Brett Slatkin
Effective Python
125 Specific Ways to Write Better Python
Paperback Engels 2025 3e druk 9780138172183Levertijd ± 8 werkdagen
Gratis verzonden
Samenvatting
Master the art of Python programming with 125 actionable best practices to write more efficient, readable, and maintainable code.
Python is a versatile and powerful language, but leveraging its full potential requires more than just knowing the syntax. Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition is your comprehensive guide to mastering Python's unique strengths and avoiding its hidden pitfalls. This updated edition builds on the acclaimed second edition, expanding from 90 to 125 best practices that are essential for writing high-quality Python code.
Drawing on years of experience at Google, Brett Slatkin offers clear, concise, and practical advice for both new and experienced Python developers. Each item in the book provides insight into the "Pythonic" way of programming, helping you understand how to write code that is not only effective but also elegant and maintainable. Whether you're building web applications, analyzing data, writing automation scripts, or training AI models, this book will equip you with the skills to make a significant impact using Python.
Key Features of the 3rd Edition:
-Expanded Content: Now with 125 actionable guidelines, including 35 entirely new items.
-Updated Best Practices: Reflects the latest features in Python releases up to version 3.13.
-New Chapters: Additional chapters on how to build robust programs that achieve high performance.
-Advanced Topics: In-depth coverage of creating C-extension modules and interfacing with native shared libraries.
-Practical Examples: Realistic code examples that illustrate each best practice.
Specificaties
ISBN13:9780138172183
Taal:Engels
Bindwijze:paperback
Aantal pagina's:608
Uitgever:Pearson Education
Druk:3
Verschijningsdatum:8-1-2025
Hoofdrubriek:IT-management / ICT
Lezersrecensies
Wees de eerste die een lezersrecensie schrijft!
Inhoudsopgave
Chapter 1: Pythonic Thinking
Item 1: Know Which Version of Python You’re Using
Item 2: Follow the PEP 8 Style Guide
Item 3: Never Expect Python to Detect Errors at Compile Time
Item 4: Write Helper Functions Instead of Complex Expressions
Item 5: Prefer Multiple-Assignment Unpacking Over Indexing
Item 6: Always Surround Single-Element Tuples with Parentheses
Item 7: Consider Conditional Expressions for Simple Inline Logic
Item 8: Prevent Repetition with Assignment Expressions
Item 9: Consider match for Destructuring in Flow Control; Avoid When if Statements Are Sufficient
Chapter 2: Strings and Slicing
Item 10: Know the Differences Between bytes and str
Item 11: Prefer Interpolated F-Strings over C-Style Format Strings and str.format
Item 12: Understand the Difference Between repr and str when Printing Objects
Item 13: Prefer Explicit String Concatenation over Implicit, Especially in Lists
Item 14: Know How to Slice Sequences
Item 15: Avoid Striding and Slicing in a Single Expression
Item 16: Prefer Catch-All Unpacking Over Slicing
Chapter 3: Loops and Iterators
Item 17: Prefer enumerate over range
Item 18: Use zip to Process Iterators in Parallel
Item 19: Avoid else Blocks After for and while Loops
Item 20: Never Use for Loop Variables After the Loop Ends
Item 21: Be Defensive when Iterating over Arguments
Item 22: Never Modify Containers While Iterating over Them; Use Copies or Caches Instead
Item 23: Pass Iterators to any and all for Efficient Short-Circuiting Logic
Item 24: Consider itertools for Working with Iterators and Generators
Chapter 4: Dictionaries
Item 25: Be Cautious when Relying on Dictionary Insertion Ordering
Item 26: Prefer get over in and KeyError to Handle Missing Dictionary Keys
Item 27: Prefer defaultdict over setdefault to Handle Missing Items in Internal State
Item 28: Know How to Construct Key-Dependent Default Values with __missing__
Item 29: Compose Classes Instead of Deeply Nesting Dictionaries, Lists, and Tuples
Chapter 5: Functions
Item 30: Know That Function Arguments Can Be Mutated
Item 31: Return Dedicated Result Objects Instead of Requiring Function Callers to Unpack More Than Three Variables
Item 32: Prefer Raising Exceptions to Returning None
Item 33: Know How Closures Interact with Variable Scope and nonlocal
Item 34: Reduce Visual Noise with Variable Positional Arguments
Item 35: Provide Optional Behavior with Keyword Arguments
Item 36: Use None and Docstrings to Specify Dynamic Default Arguments
Item 37: Enforce Clarity with Keyword-Only and Positional-Only Arguments
Item 38: Define Function Decorators with functools.wraps
Item 39: Prefer functools.partial over lambda Expressions for Glue Functions
Chapter 6: Comprehensions and Generators
Item 40: Use Comprehensions Instead of map and filter
Item 41: Avoid More Than Two Control Subexpressions in Comprehensions
Item 42: Reduce Repetition in Comprehensions with Assignment Expressions
Item 43: Consider Generators Instead of Returning Lists
Item 44: Consider Generator Expressions for Large List Comprehensions
Item 45: Compose Multiple Generators with yield from
Item 46: Pass Iterators into Generators as Arguments Instead of Calling the send Method
Item 47: Manage Iterative State Transitions with a Class Instead of the Generator throw Method
Chapter 7: Classes and Interfaces
Item 48: Accept Functions Instead of Classes for Simple Interfaces
Item 49: Prefer Object-Oriented Polymorphism over Functions with isinstance Checks
Item 50: Consider functools.singledispatch for Functional-Style Programming Instead of Object-Oriented Polymorphism
Item 51: Prefer dataclasses for Defining Lightweight Classes
Item 52: Use @classmethod Polymorphism to Construct Objects Generically
Item 53: Initialize Parent Classes with super
Item 1: Know Which Version of Python You’re Using
Item 2: Follow the PEP 8 Style Guide
Item 3: Never Expect Python to Detect Errors at Compile Time
Item 4: Write Helper Functions Instead of Complex Expressions
Item 5: Prefer Multiple-Assignment Unpacking Over Indexing
Item 6: Always Surround Single-Element Tuples with Parentheses
Item 7: Consider Conditional Expressions for Simple Inline Logic
Item 8: Prevent Repetition with Assignment Expressions
Item 9: Consider match for Destructuring in Flow Control; Avoid When if Statements Are Sufficient
Chapter 2: Strings and Slicing
Item 10: Know the Differences Between bytes and str
Item 11: Prefer Interpolated F-Strings over C-Style Format Strings and str.format
Item 12: Understand the Difference Between repr and str when Printing Objects
Item 13: Prefer Explicit String Concatenation over Implicit, Especially in Lists
Item 14: Know How to Slice Sequences
Item 15: Avoid Striding and Slicing in a Single Expression
Item 16: Prefer Catch-All Unpacking Over Slicing
Chapter 3: Loops and Iterators
Item 17: Prefer enumerate over range
Item 18: Use zip to Process Iterators in Parallel
Item 19: Avoid else Blocks After for and while Loops
Item 20: Never Use for Loop Variables After the Loop Ends
Item 21: Be Defensive when Iterating over Arguments
Item 22: Never Modify Containers While Iterating over Them; Use Copies or Caches Instead
Item 23: Pass Iterators to any and all for Efficient Short-Circuiting Logic
Item 24: Consider itertools for Working with Iterators and Generators
Chapter 4: Dictionaries
Item 25: Be Cautious when Relying on Dictionary Insertion Ordering
Item 26: Prefer get over in and KeyError to Handle Missing Dictionary Keys
Item 27: Prefer defaultdict over setdefault to Handle Missing Items in Internal State
Item 28: Know How to Construct Key-Dependent Default Values with __missing__
Item 29: Compose Classes Instead of Deeply Nesting Dictionaries, Lists, and Tuples
Chapter 5: Functions
Item 30: Know That Function Arguments Can Be Mutated
Item 31: Return Dedicated Result Objects Instead of Requiring Function Callers to Unpack More Than Three Variables
Item 32: Prefer Raising Exceptions to Returning None
Item 33: Know How Closures Interact with Variable Scope and nonlocal
Item 34: Reduce Visual Noise with Variable Positional Arguments
Item 35: Provide Optional Behavior with Keyword Arguments
Item 36: Use None and Docstrings to Specify Dynamic Default Arguments
Item 37: Enforce Clarity with Keyword-Only and Positional-Only Arguments
Item 38: Define Function Decorators with functools.wraps
Item 39: Prefer functools.partial over lambda Expressions for Glue Functions
Chapter 6: Comprehensions and Generators
Item 40: Use Comprehensions Instead of map and filter
Item 41: Avoid More Than Two Control Subexpressions in Comprehensions
Item 42: Reduce Repetition in Comprehensions with Assignment Expressions
Item 43: Consider Generators Instead of Returning Lists
Item 44: Consider Generator Expressions for Large List Comprehensions
Item 45: Compose Multiple Generators with yield from
Item 46: Pass Iterators into Generators as Arguments Instead of Calling the send Method
Item 47: Manage Iterative State Transitions with a Class Instead of the Generator throw Method
Chapter 7: Classes and Interfaces
Item 48: Accept Functions Instead of Classes for Simple Interfaces
Item 49: Prefer Object-Oriented Polymorphism over Functions with isinstance Checks
Item 50: Consider functools.singledispatch for Functional-Style Programming Instead of Object-Oriented Polymorphism
Item 51: Prefer dataclasses for Defining Lightweight Classes
Item 52: Use @classmethod Polymorphism to Construct Objects Generically
Item 53: Initialize Parent Classes with super
Item 54: Consider Composing Functionality with Mix-in Classes
Item 55: Prefer Public Attributes over Private Ones
Item 56: Prefer dataclasses for Creating Immutable Objects
Item 57: Inherit from collections.abc Classes for Custom Container Types
Chapter 8: Metaclasses and Attributes
Item 58: Use Plain Attributes Instead of Setter and Getter Methods
Item 59: Consider @property Instead of Refactoring Attributes
Item 60: Use Descriptors for Reusable @property Methods
Item 61: Use __getattr__, __getattribute__, and __setattr__ for Lazy Attributes
Item 62: Validate Subclasses with __init_subclass__
Item 63: Register Class Existence with __init_subclass__
Item 64: Annotate Class Attributes with __set_name__
Item 65: Consider Class Body Definition Order to Establish Relationships Between Attributes
Item 66: Prefer Class Decorators over Metaclasses for Composable Class Extensions
Chapter 9: Concurrency and Parallelism
Item 67: Use subprocess to Manage Child Processes
Item 68: Use Threads for Blocking I/O; Avoid for Parallelism
Item 69: Use Lock to Prevent Data Races in Threads
Item 70: Use Queue to Coordinate Work Between Threads
Item 71: Know How to Recognize When Concurrency Is Necessary
Item 72: Avoid Creating New Thread Instances for On-demand Fan-out
Item 73: Understand How Using Queue for Concurrency Requires Refactoring
Item 74: Consider ThreadPoolExecutor When Threads Are Necessary for Concurrency
Item 75: Achieve Highly Concurrent I/O with Coroutines
Item 76: Know How to Port Threaded I/O to asyncio
Item 77: Mix Threads and Coroutines to Ease the Transition to asyncio
Item 78: Maximize Responsiveness of asyncio Event Loops with async-friendly Worker Threads
Item 79: Consider concurrent.futures for True Parallelism
Chapter 10: Robustness
Item 80: Take Advantage of Each Block in try/except/else/finally
Item 81: assert Internal Assumptions and raise Missed Expectations
Item 82: Consider contextlib and with Statements for Reusable try/finally Behavior
Item 83: Always Make try Blocks as Short as Possible
Item 84: Beware of Exception Variables Disappearing
Item 85: Beware of Catching the Exception Class
Item 86: Understand the Difference Between Exception and BaseException
Item 87: Use traceback for Enhanced Exception Reporting
Item 88: Consider Explicitly Chaining Exceptions to Clarify Tracebacks
Item 89: Always Pass Resources into Generators and Have Callers Clean Them Up Outside
Item 90: Never Set __debug__ to False
Item 91: Avoid exec and eval Unless You’re Building a Developer Tool
Chapter 11: Performance
Item 92: Profile Before Optimizing
Item 93: Optimize Performance-Critical Code Using timeit Microbenchmarks
Item 94: Know When and How to Replace Python with Another Programming Language
Item 95: Consider ctypes to Rapidly Integrate with Native Libraries
Item 96: Consider Extension Modules to Maximize Performance and Ergonomics
Item 97: Rely on Precompiled Bytecode and File System Caching to Improve Startup Time
Item 98: Lazy-Load Modules with Dynamic Imports to Reduce Startup Time
Item 99: Consider memoryview and bytearray for Zero-Copy Interactions with bytes
Chapter 12: Data Structures & Algorithms
Item 100: Sort by Complex Criteria Using the key Parameter
Item 101: Know the Difference Between sort and sorted
Item 102: Consider Searching Sorted Sequences with bisect
Item 103: Prefer deque for Producer-Consumer Queues
Item 104: Know How to Use heapq for Priority Queues
Item 105: Use datetime Instead of time for Local Clocks
Item 106: Use decimal When Precision Is Paramount
Item 107: Make pickle Serialization Maintainable with copyreg
Chapter 13: Testing and Debugging
Item 108: Verify Related Behaviors in TestCase Subclasses
Item 109: Prefer Integration Tests over Unit Tests
Item 110: Isolate Tests From Each Other with setUp, tearDown, setUpModule, and tearDownModule
Item 111: Use Mocks to Test Code with Complex Dependencies
Item 112: Encapsulate Dependencies to Facilitate Mocking and Testing
Item 113: Use assertAlmostEqual to Control Precision in Floating Point Tests
Item 114: Consider Interactive Debugging with pdb
Item 115: Use tracemalloc to Understand Memory Usage and Leaks
Chapter 14: Collaboration
Item 116: Know Where to Find Community-Built Modules
Item 117: Use Virtual Environments for Isolated and Reproducible Dependencies
Item 118: Write Docstrings for Every Function, Class, and Module
Item 119: Use Packages to Organize Modules and Provide Stable APIs
Item 120: Consider Module-Scoped Code to Configure Deployment Environments
Item 121: Define a Root Exception to Insulate Callers from APIs
Item 122: Know How to Break Circular Dependencies
Item 123: Consider warnings to Refactor and Migrate Usage
Item 124: Consider Static Analysis via typing to Obviate Bugs
Item 125: Prefer Open Source Projects for Bundling Python Programs over zipimport and zipapp
Item 55: Prefer Public Attributes over Private Ones
Item 56: Prefer dataclasses for Creating Immutable Objects
Item 57: Inherit from collections.abc Classes for Custom Container Types
Chapter 8: Metaclasses and Attributes
Item 58: Use Plain Attributes Instead of Setter and Getter Methods
Item 59: Consider @property Instead of Refactoring Attributes
Item 60: Use Descriptors for Reusable @property Methods
Item 61: Use __getattr__, __getattribute__, and __setattr__ for Lazy Attributes
Item 62: Validate Subclasses with __init_subclass__
Item 63: Register Class Existence with __init_subclass__
Item 64: Annotate Class Attributes with __set_name__
Item 65: Consider Class Body Definition Order to Establish Relationships Between Attributes
Item 66: Prefer Class Decorators over Metaclasses for Composable Class Extensions
Chapter 9: Concurrency and Parallelism
Item 67: Use subprocess to Manage Child Processes
Item 68: Use Threads for Blocking I/O; Avoid for Parallelism
Item 69: Use Lock to Prevent Data Races in Threads
Item 70: Use Queue to Coordinate Work Between Threads
Item 71: Know How to Recognize When Concurrency Is Necessary
Item 72: Avoid Creating New Thread Instances for On-demand Fan-out
Item 73: Understand How Using Queue for Concurrency Requires Refactoring
Item 74: Consider ThreadPoolExecutor When Threads Are Necessary for Concurrency
Item 75: Achieve Highly Concurrent I/O with Coroutines
Item 76: Know How to Port Threaded I/O to asyncio
Item 77: Mix Threads and Coroutines to Ease the Transition to asyncio
Item 78: Maximize Responsiveness of asyncio Event Loops with async-friendly Worker Threads
Item 79: Consider concurrent.futures for True Parallelism
Chapter 10: Robustness
Item 80: Take Advantage of Each Block in try/except/else/finally
Item 81: assert Internal Assumptions and raise Missed Expectations
Item 82: Consider contextlib and with Statements for Reusable try/finally Behavior
Item 83: Always Make try Blocks as Short as Possible
Item 84: Beware of Exception Variables Disappearing
Item 85: Beware of Catching the Exception Class
Item 86: Understand the Difference Between Exception and BaseException
Item 87: Use traceback for Enhanced Exception Reporting
Item 88: Consider Explicitly Chaining Exceptions to Clarify Tracebacks
Item 89: Always Pass Resources into Generators and Have Callers Clean Them Up Outside
Item 90: Never Set __debug__ to False
Item 91: Avoid exec and eval Unless You’re Building a Developer Tool
Chapter 11: Performance
Item 92: Profile Before Optimizing
Item 93: Optimize Performance-Critical Code Using timeit Microbenchmarks
Item 94: Know When and How to Replace Python with Another Programming Language
Item 95: Consider ctypes to Rapidly Integrate with Native Libraries
Item 96: Consider Extension Modules to Maximize Performance and Ergonomics
Item 97: Rely on Precompiled Bytecode and File System Caching to Improve Startup Time
Item 98: Lazy-Load Modules with Dynamic Imports to Reduce Startup Time
Item 99: Consider memoryview and bytearray for Zero-Copy Interactions with bytes
Chapter 12: Data Structures & Algorithms
Item 100: Sort by Complex Criteria Using the key Parameter
Item 101: Know the Difference Between sort and sorted
Item 102: Consider Searching Sorted Sequences with bisect
Item 103: Prefer deque for Producer-Consumer Queues
Item 104: Know How to Use heapq for Priority Queues
Item 105: Use datetime Instead of time for Local Clocks
Item 106: Use decimal When Precision Is Paramount
Item 107: Make pickle Serialization Maintainable with copyreg
Chapter 13: Testing and Debugging
Item 108: Verify Related Behaviors in TestCase Subclasses
Item 109: Prefer Integration Tests over Unit Tests
Item 110: Isolate Tests From Each Other with setUp, tearDown, setUpModule, and tearDownModule
Item 111: Use Mocks to Test Code with Complex Dependencies
Item 112: Encapsulate Dependencies to Facilitate Mocking and Testing
Item 113: Use assertAlmostEqual to Control Precision in Floating Point Tests
Item 114: Consider Interactive Debugging with pdb
Item 115: Use tracemalloc to Understand Memory Usage and Leaks
Chapter 14: Collaboration
Item 116: Know Where to Find Community-Built Modules
Item 117: Use Virtual Environments for Isolated and Reproducible Dependencies
Item 118: Write Docstrings for Every Function, Class, and Module
Item 119: Use Packages to Organize Modules and Provide Stable APIs
Item 120: Consider Module-Scoped Code to Configure Deployment Environments
Item 121: Define a Root Exception to Insulate Callers from APIs
Item 122: Know How to Break Circular Dependencies
Item 123: Consider warnings to Refactor and Migrate Usage
Item 124: Consider Static Analysis via typing to Obviate Bugs
Item 125: Prefer Open Source Projects for Bundling Python Programs over zipimport and zipapp
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