Practices of the Python Pro (Audiobook)

## Summary Professional developers know the many benefits of writing application code that’s clean, well-organized, and easy to maintain. By learning and following established patterns and best practices, you can take your code and your career to a new level. With __Practices of the Python Pro__, you’ll learn to design professional-level, clean, easily maintainable software at scale using the incredibly popular programming language, Python. You’ll find easy-to-grok examples that use pseudocode and Python to introduce software development best practices, along with dozens of instantly useful techniques that will help you code like a pro. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. ## About the technology Professional-quality code does more than just run without bugs. It’s clean, readable, and easy to maintain. To step up from a capable Python coder to a professional developer, you need to learn industry standards for coding style, application design, and development process. That’s where this book is indispensable. ## About the book __Practices of the Python Pro__ teaches you to design and write professional-quality software that’s understandable, maintainable, and extensible. Dane Hillard is a Python pro who has helped many dozens of developers make this step, and he knows what it takes. With helpful examples and exercises, he teaches you when, why, and how to modularize your code, how to improve quality by reducing complexity, and much more. Embrace these core principles, and your code will become easier for you and others to read, maintain, and reuse. ## What's inside * Organizing large Python projects * Achieving the right levels of abstraction * Writing clean, reusable code Inheritance and composition * Considerations for testing and performance ## About the reader For readers familiar with the basics of Python, or another OO language. ## About the author **Dane Hillard** has spent the majority of his development career using Python to build web applications. **Table of Contents**: PART 1 WHY IT ALL MATTERS 1 ¦ The bigger picture PART 2 FOUNDATIONS OF DESIGN 2 ¦ Separation of concerns 3 ¦ Abstraction and encapsulation 4 ¦ Designing for high performance 5 ¦ Testing your software PART 3 NAILING DOWN LARGE SYSTEMS 6 ¦ Separation of concerns in practice 7 ¦ Extensibility and flexibility 8 ¦ The rules (and exceptions) of inheritance 9 ¦ Keeping things lightweight 10 ¦ Achieving loose coupling PART 4 WHAT’S NEXT? 11 ¦ Onward and upward Practices of the Python Pro brief contents contents preface acknowledgments about this book Who should read this book How this book is organized: A roadmap About the code liveBook discussion forum about the author about the cover illustration Part 1: Why it all matters Chapter 1: The bigger picture 1.1 Python is an enterprise language 1.1.1 The times they are a-changin’ 1.1.2 What I like about Python 1.2 Python is a teaching language 1.3 Design is a process 1.3.1 The user experience 1.3.2 You’ve been here before 1.4 Design enables better software 1.4.1 Considerations in software design 1.4.2 Organically grown software 1.5 When to invest in design 1.6 New beginnings 1.7 Design is democratic 1.7.1 Presence of mind 1.8 How to use this book Part 2: Foundations of design Chapter 2: Separation of concerns 2.1 Namespacing 2.1.1 Namespaces and the import statement 2.1.2 The many masks of importing 2.1.3 Namespaces prevent collisions 2.2 The hierarchy of separation in Python 2.2.1 Functions 2.2.2 Classes 2.2.3 Modules 2.2.4 Packages Chapter 3: Abstraction and encapsulation 3.1 What is abstraction? 3.1.1 The “black box” 3.1.2 Abstraction is like an onion 3.1.3 Abstraction is a simplifier 3.1.4 Decomposition enables abstraction 3.2 Encapsulation 3.2.1 Encapsulation constructs in Python 3.2.2 Expectations of privacy in Python 3.3 Try it out 3.3.1 Refactoring 3.4 Programming styles are an abstraction too 3.4.1 Procedural programming 3.4.2 Functional programming 3.4.3 Declarative programming 3.5 Typing, inheritance, and polymorphism 3.6 Recognizing the wrong abstraction 3.6.1 Square pegs in round holes 3.6.2 Clever gets the cleaver Chapter 4: Designing for high performance 4.1 Hurtling through time and space 4.1.1 Complexity is a little . . . complex 4.1.2 Time complexity 4.1.3 Space complexity 4.2 Performance and data types 4.2.1 Data types for constant time 4.2.2 Data types for linear time 4.2.3 Space complexity of operations on data types 4.3 Make it work, make it right, make it fast 4.3.1 Making it work 4.3.2 Making it right 4.3.3 Making it fast 4.4 Tools 4.4.1 timeit 4.4.2 CPU profiling 4.5 Try it out Chapter 5: Testing your software 5.1 What is software testing? 5.1.1 Does it do what it says on the tin? 5.1.2 The anatomy of a functional test 5.2 Functional testing approaches 5.2.1 Manual testing 5.2.2 Automated testing 5.2.3 Acceptance testing 5.2.4 Unit testing 5.2.5 Integration testing 5.2.6 The testing pyramid 5.2.7 Regression testing 5.3 Statements of fact 5.4 Unit testing with unittest 5.4.1 Test organization with unittest 5.4.2 Running tests with unittest 5.4.3 Writing your first test with unittest 5.4.4 Writing your first integration test with unittest 5.4.5 Test doubles 5.4.6 Try it out 5.4.7 Writing interesting tests 5.5 Testing with pytest 5.5.1 Test organization with pytest 5.5.2 Converting unittest tests to pytest 5.6 Beyond functional testing 5.6.1 Performance testing 5.6.2 Load testing 5.7 Test-driven development: A primer 5.7.1 It’s a mindset 5.7.2 It’s a philosophy Part 3: Nailing down large systems Chapter 6: Separation of concerns in practice 6.1 A command-line bookmarking application 6.2 A tour of Bark 6.2.1 The benefits of separation: Reprise 6.3 An initial code structure, by concern 6.3.1 The persistence layer 6.3.2 The business logic layer 6.3.3 The presentation layer Chapter 7: Extensibility and f lexibility 7.1 What is extensible code? 7.1.1 Adding new behaviors 7.1.2 Modifying existing behaviors 7.1.3 Loose coupling 7.2 Solutions for rigidity 7.2.1 Letting go: Inversion of control 7.2.2 The devil’s in the details: Relying on interfaces 7.2.3 Fighting entropy: The robustness principle 7.3 An exercise in extension Chapter 8: The rules (and exceptions) of inheritance 8.1 The inheritance of programming past 8.1.1 The silver bullet 8.1.2 The challenges of hierarchies 8.2 The inheritance of programming present 8.2.1 What is inheritance for, really? 8.2.2 Substitutability 8.2.3 The ideal use case for inheritance 8.3 Inheritance in Python 8.3.1 Type inspection 8.3.2 Superclass access 8.3.3 Multiple inheritance and method resolution order 8.3.4 Abstract base classes 8.4 Inheritance and composition in Bark 8.4.1 Refactoring to use an abstract base class 8.4.2 A final check on your inheritance work Chapter 9: Keeping things lightweight 9.1 How big should my class/function/module be? 9.1.1 Physical size 9.1.2 Single responsibility 9.1.3 Code complexity 9.2 Breaking down complexity 9.2.1 Extracting configuration 9.2.2 Extracting functions 9.3 Decomposing classes 9.3.1 Initialization complexity 9.3.2 Extracting classes and forwarding calls Chapter 10: Achieving loose coupling 10.1 Defining coupling 10.1.1 The connective tissue 10.1.2 Tight coupling 10.1.3 Loose coupling 10.2 Recognizing coupling 10.2.1 Feature envy 10.2.2 Shotgun surgery 10.2.3 Leaky abstractions 10.3 Coupling in Bark 10.4 Addressing coupling 10.4.1 User messaging 10.4.2 Bookmark persistence 10.4.3 Try it out Part 4: What’s next? Chapter 11: Onward and upward 11.1 What now? 11.1.1 Develop a plan 11.1.2 Execute the plan 11.1.3 Track your progress 11.2 Design patterns 11.2.1 Ups and downs of design patterns in Python 11.2.2 Terms to start with 11.3 Distributed systems 11.3.1 Modes of failure in distributed systems 11.3.2 Addressing application state 11.3.3 Terms to start with 11.4 Take a Python deep dive 11.4.1 Python code style 11.4.2 Language features are patterns 11.4.3 Terms to start with 11.5 Where you’ve been 11.5.1 There and back again: A developer’s tale 11.5.2 Signing off Appendix: Installing Python A.1 What version of Python should I use? A.2 The “system” Python A.3 Installing other versions of Python A.3.1 Download the official Python A.3.2 Download using Anaconda A.4 Verifying the installation index A B C D E F G H I K L M N O P Q R S T U V W Y Z A wealth of information on general software architecture and truths that are applicable to any language. David T. Kerns, Rincon Research Corporation Professional developers know the many benefits of writing application code that's clean, well-organized, and easy to maintain. By learning and following established patterns and best practices, you can take your code and your career to a new level. With Practices of the Python Pro , you'll learn to design professional-level, clean, easily maintainable software at scale using the incredibly popular programming language, Python. You'll find easy-to-grok examples that use pseudocode and Python to introduce software development best practices, along with dozens of instantly useful techniques that will help you code like a pro. about the technology Professional-quality code does more than just run without bugs. It's clean, readable, and easy to maintain. To step up from a capable Python coder to a professional developer, you need to learn industry standards for coding style, application design, and development process. That's where this book is indispensable. about the book Practices of the Python Pro teaches you to design and write professional-quality software that's understandable, maintainable, and extensible. Dane Hillard is a Python pro who has helped many dozens of developers make this step, and he knows what it takes. With helpful examples and exercises, he teaches you when, why, and how to modularize your code, how to improve quality by reducing complexity, and much more. Embrace these core principles, and your code will become easier for you and others to read, maintain, and reuse. what's inside Organizing large Python projects Achieving the right levels of abstraction Writing clean, reusable code Inheritance and composition Considerations for testing and performance about the audience For readers familiar with the basics of Python, or another OO language. about the author Dane Hillard has spent the majority of his development career using Python to build web applications. Get this book, and begin to write Python code like a professional. Davide Cadamuro, BMW Group This will take a Python developer down a path to becoming a pro. Joseph Perenia, Sony Interactive Entertainment NARRATED BY LISA FARINA. Professional-quality code does more than just run without bugs. It's clean, readable, and easy to maintain. To step up from a capable Python coder to a professional developer, you need to learn industry standards for coding style, application desgin, and development process. That's where this book is indispensable. "Practices of the Python Pro" teaches you to design and write professional-quality software that's understandable, maintainable, and extensible. Dane Hillard is a Python pro who has helped many dozens of developers make this step, and he knows what it takes. With helpful examples and exercises, he teaches you when, why, and how to modularize your code, how to improve quality by reducing complexity, and much more. Embrace these core principles, and your code will become easier for you and others to read, maintain, and reuse

Professional developers know the many benefits of writing application code that's clean, well-organized, and easy to maintain. By learning and following established patterns and best practices, you can take your code and your career to a new level.

With Practices of the Python Pro, you'll learn to design professional-level, clean, easily maintainable software at scale using the incredibly popular programming language, Python. You'll find easy-to-grok examples that use pseudocode and Python to introduce software development best practices, along with dozens of instantly useful techniques that will help you code like a pro.