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Expert Python Programming – Fourth Edition

Expert Python Programming – Fourth Edition - Fourth Edition

By : Michał Jaworski, Tarek Ziade, Ziadé
4.4 (24)
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Expert Python Programming – Fourth Edition

Expert Python Programming – Fourth Edition

4.4 (24)
By: Michał Jaworski, Tarek Ziade, 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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14
Other Books You May Enjoy
15
Index

Useful testing utilities

When it comes to efficiency in writing tests, it usually boils down to handling all those mundane or inconvenient problems like providing realistic data entries, dealing with time-sensitive processing, or working with remote services. Experienced programmers usually boost their effectiveness with the help of a large collection of small tools for dealing with all these small typical problems. Let's take a look at a few of them.

Faking realistic data values

When writing tests based on input-output data samples, we often need to provide values that have some meaning in our application:

  • Names of people
  • Addresses
  • Telephone numbers
  • Email addresses
  • Identification numbers like tax or social security identifiers

The easiest way around that is to use hardcoded values. We've already done that in the example of our test_send() function in the Mocks and unittest.mock module section:

def test_send()...
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Expert Python Programming – Fourth Edition
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