Learning Advanced Python by Studying Open Source Projects

This book is one of its own kind. It is not an encyclopedia or a hands-on tutorial that traps readers in the tutorial hell. It is a distillation of just one common Python user’s learning experience. The experience is packaged with exceptional teaching techniques, careful dependence unraveling and, most importantly, passion. Learning Advanced Python by Studying Open Source Projects helps readers overcome the difficulty in their day-to-day tasks and seek insights from solutions in famous open source projects. Different from a technical manual, this book mixes the technical knowledge, real-world applications and more theoretical content, providing readers with a practical and engaging approach to learning Python. Throughout this book, readers will learn how to write Python code that is efficient, readable and maintainable, covering key topics such as data structures, algorithms, object-oriented programming and more. The author’s passion for Python shines through in this book, making it an enjoyable and inspiring read for both beginners and experienced programmers. Learning Advanced Python by Studying Open Source Projects is a book that is written for 95% of Python users, covering 95% of their daily use cases. What does this mean? From the content perspective, it means that this book is not an encyclopedia that covers everything. It covers all the important things for most Python users. Based on my observations and research, the following topics are considered important: 1. The Python data model 2. The Python classes 3. Concurrency and asynchronous programming 4. Functions and related tips 5. How to design an OOP system 6. How to test code Preface Acknowledgments INTRODUCTION PURPOSE AND SCOPE OF THIS BOOK OVERVIEW OF THE APPROACH TAKEN NOTE CHAPTER 1 ◾ The Data Model of Python A GENTLE INTRODUCTION TO PYTHON’S DATA MODEL CUSTOMIZED COMPARISON A MANAGED ITERATION BEHAVIOR ATTRIBUTES, FUNCTION OR DICTIONARY? SUMMARY NOTES CHAPTER 2 ◾ Selected Topics of Python Classes INTRODUCTION DESCRIPTORS AND ATTRIBUTE LOOKUP ORDER Descriptor Demystified Lazy Evaluation in Matplotlib METACLASS AND ITS USAGE IN ELASTICSEARCH DSL Understanding Metaclass Using Meta-Recipe Use Metaclass to Model Documents in Elasticsearch DSL SUMMARY APPENDIX NOTES CHAPTER 3 ◾ Concurrency in Python CONCURRENCY FROM A TOP-DOWN PERSPECTIVE Operating System and Concurrency Introducing Global Interpreter Lock (GIL) MULTIPROCESSING FOR CPU BOUND TASKS Parallel Pandas Apply in pandarallel MULTITHREADING FOR I/O BOUND TASKS SUMMARY NOTES CHAPTER 4 ◾ Asynchronous Programming in Python A SHIFT OF PARADIGM EVENT-DRIVEN SIMULATION ASYNC AS A PATTERN SUMMARY APPENDIX NOTES CHAPTER 5 ◾ Power Up Your Python Functions INTRODUCTION THE DECORATOR FOR RETRYING A FUNCTION CONTEXT MANAGER IN A NUTSHELL DIVE INTO THE AIOSQLITE EXAMPLE Connection as an Executor and a Scheduler Connection and Cursor as Async Context Managers SUMMARY NOTES CHAPTER 6 ◾ Selected OOP Design Best Practices UNDERSTAND YOUR BUSINESS A Quick Overview of the Business MODEL THE BUSINESS ENTITIES WITH OOP Design the Core Entities Establish the Relationship between Classes The Benefits of Universal interface Sometimes No OOP is the Best Design SUMMARY NOTES CHAPTER 7 ◾ Testing in a Pistachio Shell INTRODUCTION FIXTURE AND PARAMETERIZATION Parameterization Resources and Fixture MONKEY PATCH Modify the Built-in Print More Powerful Monkey Patching PROPERTY-BASED TEST SUMMARY NOTES INDEX