High Performance Python 2nd Edition by Micha Gorelick & Ian Ozsvald

Unlock Maximum Speed and Efficiency in Your Python Applications with Proven Optimization Techniques

About the Book

High Performance Python 2nd Edition is the definitive resource for Python developers who need their code to run faster and handle larger workloads. Written by performance optimization specialists Micha Gorelick and Ian Ozsvald, this updated edition covers modern Python tools and techniques that dramatically improve execution speed, reduce memory consumption, and enable true parallel processing. You’ll learn how to profile your code scientifically, understand what’s really slowing things down, and apply proven patterns used by companies processing billions of data points daily.

From the Back Cover

“Your Python code works—but does it work fast enough? Learn the art and science of making Python fly with techniques that professional developers use to handle real-world performance challenges at scale.”

About the Author

Micha Gorelick is a fast-data architect specializing in building high-performance distributed systems, while Ian Ozsvald is a data science consultant and founder of ModelInsight who has helped numerous companies optimize their Python applications. Together, they bring decades of hands-on experience solving performance problems across industries ranging from finance to machine learning. Their practical, no-nonsense approach makes complex optimization concepts accessible to developers at any skill level.

Who Is This Book For?

This book is perfect for intermediate to advanced Python developers, data scientists, and software engineers who want to move beyond basic coding and tackle performance-critical applications. If you’re working with large datasets, building APIs that need to handle heavy traffic, developing machine learning pipelines, or simply frustrated with slow Python code, this guide gives you the knowledge to diagnose problems and implement solutions that deliver measurable speed improvements.

What You’ll Learn and Gain

Master essential profiling tools to identify exactly where your code spends time and memory. Discover how to leverage Cython, Numba, and JIT compilation to achieve C-like performance without leaving Python. Understand memory management, garbage collection, and data structure choices that make or break performance. Learn parallel and distributed computing patterns using multiprocessing, async programming, and tools like Dask and Ray for scaling across multiple cores and machines.

Frequently Asked Questions (FAQs)

Q: Do I need advanced Python knowledge to understand this book?
You should be comfortable with core Python concepts like functions, classes, and basic data structures. The book builds from intermediate topics to advanced optimization techniques, making it accessible if you have solid Python fundamentals.

Q: How is the 2nd edition different from the first edition?
The second edition includes updated coverage of modern Python tools like async/await, newer profiling utilities, distributed computing frameworks, and optimizations specific to Python 3.7+. It reflects current best practices and tools used in production environments today.

Q: Will this book help me optimize machine learning code?
Absolutely. The techniques for NumPy optimization, memory management, and parallel processing are directly applicable to data science and ML workflows. You’ll learn how to speed up training pipelines, handle large datasets efficiently, and reduce computational costs.

Q: Can I apply these techniques to my existing Python projects?
Yes, the book emphasizes practical, real-world optimization that you can implement incrementally. You’ll learn how to profile first, identify bottlenecks, then apply specific optimizations without rewriting your entire codebase.

Q: Does it cover both CPU and memory optimization?
The book comprehensively covers both aspects—speeding up computation time and reducing memory footprint. You’ll understand the tradeoffs between speed and memory, and learn when to optimize for each based on your specific constraints.