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

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781801814010

Length 606 pages

Edition 2nd Edition

Languages

Python

Tools

PyCharm

Concepts

Design Patterns

Author (1):

Quan Nguyen

View More author details

Table of Contents (32) Chapters

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

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...