Book Image

Expert Python Programming - Fourth Edition

By : Michał Jaworski, Tarek Ziadé
Book Image

Expert Python Programming - Fourth Edition

By: Michał Jaworski, Tarek Ziadé

Overview of this book

This new edition of Expert Python Programming provides you with a thorough understanding of the process of building and maintaining Python apps. Complete with best practices, useful tools, and standards implemented by professional Python developers, this fourth edition has been extensively updated. Throughout this book, you’ll get acquainted with the latest Python improvements, syntax elements, and interesting tools to boost your development efficiency. The initial few chapters will allow experienced programmers coming from different languages to transition to the Python ecosystem. You will explore common software design patterns and various programming methodologies, such as event-driven programming, concurrency, and metaprogramming. You will also go through complex code examples and try to solve meaningful problems by bridging Python with C and C++, writing extensions that benefit from the strengths of multiple languages. Finally, you will understand the complete lifetime of any application after it goes live, including packaging and testing automation. By the end of this book, you will have gained actionable Python programming insights that will help you effectively solve challenging problems.
Table of Contents (16 chapters)
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Quality automation

There is no arbitrary scale that can say definitely if some code's quality is bad or good. Unfortunately, the abstract concept of code quality cannot be measured and expressed in the form of numbers. Instead, we can measure various metrics of the software that are known to be highly correlated with the quality of code. The following are a few:

  • The percentage of code covered by tests
  • The number of code style violations
  • The amount of documentation
  • Complexity metrics, such as McCabe's cyclomatic complexity
  • The number of static code analysis warnings

Many projects use code quality testing in their continuous integration workflows. A good and popular approach is to test at least the basic metrics (test coverage, static code analysis, and code style violations) and not allow the merging of any code to the main branch that scores poorly on these metrics.

In the following sections, we will discuss some interesting...