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Expert Python Programming

Expert Python Programming

By : Michał Jaworski, Ziadé
3 (2)
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Expert Python Programming

Expert Python Programming

3 (2)
By: Michał Jaworski, Ziadé

Overview of this book

Python is a dynamic programming language that's used in a wide range of domains thanks to its simple yet powerful nature. Although writing Python code is easy, making it readable, reusable, and easy to maintain is challenging. Complete with best practices, useful tools, and standards implemented by professional Python developers, the third edition of Expert Python Programming will help you overcome this challenge. The book will start by taking you through the new features in Python 3.7. You'll then learn the advanced components of Python syntax, in addition to understanding how to apply concepts of various programming paradigms, including object-oriented programming, functional programming, and event-driven programming. This book will also guide you through learning the naming best practices, writing your own distributable Python packages, and getting up to speed with automated ways to deploy your software on remote servers. You’ll discover how to create useful Python extensions with C, C++, Cython, and CFFI. Furthermore, studying about code management tools, writing clear documentation, and exploring test-driven development will help you write clean code. By the end of the book, you will have become an expert in writing efficient and maintainable Python code.
Table of Contents (25 chapters)
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1
Section 1: Before You Start
4
Section 2: Python Craftsmanship
12
Section 3: Quality over Quantity
16
Section 4: Need for Speed
20
Section 5: Technical Architecture
23
reStructuredText Primer

Using architectural trade-offs

When your code can no longer be improved by reducing the complexity or choosing a proper data structure, a good approach may be to consider a trade-off. If we review users' problems and define what is really important to them, we can often relax some of the application's requirements. Performance can often be improved by doing the following:

  • Replacing exact solution algorithms with heuristics and approximation algorithms
  • Deferring some work to delayed task queues
  • Using probabilistic data structures

Let's move on and take a look at these improvement methods.

Using heuristics and approximation algorithms

Some algorithmic problems simply don't have good state-of-the-art solutions...

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