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Advanced Python Programming

You're reading from   Advanced Python Programming Accelerate your Python programs using proven techniques and design patterns

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801814010
Length 606 pages
Edition 2nd Edition
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Author (1):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
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Table of Contents (32) Chapters Close

Preface 1. Section 1: Python-Native and Specialized Optimization
2. Chapter 1: Benchmarking and Profiling FREE CHAPTER 3. Chapter 2: Pure Python Optimizations 4. Chapter 3: Fast Array Operations with NumPy, Pandas, and Xarray 5. Chapter 4: C Performance with Cython 6. Chapter 5: Exploring Compilers 7. Chapter 6: Automatic Differentiation and Accelerated Linear Algebra for Machine Learning 8. Section 2: Concurrency and Parallelism
9. Chapter 7: Implementing Concurrency 10. Chapter 8: Parallel Processing 11. Chapter 9: Concurrent Web Requests 12. Chapter 10: Concurrent Image Processing 13. Chapter 11: Building Communication Channels with asyncio 14. Chapter 12: Deadlocks 15. Chapter 13: Starvation 16. Chapter 14: Race Conditions 17. Chapter 15: The Global Interpreter Lock 18. Section 3: Design Patterns in Python
19. Chapter 16: The Factory Pattern 20. Chapter 17: The Builder Pattern 21. Chapter 18: Other Creational Patterns 22. Chapter 19: The Adapter Pattern 23. Chapter 20: The Decorator Pattern 24. Chapter 21: The Bridge Pattern 25. Chapter 22: The Façade Pattern 26. Chapter 23: Other Structural Patterns 27. Chapter 24: The Chain of Responsibility Pattern 28. Chapter 25: The Command Pattern 29. Chapter 26: The Observer Pattern 30. Assessments 31. Other Books You May Enjoy

Chapter 3: Fast Array Operations with NumPy, Pandas, and Xarray

NumPy is the de facto standard for scientific computing in Python. It offers flexible multidimensional arrays that allow you to perform fast and concise mathematical calculations.

NumPy provides common data structures and algorithms designed to express complex mathematical operations using a concise syntax. The multidimensional array, numpy.ndarray, is internally based on C arrays. Apart from the performance benefits, this choice allows NumPy code to easily interface with the existing C and FORTRAN routines; NumPy helps bridge the gap between Python and the legacy code written using those languages.

In this chapter, we will learn how to create and manipulate NumPy arrays. We will also explore the NumPy broadcasting feature, which is used to rewrite complex mathematical expressions efficiently and succinctly.

pandas is a tool that relies heavily on NumPy and provides additional data structures and algorithms targeted...

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