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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

7

Cleaning and Processing Data

Some automated tasks will require dealing with large amounts of data. As data grows, two new and distinct problems appear: processing takes too long and input data quality issues cause more problems.

Both problems are well known in the realm of data science when dealing with large quantities of data, but the problems can appear even at a smaller scale.

The quality of input data is highly related to the number of sources of the data. In general, data from a single source will be more consistent, but using a single source is limiting. And even if the data comes from the same source, it can still contain inconsistencies or errors.

Examples of differences include regional differences, such as different date formats or currencies, extra information, different names for the same concept (including spelling differences), typos, generally poor data quality including errors… The list is long!

To compare apples with apples, the input data will probably need...

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Python Automation Cookbook
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