Fluent Python
Clear, Concise, and Effective Programming
by Luciano Ramalho
2nd Edition
Publisher: O'Reilly Media
Book Details
| Print ISBN | 9781492056355 |
| eText ISBN | 9781492056300 |
| Publisher | O'Reilly Media |
| Publishing Year | 2022 |
| Edition | 2nd Edition |
| Language | English |
| Pages | 1012 |
Many developers write Python code that mimics design patterns from other programming languages, completely missing out on the unique capabilities that make the language truly powerful and elegant. Fluent Python 2nd Edition addresses this common gap directly by showing programmers how to write idiomatic, clean, and highly efficient code. In today's rapidly evolving software engineering landscape, understanding the underlying mechanics of the language is absolutely essential for building scalable, high-performance applications. This comprehensive guide helps professional developers transition from basic syntax proficiency to true architectural mastery, unlocking the full potential of the runtime environment. By focusing on the core design philosophies of the language, the book empowers readers to build robust software systems that are both maintainable and exceptionally fast.
Author Luciano Ramalho brings decades of professional consulting and training experience to this deep dive, structuring the extensive material into five distinct thematic sections. The text covers fundamental data structures, functions as first-class objects, object-oriented idioms, control flow, and advanced metaprogramming. Rather than just presenting basic syntax rules, the author explains the underlying rationale behind the design of the language, offering a profound perspective on how different features interact under the hood. Readers will explore sequences, dictionaries, sets, type hints, object references, and the complex intricacies of modern concurrency. This rigorous approach ensures that developers do not just memorize commands but actually learn to think natively in the language, leading to cleaner codebases and fewer runtime errors.
Designed specifically for intermediate and advanced programmers, Fluent Python 2nd Edition has been widely adopted by software engineers, data scientists, and systems architects seeking to elevate their technical capabilities. This updated version incorporates major modern updates, including comprehensive coverage of static typing, structural protocols, and pattern matching. The pedagogical design emphasizes hands-on learning with practical code examples, detailed explanations, and clear architectural diagrams. For those who prefer digital learning, the Fluent Python 2nd Edition PDF offers a flexible way to study these advanced concepts on any device. By mastering these techniques, readers will write shorter, faster, and more readable code that leverages the best ideas of the modern ecosystem.
Table of Contents
Chapter 1: The Python Data Model
- • A Pythonic Card Deck
- • How Special Methods Are Used
- • Emulating Numeric Types
- • String Representation
- • Boolean Value of a Custom Type
- • Collection API
- • Overview of Special Methods
- • Why len Is Not a Method
Chapter 2: An Array of Sequences
- • Overview of Built-In Sequences
- • List Comprehensions and Generator Expressions
- • Tuples Are Not Just Immutable Lists
- • Unpacking Sequences and Iterables
- • Pattern Matching with Sequences
- • Slicing
- • Using + and * with Sequences
- • Augmented Assignment with Sequences
- • list.sort Versus the sorted Built-In
- • When a List Is Not the Answer
Chapter 3: Dictionaries and Sets
- • Modern dict Syntax
- • Standard API of Mapping Types
- • Automatic Handling of Missing Keys
- • Variations of dict
- • Subclassing UserDict Instead of dict
- • Immutable Mappings
- • Dictionary Views
- • Set Theory
- • Set Operations
Chapter 4: Unicode Text Versus Bytes
- • Character Issues
- • Byte Essentials
- • Basic Encoders/Decoders
- • Understanding Encode/Decode Problems
- • Handling Text Files
- • Normalizing Unicode for Reliable Comparisons
- • Sorting Unicode Text
- • The Unicode Database
- • Dual-Mode str and bytes APIs
Chapter 5: Data Class Builders
- • Overview of Data Class Builders
- • Classic Named Tuples
- • Typed Named Tuples
- • Type Hints 101
- • More About @dataclass
- • Pattern Matching Class Instances
Chapter 6: Object References, Mutability, and Recycling
- • Variables Are Not Boxes
- • Identity, Equality, and Aliases
- • The Relative Immutability of Tuples
- • Copies Are Shallow by Default
- • Deep and Shallow Copies of Arbitrary Objects
- • Function Parameters as References
- • del and Garbage Collection
- • Tricks Python Plays with Immutables
Chapter 7: Functions as First-Class Objects
- • Treating a Function Like an Object
- • Higher-Order Functions
- • Modern Replacements for map, filter, and reduce
- • Anonymous Functions
- • The Nine Flavors of Callable Objects
- • User-Defined Callable Types
- • From Positional to Keyword-Only Parameters
- • Positional-Only Parameters
- • Packages for Functional Programming
Chapter 8: Type Hints in Functions
- • About Gradual Typing
- • Gradual Typing in Practice
- • Starting with Mypy
- • Types Are Defined by Supported Operations
- • Types Usable in Annotations
- • The Any Type
- • Simple Types and Classes
- • Optional and Union Types
- • Generic Collections
- • Tuple Types
- • Generic Mappings
- • Abstract Base Classes
- • Static Protocols
- • Callable
- • NoReturn
Chapter 9: Decorators and Closures
- • Decorators 101
- • When Python Executes Decorators
- • Registration Decorators
- • Variable Scope Rules
- • Closures
- • The nonlocal Declaration
- • Variable Lookup Logic
- • Implementing a Simple Decorator
- • Decorators in the Standard Library
- • Parameterized Decorators
Chapter 10: Design Patterns with First-Class Functions
- • Case Study: Refactoring Strategy
- • Classic Strategy
- • Function-Oriented Strategy
- • Choosing the Best Strategy: Simple Approach
- • Finding Strategies in a Module
- • Decorator-Enhanced Strategy Pattern
- • The Command Pattern
Chapter 11: A Pythonic Object
- • Object Representations
- • Vector Class Redux
- • An Alternative Constructor
- • classmethod Versus staticmethod
- • Formatted Displays
- • A Hashable Vector2d
- • Supporting Positional Pattern Matching
- • Private and Protected Attributes in Python
- • Saving Memory with __slots__
- • Overriding Class Attributes
Chapter 12: Special Methods for Sequences
- • Vector: A User-Defined Sequence Type
- • Protocols and Duck Typing
- • How Slicing Works
- • A Slice-Aware __getitem__
- • Dynamic Attribute Access
- • Hashing and a Faster ==
- • Formatting
Chapter 13: Interfaces, Protocols, and ABCs
- • The Typing Map
- • Two Kinds of Protocols
- • Programming Ducks
- • Python Digs Sequences
- • Monkey Patching: Implementing a Protocol at Runtime
- • Defensive Programming and Fail Fast
- • Goose Typing
- • Subclassing an ABC
- • ABCs in the Standard Library
- • Defining and Using an ABC
- • Structural Typing with ABCs
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