)于對(duì)象和模塊的標(biāo)準(zhǔn)描述，請(qǐng)查看Python 標(biāo)準(zhǔn)庫(kù)：The Python Standard Library.Python 語(yǔ)言參考:The Python Language Reference 給出了Python語(yǔ)言更加正式的定義。 用C或者C++來(lái)寫(xiě)相關(guān)擴(kuò)展的話，請(qǐng)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.
這里也有幾本書(shū)來(lái)深度學(xué)習(xí)Python.
這個(gè)教程不會(huì)嘗試廣泛的覆蓋每一個(gè)特征，或者甚至每一個(gè)常用的特征。相反的，它介紹了很多Python最值得注意的特征，并且會(huì)提供你一個(gè)關(guān)于Python語(yǔ)言的韻味和風(fēng)格好的建議。讀完這個(gè)教程后，你將能夠讀和寫(xiě)Python模塊和程序，你也能夠準(zhǔn)備好去學(xué)習(xí)Pyhton 標(biāo)準(zhǔn)庫(kù)：The Python Standard Library里描述的Python庫(kù)模塊。

The Glossary is also worth going through.
這個(gè)詞匯表Glossary也很值得去過(guò)一遍。

• 1. Whetting Your Appetite 歡迎你的使用
• 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