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Python Automation Cookbook

Python Automation Cookbook - Third Edition

By : Jaime Buelta
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Python Automation Cookbook

Python Automation Cookbook

By: Jaime Buelta

Overview of this book

Automating repetitive tasks and integrating systems efficiently becomes increasingly complex as workflows scale. This book helps you solve that problem with practical Python recipes that guide you from foundational automation to advanced, AI-powered workflows. You start by building a strong base in Python automation, exploring tested solutions for file handling, web scraping, APIs, testing, and system operations, and learning how to design reliable automation workflows. The cookbook approach enables you to quickly apply solutions to real problems while building a deeper understanding through hands-on practice. This third edition expands the scope of automation by introducing AI-powered capabilities. You learn how to call AI models within your scripts, use and implement the Model Context Protocol (MCP) for system integration, and design intelligent agents that automate decision-making processes. New chapters provide real-world examples of AI agents in business automation, helping you move beyond scripts to adaptive systems. This book combines practical knowledge with modern techniques to ensure you stay current with evolving automation practices. By the end of this book, you will be able to design, build, and extend Python automation workflows, including AI-driven solutions, to handle complex real-world tasks with confidence.
Table of Contents (20 chapters)
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18
Other Books You May Enjoy
19
Index

Testing using dependency mocking

One of the biggest advantages of using Python is having a rich library of resources at our disposal. That includes modules in the standard library, like the csv module to re and write CSV files or the re library to use regexes.

Others can be external, like Beautiful Soup to parse HTML or matplotlib to generate graphs. We can also create our own libraries or structure our code in different files and encapsulate the functionality in a way that's reusable and improves readability.

When creating tests, sometimes using external elements and library calls in the core of the test is not advisable. For example, to test that a report is correctly processed, it may be necessary to read a CSV file that contains the report. But preparing the test by preparing a file, in this case, becomes cumbersome and can lose focus on the actual objective of the test.

In these cases, it can be convenient to simulate those dependencies to simplify the tests or to avoid external...

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Python Automation Cookbook
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