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Practical Pydantic
The Missing Guide to Data Validation in Python
This book is 100% completeLast updated on 2025年10月14日
Bad data breaks good code. You’ve written Python that works perfectly in testing, only to watch it fail in production because of a malformed API request, a messy CSV, or a missing config value. That’s the hidden cost of Python’s flexibility: without runtime validation, you’re always one bad input away from a crash. Enter Pydantic . This book takes you from the foundations of data validation to...
This book is 100% completeLast updated on 2025年10月14日
Bad data breaks good code. You’ve written Python that works perfectly in testing, only to watch it fail in production because of a malformed API request, a messy CSV, or a missing config value. That’s the hidden cost of Python’s flexibility: without runtime validation, you’re always one bad input away from a crash. Enter Pydantic . This book takes you from the foundations of data validation to...
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About
About the Book
Unlock the full power of Python data validation with Pydantic — the library trusted by FastAPI, SQLModel, and modern Python projects worldwide.
Whether you’re building APIs, cleaning messy datasets, managing environment configurations, or structuring ML workflows, this book gives you the practical skills and deep understanding to write safer, cleaner, and more reliable Python code.
Inside, you’ll discover:
- Foundations of Pydantic and runtime data validation.
- Advanced techniques: custom validators, nested models, and performance tuning.
- Real-world projects: integrating APIs, ETL pipelines, and environment settings.
- A thorough comparison of alternatives like Marshmallow, attrs, dataclasses, TypedDict, and more.
- Expert guidance on choosing the right tool for your project.
- Full Source Code repository for the examples and solutions for the exercises.
Packed with examples, exercises, and actionable advice, this ebook is perfect for Python developers, data engineers, FastAPI users, and anyone who wants to level up their code quality.
Stop guessing whether your data is safe — trust Pydantic, and write Python code with confidence.
Contents
Table of Contents
- Preface
- Why This Book Matters
- Who This Book Is For
- How to Use This Book
- A Personal Note from the Author
- Part I – Foundations of Pydantic
- Chapter 1: Why Data Validation Matters in Python
- Chapter 2: Getting Started with Pydantic
- Chapter 3: Core Concepts You Must Know
- Chapter 4: Serialization and Transformation
- Chapter 5: Pydantic v1 vs v2 – What Changed and Why It Matters
- Part II – Pydantic in the Real World
- Chapter 6: Building APIs with FastAPI and Pydantic
- Chapter 7: Data Engineering with Pydantic
- Chapter 8: Configuration and Settings Management
- Chapter 9: Advanced Techniques
- Chapter 10: Case Study – An End-to-End Project
- Part III – Beyond Pydantic
- Chapter 11: Marshmallow
- Chapter 12: attrs and Dataclasses
- Chapter 13: Other Contenders (Cerberus, TypedDict, Voluptuous)
- Chapter 14: Choosing the Right Tool
- Final Thoughts
- Appendix: Next Steps & Resources
- Preface
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