Better Python Code: A Guide for Aspiring Experts

Cover
Half Title
Title Page
Copyright Page
Contents
Foreword
Preface
Acknowledgments
AbouttheAuthor
Introduction
1 Looping Over the Wrong Things
	1.1 (Rarely) Generate a List for Iteration
	1.2 Use enumerate() Instead of Looping Over an Index
	1.3 Don’t Iterate Over dict.keys() When You Want dict.items()
	1.4 Mutating an Object During Iteration
	1.5 for Loops Are More Idiomatic Than while Loops
	1.6 The Walrus Operator for “Loop-and-a-Half” Blocks
	1.7 zip() Simplifies Using Multiple Iterables
	1.8 zip(strict=True) and itertools.zip_longest()
	1.9 Wrapping Up
2 Confusing Equality with Identity
	2.1 Late Binding of Closures
	2.2 Overchecking for Boolean Values
	2.3 Comparing x == None
	2.4 Misunderstanding Mutable Default Arguments
		2.4.1 First Approach, Usea Class
		2.4.2 Second Approach, Use a None Sentinel
		2.4.3 Third Approach, Take Advantage of Stateful Generators
	2.5 Copies versus References to Mutable Objects
	2.6 Confusing is with == (in the Presence of Interning)
	2.7 Wrapping Up
3 A Grab Bag of Python Gotchas
	3.1 Naming Things
		3.1.1 Naming a File Identically to a Standard Library Module
		3.1.2 Avoid Using import *
		3.1.3 Bare or Overly Generic except Statements
	3.2 Quadratic Behavior of Naive String Concatenation
	3.3 Usea Context Manager to Opena File
		3.3.1 First Danger
		3.3.2 Second Danger
		3.3.3 Correcting the Fragility
	3.4 Optional Argument key to .sort() and sorted()
	3.5 Usedict .get() for Uncertain Keys
	3.6 Wrapping Up
4 Advanced Python Usage
	4.1 Comparing type(x) == type(y)
	4.2 Naming Things (Revisited)
		4.2.1 Overriding Names in Built-ins
		4.2.2 Directly Accessing a Protected Attribute
	4.3 Keep Less-Used Features in Mind
		4.3.1 F-String Debugging
		4.3.2 The Elegant Magic of Decorators
		4.3.3 Use itertools (Sufficiently)
		4.3.4 The more-itertools Third-Party Library
	4.4 Type Annotations Are Not Runtime Types
		4.4.1 Type Annotations Are Not Runtime Constraints
		4.4.2 Mistaking typing.NewType() for a Runtime Type
	4.5 Wrapping Up
5 Just Because You Can, It Doesn’t Mean You Should…
	5.1 Metaclasses
	5.2 Monkey patching
	5.3 Getters and Setters
	5.4 It’s Easier to Ask for Forgiveness Than Permission
	5.5 Structural Pattern Matching
	5.6 Regular Expressions and Catastrophic Backtracking
	5.7 Wrapping Up
6 Picking the Right Data Structure
	6.1 collections. defaultdict
	6.2 collections.Counter
		6.2.1 The Solution
		6.2.2 The Mistake
	6.3 collections.deque
		6.3.1 The Solution
		6.3.2 The Mistake
	6.4 collections.ChainMap
		6.4.1 The Solution
		6.4.2 The Mistake
	6.5 Dataclasses and Namedtuples
		6.5.1 Using Namedtuples
		6.5.2 Static versus Dynamic
		6.5.3 Data Classes
	6.6 Efficient Concrete Sequences
	6.7 Wrapping Up
7 Misusing Data Structures
	7.1 Quadratic Behavior of Repeated List Search
	7.2 Deleting or Adding Elements to the Middle of a List
		7.2.1 More Efficient Data Structures
	7.3 Strings Are Iterables of Strings
	7.4 (Often) Use enum Rather Than CONSTANT
	7.5 Learn Less Common Dictionary Methods
		7.5.1 The Dictionaries Defining Objects
		7.5.2 Back to Our Regularly Scheduled Mistake
	7.6 JSON Does Not Round-Trip Cleanly to Python
		7.6.1 Some Back ground on JSON
		7.6.2 Data That Fails to Round-Trip
	7.7 Rolling Your Own Data Structures
		7.7.1 When Rolling Your Own Is a Bad Idea
		7.7.2 When Rolling Your Own Is a Good Idea
		7.7.3 Takeaways
	7.8 Wrapping Up
8 Security
	8.1 Kinds of Randomness
		8.1.1 Use secrets for Cryptographic Randomness
		8.1.2 Reproducible Random Distributions
	8.2 Putting Passwords or Other Secrets in “Secure” Source Code
	8.3 “Rolling Your Own” Security Mechanisms
	8.4 Use SSL/TLS for Microservices
	8.5 Using the Third-Party requests Library
	8.6 SQL Injection Attacks When Not Using DB-API
	8.7 Don’t Use assert to Check Safety Assumptions
	8.8 Wrapping Up
9 Numeric Computation in Python
	9.1 Understanding IEEE-754 Floating Point Numbers
		9.1.1 Comparing NaNs (and Other Floating Point Numbers)
		9.1.2 NaNs and statistics.median()
		9.1.3 Naive Use of Floating Point Numbers: Associativity and Distributivity
		9.1.4 Naive Use of Floating Point Numbers: Granularity
	9.2 Numeric Datatypes
		9.2.1 Avoid Floating Point Numbers for Financial Calculations
		9.2.2 Nonobvious Behaviors of Numeric Datatypes
	9.3 Wrapping Up
A Appendix: Topics for Other Books
	A.1 Test-Driven Development
	A.2 Concurrency
	A.3 Packaging
	A.4 Type Checking
	A.5 Numeric and Dataframe Libraries
Index
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