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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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Toc

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 1

  1. In the order of importance: functionality, correctness, and efficiency.
  2. An assert statement raises an error when the condition it checks for is not satisfied. As such, these statements are used in tests, where we determine whether a program computes and outputs values as it is supposed to.
  3. A benchmark is a small but representative use case that can be used to estimate the speed of a program. Benchmarks can be used to compare different versions of a program to see if a new implementation leads to an improvement in efficiency.
  4. In IPython or Jupyter notebooks, the timeit magic command, when placed in front of a code snippet, will run that code several times and record the running time of each run. The output of the command will show summary statistics of the recorded times so that we can estimate the average running time of the code we are interested in.
  5. cProfile includes the following in its output:
    1. ncalls: The number of times the function was called.
    2. tottime...
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