Python Tricks

A Buffet of Awesome Python Features for Intermediate Developers

Master the lesser-known corners of Python so you write cleaner, more idiomatic code that your colleagues actually want to read.

D Dan Bader

Python Tricks by Dan Bader is a practical collection of bite-sized techniques that expose the hidden features and best practices most Python tutorials never cover. Each trick is self-contained, explained clearly, and backed by runnable code examples. Whether you want to write more Pythonic classes, wrangle decorators with confidence, or stop second-guessing yourself on data structures, this book gives you the working knowledge to do it right.

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About this book

Most Python developers learn enough to get the job done, then hit a plateau. Their code works, but it doesn't feel quite right. They copy patterns from Stack Overflow without fully understanding why those patterns exist. They write functions that a senior Pythonista would quietly refactor the next morning.

Python Tricks is written for exactly that moment. Dan Bader spent years answering questions from intermediate Python developers on his blog and newsletter, and this book distills the most impactful answers into a single, readable volume. Each chapter focuses on one concept, shows you the problem it solves, and walks through working code you can run and modify immediately.

The tricks aren't trivia. They are the patterns that separate code that merely runs from code that is clear, robust, and easy to maintain. You'll learn how Python's object model actually works, what makes a class truly Pythonic, why some decorator patterns are traps, and how to use built-in data structures in ways that cut boilerplate in half.

Understand what Python's data model really means for your classes
Write decorators correctly, including how to preserve function metadata
Use namedtuples, defaultdicts, and ChainMaps in the right situations
Know when and why to use __slots__, __repr__, and __str__
Avoid the most common pitfalls with mutable default arguments and closures
Structure assertions, context managers, and generators for real production use

The format respects your time. Every section is short enough to read on a lunch break and dense enough that you'll still be thinking about it the next morning. There are no lengthy preambles, no padded theory chapters, and no exercises that feel disconnected from real work.

If you already know the basics of Python and want to write the kind of code you'd be comfortable showing to an expert, Python Tricks is where you go next.

🎯 What you'll learn

Explain Python's data model and use it to write classes that behave naturally with built-in operations
Build correct, reusable decorators without accidentally discarding function metadata
Choose the right data structure from Python's standard library for each specific problem
Avoid subtle bugs caused by mutable default arguments, late-binding closures, and shared state
Write generators and context managers that clean up resources reliably in production code
Apply assertion and exception patterns that communicate intent rather than hide errors
Recognize common anti-patterns that make Python code harder to test and maintain

👤 Who is this book for?

Intermediate Python developers who know the syntax but want to write more idiomatic, production-quality code
Self-taught programmers who learned Python from tutorials and feel gaps in their foundational understanding
Backend or data engineers who use Python daily but rarely have time to read the full language documentation
Developers coming from another language who want to stop writing Java or JavaScript in Python syntax
Technical leads who want a reference they can recommend to junior teammates as a leveling-up resource

01

Cleaning Up Your Python Syntax

Covers the small but high-impact syntax habits that make Python code immediately more readable, including how idiomatic Python differs from simply correct Python.
02

Patterns for Better Functions

Explores argument defaults, keyword-only arguments, and the pitfalls of mutable default values, giving you reliable patterns for defining functions that behave predictably.
03

Decorators Demystified

Walks through how decorators work at the function-object level, how to stack them correctly, and how to use functools.wraps to preserve function metadata.
04

Classes and the Python Data Model

Shows how special methods like __repr__, __str__, __eq__, and __slots__ let your classes integrate naturally with Python's built-in operations and protocols.
05

Common Data Structures Done Right

Covers namedtuples, defaultdicts, OrderedDicts, ChainMaps, and other standard-library containers, with concrete guidance on when each one earns its place.
06

Looping and Iteration Idioms

Teaches generator expressions, itertools patterns, and the iterator protocol so you write loops that are both memory-efficient and easy to follow.
07

Dictionary Tricks

Unpacks dictionary merging, comprehension patterns, and the correct way to iterate and mutate dicts without runtime surprises.
08

Pythonic Productivity Patterns

Brings together context managers, assertions, and exception-handling strategies that make your code safer and your intent clearer to the next reader.

Frequently asked questions

Do I need to be an experienced Python developer to benefit from this book?

You should already be comfortable with Python basics: functions, classes, loops, and standard library imports. The book is aimed at intermediate developers who want to move beyond that foundation, not complete beginners.

Is this book still relevant given it was published in 2017?

The vast majority of content covers core Python 3 features and idioms that have not changed. A small number of library details may reflect older minor versions, but the concepts and patterns remain accurate and applicable.

Does the book come with downloadable code examples?

The code examples in the book are short and self-contained, designed to be typed and run directly. Check the publisher's website for any companion resources associated with your purchase.

Is this a reference book or should I read it cover to cover?

Each section is self-contained, so you can dip in wherever a topic is relevant to something you're working on. Most readers start from the beginning and read a section or two at a time rather than in one sitting.

Does the book cover Python 2?

No. The book focuses on Python 3, which is the version you should be using for all new work.