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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

8

Developing Stunning Graphs

Graphs and images are fantastic ways of presenting complex data in an easy-to-understand way. In this chapter, we will make use of powerful Python libraries to learn how to create all kinds of graphs. We will mainly use matplotlib, a library aimed at displaying data in multiple ways and which can create stunning plots that help transmit and display information, but we will also use plotly.

matplotlib is well known and interacts well with other tools in the Python ecosystem. For example, matplotlib graphs can also be automatically displayed by Jupyter Notebooks as introduced in Chapter 7, Cleaning and Processing Data.

The graphs we'll cover will go from simple bar graphs to line graphs, and combine multiple plots in the same graph, annotate them, and even draw geographical maps.

The following recipes will be covered in this chapter:

  • Plotting a simple sales graph
  • Drawing stacked bars
  • Displaying multiple lines
  • Drawing a scatter plot
  • Visualizing map info...
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Python Automation Cookbook
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