Book Image

Advanced Python Programming - Second Edition

By : Quan Nguyen
Book Image

Advanced Python Programming - Second Edition

By: Quan Nguyen

Overview of this book

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
Table of Contents (32 chapters)
1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

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...