Learning Python. 6th Edition (ebook) Wojkowice

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz's popular training course, this updated sixth edition will help you quickly write efficient, high-quality code with Python. It's an ideal way to begin, whether you're new to programming or a professional developer versed in other languages.Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow self-paced tutorial gets you started with Python 3.12 and all other releases in use today. With a pragmatic focus on what you need to know, it also introduces some advanced language features that have become increasingly common in Python code.This book helps you:Explore Python's built-in object types such as strings, lists, dictionaries, and filesCreate and process objects with Python statements, and learn Python's syntax modelUse functions and functional programming to avoid redundancy and maximize reuseOrganize code into larger components with modules and packagesCode robust programs with Python's exception handling and development toolsApply object-oriented programming and classes to make code customizableSurvey advanced Python tools including decorators, descriptors, and metaclassesWrite idiomatic Python code that runs portably across a wide variety of platforms Spis treści: Preface Python This Book This Edition Media Choices Updates and Examples Conventions and Reuse Acknowledgments I. Getting Started 1. A Python Q&A Session Why Do People Use Python? Software Quality Developer Productivity Is Python a Scripting Language? OK, but Whats the Downside? Who Uses Python Today? What Can I Do with Python? Systems Programming GUIs and UIs Internet and Web Scripting Component Integration Database Access Rapid Prototyping Numeric and Scientific Programming And More: AI, Games, Images, QA, Excel, Apps What Are Pythons Technical Strengths? Its Object-Oriented and Functional Its Free and Open Its Portable Its Powerful Its Mixable Its Relatively Easy to Use Its Relatively Easy to Learn Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 2. How Python Runs Programs Introducing the Python Interpreter Program Execution The Programmers View Pythons View Bytecode compilation The Python Virtual Machine (PVM) Performance implications Development implications Execution-Model Variations Python Implementation Alternatives Standalone Executables Future Possibilities Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 3. How You Run Programs Installing Python Interactive Code Starting an Interactive REPL Where to Run: Code Folders What Not to Type: Prompts and Comments Other Python REPLs Running Code Interactively Why the Interactive Prompt? Learning Testing Program Files A First Script Running Files with Command Lines Command-Line Usage Variations Other Ways to Run Files Clicking and Tapping File Icons The IDLE Graphical User Interface Other IDEs for Python Smartphone Apps WebAssembly for Browsers Jupyter Notebooks for Science Ahead-of-Time Compilers for Speed Running Code in Code Importing modules Reloading modules Module attributes: a first look The exec built-in Command-line launchers Other Launch Options Which Option Should I Use? Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers Test Your Knowledge: Part I Exercises II. Objects and Operations 4. Introducing Python Objects The Python Conceptual Hierarchy Why Use Built-in Objects? Pythons Core Object Types Numbers Strings Sequence Operations Immutability Type-Specific Methods Getting Help Other Ways to Code Strings Unicode Strings Lists Sequence Operations Type-Specific Operations Bounds Checking Nesting Comprehensions Dictionaries Mapping Operations Nesting Revisited Missing Keys: if Tests Item Iteration: for Loops Tuples Why Tuples? Files Unicode and Byte Files Other File-Like Tools Other Object Types Sets Booleans and None Types Type Hinting User-Defined Objects And Everything Else Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 5. Numbers and Expressions Numeric Object Basics Numeric Literals Built-in Numeric Tools Python Expression Operators Mixed Operators: Precedence Parentheses Group Subexpressions Mixed Types Are Converted Up Preview: Operator Overloading and Polymorphism Numbers in Action Variables and Basic Expressions Numeric Display Formats Comparison Operators Chained comparisons Floating-point equality Division Operators Floor versus truncation Integer Precision Complex Numbers Hex, Octal, and Binary Bitwise Operations Underscore Separators in Numbers Other Built-in Numeric Tools Other Numeric Objects Decimal Objects Decimal basics Setting decimal precision Fraction Objects Fraction basics Numeric accuracy in fractions and decimals Set Objects Sets in action Immutable constraints and frozen sets Set comprehensions Why sets? Boolean Objects Numeric Extensions Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 6. The Dynamic Typing Interlude The Case of the Missing Declaration Statements Variables, Objects, and References Types Live with Objects, Not Variables Objects Are Garbage-Collected Shared References Shared References and In-Place Changes Shared References and Equality Dynamic Typing Is Everywhere Type Hinting: Optional, Unused, and Why? Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 7. String Fundamentals String Object Basics String Literals Single and Double Quotes Are the Same Escape Sequences Are Special Characters Raw Strings Suppress Escapes Triple Quotes and Multiline Strings Strings in Action Basic Operations Indexing and Slicing Extended slicing: The third limit and slice objects String Conversion Tools Character-code conversions String comparisons Changing Strings Part 1: Sequence Operations String Methods Method Call Syntax All String Methods (Today) Changing Strings, Part 2: String Methods More String Methods: Parsing Text Other Common String Methods String Formatting: The Triathlon String-Formatting Options The String-Formatting Expression Formatting expression basics Formatting expression custom formats Advanced formatting expression examples Dictionary-based formatting expressions The String-Formatting Method Formatting method basics Adding keys, attributes, and offsets Formatting method custom formats Advanced formatting method examples The F-String Formatting Literal F-string formatting basics F-string custom formats Advanced f-string examples And the Winner Is General Type Categories Types Share Operation Sets by Categories Mutable Types Can Be Changed in Place Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 8. Lists and Dictionaries Lists Lists in Action Basic List Operations Indexing and Slicing Changing Lists in Place Index and slice assignments List method calls Sorting lists More List Methods Iteration, Comprehensions, and Unpacking List comprehensions and maps List-literal unpacking Other List Operations Dictionaries Dictionaries in Action Basic Dictionary Operations Changing Dictionaries in Place More Dictionary Methods Other Dictionary Makers Dictionary-literal unpacking Dictionary Comprehensions Key Insertion Ordering Dictionary Union Operator Intermission: Books Database Mapping values to keys Dictionary Usage Tips Using dictionaries to simulate flexible lists: Integer keys Using dictionaries for sparse data structures: Tuple keys Avoiding missing-key errors Nesting in dictionaries Dictionary key/value/item view objects Dictionary views and sets Sorting dictionary keys Dictionary magnitude comparisons Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 9. Tuples, Files, and Everything Else Tuples Tuples in Action Tuple syntax peculiarities: Commas and parentheses Conversions, methods, and immutability Why Lists and Tuples? Records Revisited: Named Tuples Files Opening Files Using Files Files in Action Text and Binary Files: The Short Story Storing Objects with Conversions Storing Objects with pickle Storing Objects with JSON Storing Objects with Other Tools File Context Managers Other File Tools Core Types Review and Summary Object Flexibility References Versus Copies Comparisons, Equality, and Truth Mixed-type comparisons and sorts Dictionary comparisons The Meaning of True and False in Python The None object The bool type Pythons Type Hierarchies Type Objects Other Types in Python Built-in Type Gotchas Assignment Creates References, Not Copies Repetition Adds One Level Deep Beware of Cyclic Data Structures Immutable Types Cant Be Changed in Place Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers Test Your Knowledge: Part II Exercises III. Statements and Syntax 10. Introducing Python Statements The Python Conceptual Hierarchy Revisited Pythons Statements A Tale of Two ifs What Python Adds What Python Removes Parentheses are optional End-of-line is end of statement End of indentation is end of block Why Indentation Syntax? A Few Special Cases Statement rule special cases Block rule special case A Quick Example: Interactive Loops A Simple Interactive Loop Doing Math on User Inputs Handling Errors by Testing Inputs Handling Errors with try Statements Supporting Floating-Point Numbers Nesting Code Three Levels Deep Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 11. Assignments, Expressions, and Prints Assignments Assignment Syntax Forms Basic Assignments Sequence Assignments Advanced sequence-assignment patterns Extended-Unpacking Assignments Extended unpacking in action Boundary cases A useful convenience Application to for loops Multiple-Target Assignments Multiple-target assignment and shared references Augmented Assignments Augmented assignment and shared references Named Assignment Expressions When to use named assignment Variable Name Rules Naming conventions Names have no type, but objects do Expression Statements Expression Statements and In-Place Changes Print Operations The print Function Call format The print function in action Print Stream Redirection The Python hello world program Manual stream redirection Automatic stream redirection Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 12. if and match Selections if Statements General Format Basic Examples Multiple-Choice Selections Handling switch defaults Handling larger actions match Statements Basic match Usage Match versus if live Advanced match Usage Python Syntax Revisited Block Delimiters: Indentation Rules Avoid mixing tabs and spaces Statement Delimiters: Lines and Continuations Special Syntax Cases in Action Truth Values Revisited The if/else Ternary Expression Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 13. while and for Loops while Loops General Format Examples break, continue, pass, and the Loop else General Loop Format pass The ellipsis-literal alternative continue The nested-code alternative break The named-assignment alternative Loop else Why the loop else? for Loops General Format Examples Basic usage Other data types Tuple (sequence) assignment in for loops Extended-unpacking assignment in for loops Nested for loops Loop Coding Techniques Counter Loops: range Sequence Scans: while, range, and for Sequence Shufflers: range and len Skipping Items: range and Slices Changing Lists: range and Comprehensions Parallel Traversals: zip More on zip: size and truncation More zip roles: dictionaries Offsets and Items: enumerate Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 14. Iterations and Comprehensions Iterations The Iteration Protocol The iter and next built-ins The full iteration protocol Manual iteration More on iter and next Other Built-in Iterables Reprise: Dictionaries, range, enumerate, and zip Iterator nesting Functional iterables: map and filter Multiple-pass versus single-pass iterables Standard-library iterables in Python Comprehensions List Comprehension Basics List Comprehensions and Files Extended List Comprehension Syntax Filter clauses: if Nested loops: for Comprehensions Cliff-Hanger Iteration Tools Other Iteration Topics Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 15. The Documentation Interlude Python Documentation Sources # Comments The dir Function Docstrings and __doc__ User-defined docstrings Docstring standards Built-in docstrings Pydoc: The help Function Running help on built-in tools Running help on your own code Pydoc: HTML Reports Using Pydocs browser interface Customizing Pydoc More Pydoc tips Beyond Docstrings: Sphinx The Standard Manuals Web Resources Common Coding Gotchas Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers Test Your Knowledge: Part III Exercises IV. Functions and Generators 16. Function Basics Why Use Functions? Function Coding Overview Basic Function Tools Advanced Function Tools General Function Concepts def Statements return Statements def Executes at Runtime lambda Makes Anonymous Functions A First Example: Definitions and Calls Definition Calls Polymorphism in Python A Second Example: Intersecting Sequences Definition Calls Polymorphism Revisited Segue: Local Variables Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 17. Scopes Python Scopes Basics Scopes Overview Name Resolution: The LEGB Rule Preview: Other Python scopes Scopes Examples The Built-in Scope Redefining built-in names: For better or worse The global Statement Program Design: Minimize Global Variables Program Design: Minimize Cross-File Changes Other Ways to Access Globals Nested Functions and Scopes Nested Scopes Overview Nested Scopes Examples Closures and Factory Functions Arbitrary Scope Nesting The nonlocal Statement nonlocal Basics nonlocal in Action nonlocal Boundary Cases State-Retention Options Nonlocals: Changeable, Per-Call, LEGB Globals: Changeable but Shared Function Attributes: Changeable, Per-Call, Explicit Classes: Changeable, Per-Call, OOP And the Winner Is Scopes and Argument Defaults Loops Require Defaults, Not Scopes Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 18. Arguments Argument-Passing Basics Arguments and Shared References Avoiding Mutable Argument Changes Simulating Output Parameters and Multiple Results Special Argument-Matching Modes Argument Matching Overview Argument Matching Syntax Argument Passing Details Keyword and Default Examples Keywords Defaults Combining keywords and defaults Arbitrary Arguments Examples Definitions: Collecting arguments Calls: Unpacking arguments Why arbitrary arguments? Keyword-Only Arguments Why keyword-only arguments? Positional-Only Arguments Argument Ordering: The Gritty Details Definition Ordering Formal definition Boundary cases Calls Ordering Formal definition Boundary cases Perspective Example: The min Wakeup Call Full Credit Bonus Points The Punch Line Example: Generalized Set Functions Testing the Code Example: Rolling Your Own Print Using Keyword-Only Arguments Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 19. Function Odds and Ends Function Design Concepts Recursive Functions Summation with Recursion Coding Alternatives Loop Statements Versus Recursion Handling Arbitrary Structures Testing with a separate script Recursion versus queues and stacks Cycles, paths, and stack limits More recursion examples Function Tools: Attributes, Annotations, Etc. The First-Class Object Model Function Introspection Function Attributes Function Annotations and Decorations Function decorators alternative: Preview Anonymous Functions: lambda lambda Basics Why Use lambda? Multiway branches: The finale How (Not) to Obfuscate Your Python Code Scopes: lambdas Can Be Nested Too Functional Programming Tools Mapping Functions over Iterables: map Selecting Items in Iterables: filter Combining Items in Iterables: reduce Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 20. Comprehensions and Generations Comprehensions: The Final Act List Comprehensions Review Formal Comprehension Syntax Example: List Comprehensions and Matrixes When not to use list comprehensions: Code obfuscation When to use list comprehensions: Speed, conciseness, etc. Generator Functions and Expressions Generator Functions: yield Versus return State suspension Iteration protocol integration Generator functions in action Why generator functions? Extended generator function protocol: send versus next The yield from extension Generator Expressions: Iterables Meet Comprehensions Why generator expressions? Generator expressions versus map Generator expressions versus filter Generator Functions Versus Generator Expressions Generator Odds and Ends Generators are single-pass iterables Generation in built-ins and classes Comprehensions versus type calls and generators Scopes and comprehension variables Generating infinite (well, indefinite) results Example: Shuffling Sequences Scrambling Sequences Simple functions Generator functions Generator expressions Tester client Permutating Sequences Why generators here: Space, time, and more Example: Emulating zip and map Coding Your Own map Coding Your Own zip and 2.X map Asynchronous Functions: The Short Story Async Basics Running serial tasks with normal blocking calls Running concurrent tasks with await and async def How not to use async functions Running concurrent tasks with as_completed and gather Running concurrent tasks with async with and async for The Async Wrap-Up Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 21. The Benchmarking Interlude Benchmarking with Homegrown Tools Timer Module: Take 1 Timer Module: Take 2 Timing Runner and Script Iteration Results Other Pythons results For more good times: Function calls and map More Module Mods Benchmarking with Pythons timeit Basic timeit Usage API-calls mode Command-line mode Handling multiline statements Other timeit usage modes Timing sort speed Automating timeit Benchmarking Benchmark module Benchmark script Timing individual Pythons Timing multiple Pythons Timing set and dictionary iterations Conclusion: Comparing tools Function Gotchas Local Names Are Detected Statically Defaults and Mutable Objects Functions Without returns Miscellaneous Function Gotchas Enclosing scopes and loop variables Hiding built-ins by assignment Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers Test Your Knowledge: Part IV Exercises V. Modules and Packages 22. Modules: The Big Picture Module Essentials Why Use Modules? Python Program Architecture How to Structure a Program Imports and Attributes Standard-Library Modules How Imports Work Step 1: Find It Step 2: Compile It (Maybe) Step 3: Run It The Module Search Path Search-Path Components Configuring the Search Path The sys.path List Inspecting the module search path Changing the module search path Module File Selection Module sources Selection priorities Path Outliers: Standalones and Packages Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 23. Module Coding Basics Creating Modules Module Filenames Other Kinds of Modules Using Modules The import Statement The from Statement The from * Statement Imports Happen Only Once Initialization code Imports Are Runtime Assignments Changing mutables in modules Cross-file name changes import and from Equivalence Potential Pitfalls of the from Statement When import is required Module Namespaces How Files Generate Namespaces Namespace Dictionaries: __dict__ Attribute Name Qualification Imports Versus Scopes Namespace Nesting Reloading Modules reload Basics reload Example reload Odds and Ends Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 24. Module Packages Using Packages Package Imports Packages and the Module Search Path Creating Packages Basic Package Structure Using the basic package Package __init__.py Files Using the updated package Package __main__.py Files Using the updated package Why Packages? A Tale of Two Systems The Roles of __init__.py Files Package-Relative Imports Relative and Absolute Imports Relative-Import Rationales and Trade-Offs Package-Relative Imports in Action The normal-import warm-up The relative-import adventure The absolute-import solution Namespace Packages Python Import Models Namespace-Package Rationales The Module Search Algorithm Namespace Packages in Action Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 25. Module Odds and Ends Module Design Concepts Data Hiding in Modules Minimizing from * Damage: _X and __all__ Managing Attribute Access: __getattr__ and __dir__ Enabling Language Changes: __future__ Dual-Usage Modes: __name__ and __main__ Example: Unit Tests with __name__ The as Extension for import and from Module Introspection Example: Listing Modules with __dict__ Importing Modules by Name String Running Code Strings Direct Calls: Two Options Example: Transitive Module Reloads A recursive reloader Testing recursive reloads Alternative codings Testing reload variants Module Gotchas Module Name Clashes: Package and Package-Relative Imports Statement Order Matters in Top-Level Code from Copies Names but Doesnt Link from * Can Obscure the Meaning of Variables reload May Not Impact from Imports reload, from, and Interactive Testing Recursive from Imports May Not Work Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers Test Your Knowledge: Part V Exercises VI. Classes and OOP 26. OOP: The Big Picture Why Use Classes? OOP from 30,000 Feet Attribute Inheritance Search Classes and Instances Method Calls Coding Class Trees Operator Overloading OOP Is About Code Reuse Polymorphism and classes Programming by customization Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 27. Class Coding Basics Classes Generate Multiple Instance Objects Class Objects Provide Default Behavior Instance Objects Are Concrete Items A First Example Classes Are Customized by Inheritance A Second Example Classes Are Attributes in Modules Classes Can Intercept Python Operators A Third Example Returning resultsor not Other operator-overloading methods The Worlds Simplest Python Class Classes: Under the Hood Records Revisited: Classes Versus Dictionaries Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 28. A More Realistic Example Step 1: Making Instances Coding Constructors Testing as You Go Using Code Two Ways Step 2: Adding Behavior Methods Coding Methods Step 3: Operator Overloading Providing Print Displays Step 4: Customizing Behavior by Subclassing Coding Subclasses Augmenting Methods: The Bad Way Augmenting Methods: The Good Way Polymorphism in Action Inherit, Customize, and Extend OOP: The Big Idea Step 5: Customizing Constructors, Too OOP Is Simpler Than You May Think Other Ways to Combine Classes: Composites Step 6: Using Introspection Tools Special Class Attributes A Generic Display Tool Instance Versus Class Attributes Name Considerations in Tool Classes Our Classes Final Form Step 7 (Final): Storing Objects in a Database Pickles and Shelves The pickle module The shelve module Storing Objects on a shelve Database Exploring Shelves Interactively Updating Objects on a Shelf Future Directions Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 29. Class Coding Details The class Statement General Syntax and Usage Example: Class Attributes Methods Method Example Other Method-Call Possibilities Inheritance Attribute Tree Construction Inheritance Fine Print Specializing Inherited Methods Class Interface Techniques Abstract Superclasses Preview: Abstract superclasses with library tools Namespaces: The Conclusion Simple Names: Global Unless Assigned Attribute Names: Object Namespaces The Zen of Namespaces: Assignments Classify Names Nested Classes: The LEGB Scopes Rule Revisited Namespace Dictionaries: Review Namespace Links: A Tree Climber Documentation Strings Revisited Classes Versus Modules Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 30. Operator Overloading The Basics Constructors and Expressions: __init__ and __sub__ Common Operator-Overloading Methods Indexing and Slicing: __getitem__ and __setitem__ Intercepting Slices Intercepting Item Assignments But __index__ Means As-Integer Index Iteration: __getitem__ Iterable Objects: __iter__ and __next__ User-Defined Iterables Single versus multiple scans Classes versus generators Multiple Iterators on One Object Classes versus slices Coding Alternative: __iter__ Plus yield Multiple iterators with yield Membership: __contains__, __iter__, and __getitem__ Attribute Access: __getattr__ and __setattr__ Attribute Reference Attribute Assignment and Deletion Other Attribute-Management Tools Emulating Privacy for Instance Attributes: Part 1 String Representation: __repr__ and __str__ Why Two Display Methods? Display Usage Notes Right-Side and In-Place Ops: __radd__ and __iadd__ Right-Side Addition Reusing __add__ in __radd__ Propagating class type In-Place Addition Call Expressions: __call__ Function Interfaces and Callback-Based Code Comparisons: __lt__, __gt__, and Others Boolean Tests: __bool__ and __len__ Object Destruction: __del__ Destructor Usage Notes Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 31. Designing with Classes Python and OOP Polymorphism Means Interfaces, Not Call Signatures OOP and Inheritance: Is-a Relationships OOP and Composition: Has-a Relationships Stream Processors Revisited OOP and Delegation: Like-a Relationships Pseudoprivate Class Attributes Name Mangling Overview Why Use Pseudoprivate Attributes? Method Objects: Bound or Not Bound Methods in Action Classes Are Objects: Generic Object Factories Why Factories? Multiple Inheritance and the MRO How Multiple Inheritance Works How the MRO Works Attribute Conflict Resolution Example: Mix-in Attribute Listers Listing instance attributes with __dict__ Listing inherited attributes with dir Listing attributes per object in class trees Example: Mapping Attributes to Inheritance Sources Other Design-Related Topics Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 32. Class Odds and Ends Extending Built-in Object Types Extending Types by Embedding Extending Types by Subclassing The Python Object Model Classes Are Types Are Classes Some Instances Are More Equal Than Others The Inheritance Bifurcation The Metaclass/Class Dichotomy And One object to Rule Them All Advanced Attribute Tools Slots: Attribute Declarations Slot basics You shouldnt normally use slots Slots and namespace dictionaries Multiple __slot__ lists in superclasses Handling slots and other virtual attributes generically Slot usage rules Example impacts of slots: ListTree and mapattrs What about slots speed? Properties: Attribute Accessors Property basics __getattribute__ and Descriptors: Attribute Implementations Static and Class Methods Why the Special Methods? Plain-Function Methods Static Method Alternatives Using Static and Class Methods Counting Instances with Static Methods Counting Instances with Class Methods Counting instances per class with class methods Decorators and Metaclasses Function Decorator Basics A First Look at User-Defined Function Decorators A First Look at Class Decorators and Metaclasses For More Details The super Function The super Basics The super Details A magic proxy Attribute-fetch algorithm Universal deployment Call-chain anchors Same argument lists Noncalls and operator overloading The super Wrap-Up Class Gotchas Changing Class Attributes Can Have Side Effects Changing Mutable Class Attributes Can Have Side Effects, Too Multiple Inheritance: Order Matters Scopes in Methods and Classes Miscellaneous Class Gotchas Overwrapping-itis Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers Test Your Knowledge: Part VI Exercises VII. Exceptions 33. Exception Basics Why Use Exceptions? Exception Roles Exceptions: The Short Story Default Exception Handler Catching Exceptions Raising Exceptions User-Defined Exceptions Termination Actions Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 34. Exception Coding Details The try Statement try Statement Clauses The except and else Clauses How try statements work Catching many exceptions with a tuple Catching all exceptions with empties and Exception Catching the no-exception case with else Example: Default behavior Example: Catching built-in exceptions The finally Clause Example: Coding termination actions with try/finally Combined try Clauses Combined-clause syntax rules Combining finally and except by nesting Combined-clauses example The raise Statement Raising Exceptions The except as hook Scopes and except as Propagating Exceptions with raise Exception Chaining: raise from The assert Statement Example: Trapping Constraints (but Not Errors!) The with Statement and Context Managers Basic with Usage The Context-Management Protocol Multiple Context Managers The Termination-Handlers Shoot-Out Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 35. Exception Objects Exception Classes Coding Exceptions Classes Why Exception Hierarchies? Built-in Exception Classes Built-in Exception Categories Default Printing and State Custom Print Displays Custom State and Behavior Providing Exception Details Providing Exception Methods Exception Groups: Yet Another Star! Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 36. Exception Odds and Ends Nesting Exception Handlers Example: Control-Flow Nesting Example: Syntactic Nesting Exception Idioms Breaking Out of Multiple Nested Loops: go to Exceptions Arent Always Errors Functions Can Signal Conditions with raise Closing Files and Server Connections Debugging with Outer try Statements Running In-Process Tests More on sys.exc_info The sys.exception alternativeand diss Displaying Errors and Tracebacks Exception Design Tips and Gotchas What Should Be Wrapped Catching Too Much: Avoid Empty except and Exception Catching Too Little: Use Class-Based Categories Core Language Wrap-Up The Python Toolset Development Tools for Larger Projects Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers Test Your Knowledge: Part VII Exercises VIII. Advanced Topics 37. Unicode and Byte Strings Unicode Foundations Character Representations Character Encodings Introducing Python String Tools The str Object The bytes Object The bytearray Object Text and Binary Files Using Text Strings Literals and Basic Properties String Type Conversions Coding Unicode Strings in Python Source-File Encoding Declarations Using Byte Strings Methods Sequence Operations Formatting Other Ways to Make Bytes Mixing String Types The bytearray Object Using Text and Binary Files Text-File Basics Text and Binary Modes Unicode-Text Files Unicode, Bytes, and Other String Tools The re Pattern-Matching Module The struct Binary-Data Module The pickle and json Serialization Modules Filenames in open and Other Filename Tools The Unicode Twilight Zone Dropping the BOM in Python Making BOMs in Text Editors Making BOMs in Python Unicode Normalization: Whither Standard? Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 38. Managed Attributes Why Manage Attributes? Inserting Code to Run on Attribute Access Properties The Basics A First Example Computed Attributes Coding Properties with Decorators Setter and deleter decorators Descriptors The Basics Descriptor method arguments Read-only descriptors A First Example Computed Attributes Using State Information in Descriptors How Properties and Descriptors Relate Descriptors and slots and more __getattr__ and __getattribute__ The Basics Avoiding loops in attribute interception methods A First Example Using __getattribute__ Computed Attributes Using __getattribute__ __getattr__ and __getattribute__ Compared Management Techniques Compared Intercepting Built-in Operation Attributes Revisiting Chapter 28s delegation example Example: Attribute Validations Using Properties to Validate Testing code Using Descriptors to Validate Option 1: Validating with shared descriptor-instance state (badly!) Option 2: Validating with per-client-instance state (correctly) Using __getattr__ to Validate Using __getattribute__ to Validate Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 39. Decorators Whats a Decorator? Managing Calls and Instances Managing Functions and Classes Using and Defining Decorators Why Decorators? The Basics Function Decorator Basics Usage Implementation Supporting method decoration Class Decorator Basics Usage Implementation Supporting multiple instances Decorator Nesting Decorator Arguments Decorators Manage Functions and Classes, Too Coding Function Decorators Tracing Function Calls Decorator State Retention Options State with class-instance attributes State with global variables State with enclosing-scope nonlocals State with function attributes Class Pitfall: Decorating Methods Using nested functions to decorate methods Using descriptors to decorate methods Timing Function Calls Adding Decorator Arguments Coding Class Decorators Singleton Classes Singleton coding alternatives Tracing Object Interfaces The nondecorator approach The class-decorator approach Applying class decorators to built-in types Class Pitfall: Retaining Multiple Instances Example: Private and Public Attributes Implementing Private Attributes Implementation Details I Inheritance versus delegation Decorator arguments State retention and enclosing scopes Using __dict__ and __slots__ (and other virtuals) Generalizing for Public Declarations Implementation Details II Using __X pseudoprivate names Breaking privacy Decorator trade-offs Delegating Built-In Operations Workaround: Coding operator-overloading methods inline Workaround: Coding operator-overloading methods in superclasses Workaround: Generating operator-overloading descriptors Example: Validating Function Arguments The Goal A Basic Range-Testing Decorator for Positional Arguments Generalizing for Keywords and Defaults Implementation Details Function introspection Argument assumptions Matching algorithm Open Issues Invalid calls Arbitrary arguments Decorator nesting Decorator Arguments Versus Function Annotations Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 40. Metaclasses and Inheritance To Metaclass or Not to Metaclass The Downside of Helper Functions Metaclasses Versus Class Decorators: Round 1 The Metaclass Model Classes Are Instances of type Metaclasses Are Subclasses of type Class Statements Call a type Class Statements Can Choose a type Metaclass Method Protocol Coding Metaclasses A Basic Metaclass Customizing Construction and Initialization Other Metaclass Coding Techniques Using simple factory functions Overloading class creation calls with normal classes Managing Classes with Metaclasses and Decorators Adding methods to classes Automatically decorating class methods Inheritance: The Finale Metaclass Versus Superclass Metaclass Inheritance Python Inheritance Algorithm: The Simple Version The descriptors deviation Python Inheritance Algorithm: The Less Simple Version The assignment addendum The super supplement The built-ins bifurcation The Inheritance Wrap-Up Metaclass Methods Metaclass Methods Versus Class Methods Operator Overloading in Metaclass Methods Metaclass Methods Versus Instance Methods Chapter Summary Test Your Knowledge: Quiz Test Your Knowledge: Answers 41. All Good Things The Python Tsunami The Python Sandbox The Python Upside Closing Thoughts Where to Go from Here Encore: Print Your Own Completion Certificate! IX. Appendixes A. Platform Usage Tips Using Python on Windows Using Python on macOS Using Python on Linux Using Python on Android Using Python on iOS Standalone Apps and Executables Etcetera B. Solutions to End-of-Part Exercises Part I, Getting Started Part II, Objects and Operations Part III, Statements and Syntax Part IV, Functions and Generators Part V, Modules and Packages Part VI, Classes and OOP Part VII, Exceptions Index O autorze: Mark Lutz — to znany na całym świecie instruktor Pythona, autor najwcześniejszych oraz najlepiej sprzedających się tekstów poświęconych temu językowi i jedna z najważniejszych postaci w środowisku Pythona. Poza poprzednimi wydaniami tej książki — w tym przetłumaczonej na język polski wersji „Python. Wprowadzenie” (Helion, 2002), napisał także „Programming Python” oraz „Python Pocket Reference”. Osobiście używa języka Python i promuje go od 1992 roku. Książki na temat tego języka zaczął publikować w 1995 roku, zaś szkolenia z Pythona prowadzi od 1997 roku. Do początku roku 2008 przeprowadził ponad dwieście sesji treningowych poświęconych temu językowi.