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Advanced Python Programming - Second Edition

You're reading from  Advanced Python Programming - Second Edition

Product type Book
Published in Mar 2022
Publisher Packt
ISBN-13 9781801814010
Pages 606 pages
Edition 2nd Edition
Languages
Author (1):
Quan Nguyen Quan Nguyen
Profile icon Quan Nguyen

Table of Contents (32) Chapters

Preface 1. Section 1: Python-Native and Specialized Optimization
2. Chapter 1: Benchmarking and Profiling 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

Working with database-style data with pandas

pandas is a library that was originally developed by Wes McKinney. It was designed to analyze datasets in a seamless and performant way. In recent years, this powerful library has seen incredible growth and a huge adoption by the Python community. In this section, we will introduce the main concepts and tools provided in this library, and we will use them to increase the performance of various use cases that can't otherwise be addressed with NumPy's vectorized operations and broadcasting.

pandas fundamentals

While NumPy deals mostly with arrays, pandas's main data structures are pandas.Series, pandas.DataFrame, and pandas.Panel. In the rest of this chapter, we will abbreviate pandas to pd.

The main difference between a pd.Series object and an np.array is that a pd.Series object associates a specific key with each element of an array. Let's see how this works in practice with an example.

Let's assume that...

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