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Python Automation Cookbook

Python Automation Cookbook - Second Edition

By : Jaime Buelta
3.8 (16)
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Python Automation Cookbook

Python Automation Cookbook

3.8 (16)
By: Jaime Buelta

Overview of this book

In this updated and extended version of Python Automation Cookbook, each chapter now comprises the newest recipes and is revised to align with Python 3.8 and higher. The book includes three new chapters that focus on using Python for test automation, machine learning projects, and for working with messy data. This edition will enable you to develop a sharp understanding of the fundamentals required to automate business processes through real-world tasks, such as developing your first web scraping application, analyzing information to generate spreadsheet reports with graphs, and communicating with automatically generated emails. Once you grasp the basics, you will acquire the practical knowledge to create stunning graphs and charts using Matplotlib, generate rich graphics with relevant information, automate marketing campaigns, build machine learning projects, and execute debugging techniques. By the end of this book, you will be proficient in identifying monotonous tasks and resolving process inefficiencies to produce superior and reliable systems.
Table of Contents (16 chapters)
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14
Other Books You May Enjoy
15
Index

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 the task takes too long and input data quality issues cause more problems.

Both problems are well known in the realm of data science dealing with big 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. Even if the data comes from the same source, it could still contain inconsistencies or errors.

Some examples of differences could be regional, such as date formats or currencies, extra information, different names for the same concept (including spelling differences), typos, general bad quality of data with errors… The list is huge!

To compare apples with apples, the input data...

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Python Automation Cookbook
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