Beyond the Basic Stuff with Python : Best Practices for Writing Clean Code

Name: Beyond the Basic Stuff with Python : Best Practices for Writing Clean Code
Author: Al. Sweigart
ISBN: 9781593279660

by Al. Sweigart

Paperback

Beyond the Basic Stuff with Python : Best Practices for Writing Clean Code

by Al. Sweigart

Paperback

Overview

BRIDGE THE GAP BETWEEN NOVICE AND PROFESSIONAL You've completed a basic Python programming tutorial or finished Al Sweigart's bestseller, Automate the Boring Stuff with Python . What's the next step toward becoming a capable, confident software developer? Welcome to Beyond the Basic Stuff with Python . More than a mere collection of advanced syntax and masterful tips for writing clean code, you'll learn how to advance your Python programming skills by using the command line and other professional tools like code formatters, type checkers, linters, and version control. Sweigart takes you through best practices for setting up your development environment, naming variables, and improving readability, then tackles documentation, organization and performance measurement, as well as object-oriented design and the Big-O algorithm analysis commonly used in coding interviews. The skills you learn will boost your ability to program--not just in Python but in any language. You'll learn- Coding style, and how to use Python's Black auto-formatting tool for cleaner code Common sources of bugs, and how to detect them with static analyzers How to structure the files in your code projects with the Cookiecutter template tool Functional programming techniques like lambda and higher-order functions How to profile the speed of your code with Python's built-in timeit and cProfile modules The computer science behind Big-O algorithm analysis How to make your comments and docstrings informative, and how often to write them How to create classes in object-oriented programming, and why they're used to organize code Toward the end of the book you'll read a detailed source-code breakdown of two classic command-line games, the Tower of Hanoi (a logic puzzle) and Four-in-a-Row (a two-player tile-dropping game), and a breakdown of how their code follows the book's best practices. You'll test your skills by implementing the program yourself. Of course, no single book can make you a professional software developer. But Beyond the Basic Stuff with Python will get you further down that path and make you a better programmer, as you learn to write readable code that's easy to debug and perfectly Pythonic Requirements- Covers Python 3.6 and higher

FormatPaperback

LanguageEnglish

PublisherNo Starch Press, Incorporated

Publication DateDec. 16th, 2020

ISBN-139781593279660

Dimensions6.97 x 9.17 x 1.06 inches

ISBN-101593279663

Shipping Weight1.65 lbs

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Professional Reviews

"My early Python programs work but could be improved massively with what Al writes about . . . a small goldmine of knowledge that beginners, intermediates and probably even advanced programmers will benefit from."
-- GeekTechStuff

Acknowledgments	p. xix
Introduction	p. xxi
Who Should Read This Book and Why	p. xxii
About This Book	p. xxii
Your Programming Journey	p. xxiv
Getting Started	p. 1
Dealing with Errors and Asking for Help	p. 3
How to Understand Python Error Messages	p. 4
Examining Tracebacks	p. 4
Searching for Error Messages	p. 7
Preventing Errors with Linters	p. 8
How to Ask for Programming Help	p. 9
Limit Back and Forth by Providing Your Information Upfront	p. 10
State Your Question in the Form of an Actual Question	p. 10
Ask Your Question on the Appropriate Website	p. 10
Summarize Your Question in the Headline	p. 11
Explain What You Want the Code to Do	p. 11
Include the Full Error Message	p. 11
Share Your Complete Code	p. 11
Make Your Code-Readable with Proper Formatting	p. 12
Tell Your Helper What You've Already Tried	p. 13
Describe Your Setup	p. 13
Examples of Asking a Question	p. 14
Summary	p. 14
Environment Setup and the Command Line	p. 17
The Filesystem	p. 18
Paths in Python	p. 18
The Home Directory	p. 19
The Current Working Directory	p. 19
Absolute vs. Relative Paths	p. 20
Programs and Processes	p. 21
The Command Line	p. 22
Opening a Terminal Window	p. 23
Running Programs from the Command Line	p. 23
Using Command Line Arguments	p. 24
Running Python Code from the Command Line with -c	p. 26
Running Python Programs from the Command Line	p. 26
Running the py.exe Program	p. 26
Running Commands from a Python Program	p. 27
Minimizing Typing with Tab Completion	p. 27
Viewing the Command History	p. 28
Working with Common Commands	p. 28
Environment Variables and Path	p. 35
Viewing Environment Variables	p. 36
Working with the Path Environment Variable	p. 36
Changing the Command Line's PATH Environment Variable	p. 37
Permanently Adding Folders to PATH on Windows	p. 38
Permanently Adding Folders to PATH on macOS and Linux	p. 39
Running Python Programs Without the Command Line	p. 39
Running Python Programs on Windows	p. 40
Running Python Programs on macOS	p. 41
Running Python Programs on Ubuntu Linux	p. 41
Summary	p. 42
Best Practices, Tools, and Techniques	p. 43
Code Formatting with Black	p. 45
How to Lose Friends and Alienate Co-Workers	p. 46
Style Guides and PEP 8	p. 46
Horizontal Spacing	p. 47
Use Space Characters for Indentation	p. 47
Spacing Within a Line	p. 48
Vertical Spacing	p. 51
A Vertical Spacing Example	p. 51
Vertical Spacing Best Practices	p. 52
Black: The Uncompromising Code Formatter	p. 53
Installing Black	p. 54
Running Black from the Command Line	p. 54
Disabling Black for Parts of Your Code	p. 57
Summary	p. 58
Choosing Understandable Names	p. 59
Casing Styles	p. 60
PEP 8's Naming Conventions	p. 61
Appropriate Name Length	p. 61
Too Short Names	p. 61
Too Long Names	p. 63
Make Names Searchable	p. 64
Avoid Jokes, Puns, and Cultural References	p. 64
Don't Overwrite Built-in Names	p. 65
The Worst Possible Variable Names Ever	p. 66
Summary	p. 67
Finding Code Smells	p. 69
Duplicate Code	p. 70
Magic Numbers	p. 71
Commented-Out Code and Dead Code	p. 74
Print Debugging	p. 75
Variables with Numeric Suffixes	p. 76
Classes That Should Just Be Functions or Modules	p. 77
List Comprehensions Within List Comprehensions	p. 77
Empty except Blocks and Poor Error Messages	p. 79
Code Smell Myths	p. 80
Myth: Functions Should Have Only One Return Statement at the End	p. 80
Myth: Functions Should Have at Most One try Statement	p. 81
Myth: Flag Arguments Are Bad	p. 82
Myth: Global Variables Are Bad	p. 82
Myth: Comments Are Unnecessary	p. 83
Summary	p. 84
Writing Pythonic Code	p. 87
The Zen of Python	p. 88
Learning to Love Significant Indentation	p. 91
Commonly Misused Syntax	p. 92
Use enumerate() Instead of range()	p. 92
Use the with Statement Instead of open() and close()	p. 93
Use is to Compare with None Instead of ==.	p. 94
Formatting Strings	p. 95
Use Raw Strings If Your String Has Many Backslashes	p. 95
Format Strings with F-Strings	p. 96
Making Shallow Copies of Lists	p. 97
Pythonic Ways to Use Dictionaries	p. 98
Use get() and Setdefault() with Dictionaries	p. 98
Use Collections.defaultdict for Default Values	p. 99
Use Dictionaries Instead of a switch Statement	p. 100
Conditional Expressions: Python's "Ugly" Ternary Operator	p. 101
Working with Variable Values	p. 103
Chaining Assignment and Comparison Operators	p. 103
Checking Whether a Variable Is One of Many Values	p. 103
Summary	p. 104
Programming Jargon	p. 107
Definitions	p. 108
Python the Language and Python the Interpreter	p. 108
Garbage Collection	p. 109
Literals	p. 109
Keywords	p. 110
Objects, Values, Instances, and Identities	p. 111
Items	p. 114
Mutable and Immutable	p. 114
Indexes, Keys, and Hashes	p. 117
Containers, Sequences, Mapping, and Set Types	p. 119
Dunder Methods and Magic Methods	p. 120
Modules and Packages	p. 120
Collables and First-Class Objects	p. 121
Commonly Confused Terms	p. 122
Statements vs. Expressions	p. 122
Block vs. Clause vs. Body	p. 123
Variable vs. Attribute	p. 124
Function vs. Method	p. 124
Iterable vs. Iterator	p. 125
Syntax vs. Runtime vs. Semantic Errors	p. 126
Parameters vs. Arguments	p. 128
Type Coercion vs. Type Casting	p. 128
Properties vs. Attributes	p. 128
Bytecode vs. Machine Code	p. 129
Script vs. Program, Scripting Language vs. Programming Language	p. 129
Library vs. Framework vs. SDK vs. Engine vs. API	p. 130
Summary	p. 131
Further Reading	p. 131
Common Python Gotchas	p. 133
Don't Add or Delete Items from a List While Looping Over It	p. 134
Don't Copy Mutable Values Without copy.copy() and copy.deepcopy()	p. 140
Don't Use Mutable Values for Default Arguments	p. 142
Don't Build Strings with String Concatenation	p. 144
Don't Expect sort() to Sort Alphabetically	p. 146
Don't Assume Floating-Point Numbers Are Perfectly Accurate	p. 147
Don't Chain Inequality != Operators	p. 149
Don't Forget the Comma in Single-Item Tuples	p. 150
Summary	p. 150
Esoteric Python Oddities	p. 153
Why 256 Is 256 but 257 Is Not 257	p. 154
String Interning	p. 155
Python's Fake Increment and Decrement Operators	p. 156
All of Nothing	p. 157
Boolean Values Are Integer Values	p. 158
Chaining Multiple Kinds of Operators	p. 159
Python's Antigravity Feature	p. 160
Summary	p. 160
Writing Effective Functions	p. 161
Function Names	p. 162
Function Size Trade-Offs	p. 162
Function Parameters and Arguments	p. 165
Default Arguments	p. 165
Using * and ** to Pass Arguments to Functions	p. 166
Using * to Create Variadic Functions	p. 167
Using ** to Create Variadic Functions	p. 169
Using * and ** to Create Wrapper Functions	p. 171
Functional Programming	p. 172
Side Effects	p. 172
Higher-Order Functions	p. 174
Lambda Functions	p. 174
Mapping and Filtering with List Comprehensions	p. 175
Return Values Should Always Have the Same Data Type	p. 177
Raising Exceptions vs. Returning Error Codes	p. 178
Summary	p. 179
Comments, Docstrings, and Type Hints	p. 181
Comments	p. 182
Comment Style	p. 183
Inline Comments	p. 184
Explanatory Comments	p. 184
Summary Comments	p. 185
"Lessons Learned" Comments	p. 185
Legal Comments	p. 186
Professional Comments	p. 186
Codetags and TODO Comments	p. 187
Magic Comments and Source File Encoding	p. 187
Docstrings	p. 188
Type Hints	p. 190
Using Static Analyzers	p. 192
Setting Type Hints for Multiple Types	p. 194
Setting Type Hints for Lists, Dictionaries, and More	p. 195
Backporting Type Hints with Comments	p. 196
Summary	p. 197
Organizing Your Code Projects with Git	p. 199
Git Commits and Repos	p. 200
Using Cookiecutter to Create New Python Projects	p. 200
Installing Git	p. 202
Configuring Your Git Username and Email	p. 203
Installing GUI Git Tools	p. 203
The Git Workflow	p. 204
How Git Keeps Track of File Status	p. 204
Why Stage Files?	p. 206
Creating a Git Repo on Your Computer	p. 206
Adding Files for Git to Track	p. 208
Ignoring Files in the Repo	p. 209
Committing Changes	p. 210
Deleting Files from the Repo	p. 214
Renaming and Moving Files in the Repo	p. 215
Viewing the Commit Log	p. 216
Recovering Old Changes	p. 217
Undoing Uncommitted Local Changes	p. 218
Unstaging a Staged File	p. 218
Rolling Back the Most Recent Commits	p. 218
Rolling Back to a Specific Commit for a Single File	p. 219
Rewriting the Commit History	p. 220
GitHub and the git push Command	p. 221
Pushing an Existing Repository to GitHub	p. 222
Cloning a Repo from an Existing GitHub Repo	p. 222
Summary	p. 223
Measuring Performance and Big O Algorithm Analysis	p. 225
The timeit Module	p. 226
The cProfile Profiler	p. 228
Big O Algorithm Analysis	p. 230
Big O Orders	p. 230
A Bookshelf Metaphor for Big O Orders	p. 231
Big O Measures the Worst-Case Scenario	p. 235
Determining the Big O Order of Your Code	p. 237
Why Lower Orders and Coefficients Don't Matter	p. 238
Big O Analysis Examples	p. 239
The Big O Order of Common Function Calls	p. 242
Analyzing Big O at a Glance	p. 243
Big O Doesn't Matter When n Is Small, and n Is Usually Small	p. 244
Summary	p. 244
Practice Projects	p. 247
The Tower of Hanoi	p. 248
The Output	p. 249
The Source Code	p. 250
Writing the Code	p. 252
Four-in-a-Raw	p. 259
The Output	p. 259
The Source Code	p. 260
Writing the Code	p. 264
Summary	p. 271
Object-Oriented Python	p. 273
Object-Oriented Programming and Classes	p. 275
Real-World Analogy: Filling Out a Form	p. 276
Creating Objects from Classes	p. 278
Creating a Simple Class: WizCoin	p. 279
Methods, _init_(), and self	p. 280
Attributes	p. 282
Private Attributes and Private Methods	p. 282
The Type() Function and _qualname_Attribute	p. 284
Non-OOP vs. OOP Examples: Tic-Tac-Toe	p. 285
Designing Classes for the Real World Is Hard	p. 290
Summary	p. 291
Object-Oriented Programming and Inheritance	p. 293
How Inheritance Works	p. 294
Overriding Methods	p. 296
The super() Function	p. 297
Favor Composition Over Inheritance	p. 299
Inheritance's Downside	p. 301
The isinstance() and issubclass() Functions	p. 303
Class Methods	p. 304
Class Attributes	p. 306
Static Methods	p. 306
When to Use Class and Static Object-Oriented Features	p. 307
Object-Oriented Buzzwords	p. 307
Encapsulation	p. 307
Polymorphism	p. 308
When Not to Use Inheritance	p. 308
Multiple Inheritance	p. 309
Method Resolution Order	p. 311
Summary	p. 312
Pythonic OOP: Properties and Dunder Methods	p. 315
Properties	p. 316
Turning an Attribute into a Property	p. 316
Using Setters to Validale Data!	p. 319
Read-Only Properties	p. 320
When to Use Properties	p. 322
Python's Dunder Methods	p. 322
String Representation Dunder Methods	p. 323
Numeric Dunder Methods	p. 325
Reflected Numeric Dunder Methods	p. 328
In-Place Augmented Assignment Dunder Methods	p. 330
Comparison Dunder Methods	p. 332
Summary	p. 337
Index	p. 339
Table of Contents provided by Ingram. All Rights Reserved.

Find at your local library from our friends at WorldCat	Find Libraries

Overview

Product Details

FormatPaperback

LanguageEnglish

PublisherNo Starch Press, Incorporated

Publication DateDec. 16th, 2020

ISBN-139781593279660

Dimensions6.97 x 9.17 x 1.06 inches

ISBN-101593279663

Shipping Weight1.65 lbs

Professional Reviews

Acknowledgments	p. xix
Introduction	p. xxi
Who Should Read This Book and Why	p. xxii
About This Book	p. xxii
Your Programming Journey	p. xxiv
Getting Started	p. 1
Dealing with Errors and Asking for Help	p. 3
How to Understand Python Error Messages	p. 4
Examining Tracebacks	p. 4
Searching for Error Messages	p. 7
Preventing Errors with Linters	p. 8
How to Ask for Programming Help	p. 9
Limit Back and Forth by Providing Your Information Upfront	p. 10
State Your Question in the Form of an Actual Question	p. 10
Ask Your Question on the Appropriate Website	p. 10
Summarize Your Question in the Headline	p. 11
Explain What You Want the Code to Do	p. 11
Include the Full Error Message	p. 11
Share Your Complete Code	p. 11
Make Your Code-Readable with Proper Formatting	p. 12
Tell Your Helper What You've Already Tried	p. 13
Describe Your Setup	p. 13
Examples of Asking a Question	p. 14
Summary	p. 14
Environment Setup and the Command Line	p. 17
The Filesystem	p. 18
Paths in Python	p. 18
The Home Directory	p. 19
The Current Working Directory	p. 19
Absolute vs. Relative Paths	p. 20
Programs and Processes	p. 21
The Command Line	p. 22
Opening a Terminal Window	p. 23
Running Programs from the Command Line	p. 23
Using Command Line Arguments	p. 24
Running Python Code from the Command Line with -c	p. 26
Running Python Programs from the Command Line	p. 26
Running the py.exe Program	p. 26
Running Commands from a Python Program	p. 27
Minimizing Typing with Tab Completion	p. 27
Viewing the Command History	p. 28
Working with Common Commands	p. 28
Environment Variables and Path	p. 35
Viewing Environment Variables	p. 36
Working with the Path Environment Variable	p. 36
Changing the Command Line's PATH Environment Variable	p. 37
Permanently Adding Folders to PATH on Windows	p. 38
Permanently Adding Folders to PATH on macOS and Linux	p. 39
Running Python Programs Without the Command Line	p. 39
Running Python Programs on Windows	p. 40
Running Python Programs on macOS	p. 41
Running Python Programs on Ubuntu Linux	p. 41
Summary	p. 42
Best Practices, Tools, and Techniques	p. 43
Code Formatting with Black	p. 45
How to Lose Friends and Alienate Co-Workers	p. 46
Style Guides and PEP 8	p. 46
Horizontal Spacing	p. 47
Use Space Characters for Indentation	p. 47
Spacing Within a Line	p. 48
Vertical Spacing	p. 51
A Vertical Spacing Example	p. 51
Vertical Spacing Best Practices	p. 52
Black: The Uncompromising Code Formatter	p. 53
Installing Black	p. 54
Running Black from the Command Line	p. 54
Disabling Black for Parts of Your Code	p. 57
Summary	p. 58
Choosing Understandable Names	p. 59
Casing Styles	p. 60
PEP 8's Naming Conventions	p. 61
Appropriate Name Length	p. 61
Too Short Names	p. 61
Too Long Names	p. 63
Make Names Searchable	p. 64
Avoid Jokes, Puns, and Cultural References	p. 64
Don't Overwrite Built-in Names	p. 65
The Worst Possible Variable Names Ever	p. 66
Summary	p. 67
Finding Code Smells	p. 69
Duplicate Code	p. 70
Magic Numbers	p. 71
Commented-Out Code and Dead Code	p. 74
Print Debugging	p. 75
Variables with Numeric Suffixes	p. 76
Classes That Should Just Be Functions or Modules	p. 77
List Comprehensions Within List Comprehensions	p. 77
Empty except Blocks and Poor Error Messages	p. 79
Code Smell Myths	p. 80
Myth: Functions Should Have Only One Return Statement at the End	p. 80
Myth: Functions Should Have at Most One try Statement	p. 81
Myth: Flag Arguments Are Bad	p. 82
Myth: Global Variables Are Bad	p. 82
Myth: Comments Are Unnecessary	p. 83
Summary	p. 84
Writing Pythonic Code	p. 87
The Zen of Python	p. 88
Learning to Love Significant Indentation	p. 91
Commonly Misused Syntax	p. 92
Use enumerate() Instead of range()	p. 92
Use the with Statement Instead of open() and close()	p. 93
Use is to Compare with None Instead of ==.	p. 94
Formatting Strings	p. 95
Use Raw Strings If Your String Has Many Backslashes	p. 95
Format Strings with F-Strings	p. 96
Making Shallow Copies of Lists	p. 97
Pythonic Ways to Use Dictionaries	p. 98
Use get() and Setdefault() with Dictionaries	p. 98
Use Collections.defaultdict for Default Values	p. 99
Use Dictionaries Instead of a switch Statement	p. 100
Conditional Expressions: Python's "Ugly" Ternary Operator	p. 101
Working with Variable Values	p. 103
Chaining Assignment and Comparison Operators	p. 103
Checking Whether a Variable Is One of Many Values	p. 103
Summary	p. 104
Programming Jargon	p. 107
Definitions	p. 108
Python the Language and Python the Interpreter	p. 108
Garbage Collection	p. 109
Literals	p. 109
Keywords	p. 110
Objects, Values, Instances, and Identities	p. 111
Items	p. 114
Mutable and Immutable	p. 114
Indexes, Keys, and Hashes	p. 117
Containers, Sequences, Mapping, and Set Types	p. 119
Dunder Methods and Magic Methods	p. 120
Modules and Packages	p. 120
Collables and First-Class Objects	p. 121
Commonly Confused Terms	p. 122
Statements vs. Expressions	p. 122
Block vs. Clause vs. Body	p. 123
Variable vs. Attribute	p. 124
Function vs. Method	p. 124
Iterable vs. Iterator	p. 125
Syntax vs. Runtime vs. Semantic Errors	p. 126
Parameters vs. Arguments	p. 128
Type Coercion vs. Type Casting	p. 128
Properties vs. Attributes	p. 128
Bytecode vs. Machine Code	p. 129
Script vs. Program, Scripting Language vs. Programming Language	p. 129
Library vs. Framework vs. SDK vs. Engine vs. API	p. 130
Summary	p. 131
Further Reading	p. 131
Common Python Gotchas	p. 133
Don't Add or Delete Items from a List While Looping Over It	p. 134
Don't Copy Mutable Values Without copy.copy() and copy.deepcopy()	p. 140
Don't Use Mutable Values for Default Arguments	p. 142
Don't Build Strings with String Concatenation	p. 144
Don't Expect sort() to Sort Alphabetically	p. 146
Don't Assume Floating-Point Numbers Are Perfectly Accurate	p. 147
Don't Chain Inequality != Operators	p. 149
Don't Forget the Comma in Single-Item Tuples	p. 150
Summary	p. 150
Esoteric Python Oddities	p. 153
Why 256 Is 256 but 257 Is Not 257	p. 154
String Interning	p. 155
Python's Fake Increment and Decrement Operators	p. 156
All of Nothing	p. 157
Boolean Values Are Integer Values	p. 158
Chaining Multiple Kinds of Operators	p. 159
Python's Antigravity Feature	p. 160
Summary	p. 160
Writing Effective Functions	p. 161
Function Names	p. 162
Function Size Trade-Offs	p. 162
Function Parameters and Arguments	p. 165
Default Arguments	p. 165
Using * and ** to Pass Arguments to Functions	p. 166
Using * to Create Variadic Functions	p. 167
Using ** to Create Variadic Functions	p. 169
Using * and ** to Create Wrapper Functions	p. 171
Functional Programming	p. 172
Side Effects	p. 172
Higher-Order Functions	p. 174
Lambda Functions	p. 174
Mapping and Filtering with List Comprehensions	p. 175
Return Values Should Always Have the Same Data Type	p. 177
Raising Exceptions vs. Returning Error Codes	p. 178
Summary	p. 179
Comments, Docstrings, and Type Hints	p. 181
Comments	p. 182
Comment Style	p. 183
Inline Comments	p. 184
Explanatory Comments	p. 184
Summary Comments	p. 185
"Lessons Learned" Comments	p. 185
Legal Comments	p. 186
Professional Comments	p. 186
Codetags and TODO Comments	p. 187
Magic Comments and Source File Encoding	p. 187
Docstrings	p. 188
Type Hints	p. 190
Using Static Analyzers	p. 192
Setting Type Hints for Multiple Types	p. 194
Setting Type Hints for Lists, Dictionaries, and More	p. 195
Backporting Type Hints with Comments	p. 196
Summary	p. 197
Organizing Your Code Projects with Git	p. 199
Git Commits and Repos	p. 200
Using Cookiecutter to Create New Python Projects	p. 200
Installing Git	p. 202
Configuring Your Git Username and Email	p. 203
Installing GUI Git Tools	p. 203
The Git Workflow	p. 204
How Git Keeps Track of File Status	p. 204
Why Stage Files?	p. 206
Creating a Git Repo on Your Computer	p. 206
Adding Files for Git to Track	p. 208
Ignoring Files in the Repo	p. 209
Committing Changes	p. 210
Deleting Files from the Repo	p. 214
Renaming and Moving Files in the Repo	p. 215
Viewing the Commit Log	p. 216
Recovering Old Changes	p. 217
Undoing Uncommitted Local Changes	p. 218
Unstaging a Staged File	p. 218
Rolling Back the Most Recent Commits	p. 218
Rolling Back to a Specific Commit for a Single File	p. 219
Rewriting the Commit History	p. 220
GitHub and the git push Command	p. 221
Pushing an Existing Repository to GitHub	p. 222
Cloning a Repo from an Existing GitHub Repo	p. 222
Summary	p. 223
Measuring Performance and Big O Algorithm Analysis	p. 225
The timeit Module	p. 226
The cProfile Profiler	p. 228
Big O Algorithm Analysis	p. 230
Big O Orders	p. 230
A Bookshelf Metaphor for Big O Orders	p. 231
Big O Measures the Worst-Case Scenario	p. 235
Determining the Big O Order of Your Code	p. 237
Why Lower Orders and Coefficients Don't Matter	p. 238
Big O Analysis Examples	p. 239
The Big O Order of Common Function Calls	p. 242
Analyzing Big O at a Glance	p. 243
Big O Doesn't Matter When n Is Small, and n Is Usually Small	p. 244
Summary	p. 244
Practice Projects	p. 247
The Tower of Hanoi	p. 248
The Output	p. 249
The Source Code	p. 250
Writing the Code	p. 252
Four-in-a-Raw	p. 259
The Output	p. 259
The Source Code	p. 260
Writing the Code	p. 264
Summary	p. 271
Object-Oriented Python	p. 273
Object-Oriented Programming and Classes	p. 275
Real-World Analogy: Filling Out a Form	p. 276
Creating Objects from Classes	p. 278
Creating a Simple Class: WizCoin	p. 279
Methods, _init_(), and self	p. 280
Attributes	p. 282
Private Attributes and Private Methods	p. 282
The Type() Function and _qualname_Attribute	p. 284
Non-OOP vs. OOP Examples: Tic-Tac-Toe	p. 285
Designing Classes for the Real World Is Hard	p. 290
Summary	p. 291
Object-Oriented Programming and Inheritance	p. 293
How Inheritance Works	p. 294
Overriding Methods	p. 296
The super() Function	p. 297
Favor Composition Over Inheritance	p. 299
Inheritance's Downside	p. 301
The isinstance() and issubclass() Functions	p. 303
Class Methods	p. 304
Class Attributes	p. 306
Static Methods	p. 306
When to Use Class and Static Object-Oriented Features	p. 307
Object-Oriented Buzzwords	p. 307
Encapsulation	p. 307
Polymorphism	p. 308
When Not to Use Inheritance	p. 308
Multiple Inheritance	p. 309
Method Resolution Order	p. 311
Summary	p. 312
Pythonic OOP: Properties and Dunder Methods	p. 315
Properties	p. 316
Turning an Attribute into a Property	p. 316
Using Setters to Validale Data!	p. 319
Read-Only Properties	p. 320
When to Use Properties	p. 322
Python's Dunder Methods	p. 322
String Representation Dunder Methods	p. 323
Numeric Dunder Methods	p. 325
Reflected Numeric Dunder Methods	p. 328
In-Place Augmented Assignment Dunder Methods	p. 330
Comparison Dunder Methods	p. 332
Summary	p. 337
Index	p. 339
Table of Contents provided by Ingram. All Rights Reserved.

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Beyond the Basic Stuff with Python : Best Practices for Writing Clean Code

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Beyond the Basic Stuff with Python : Best Practices for Writing Clean Code

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