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  • Book Overview & Buying Python Automation Cookbook
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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

Extracting data from structured strings

In a lot of automated tasks, we'll need to treat input text structured in a known format and extract the relevant information. For example, a spreadsheet may define a percentage in a piece of text (such as 37.4%) and we want to retrieve it in a numerical format to apply it later (0.374, as a float).

In this recipe, we'll learn how to process sale logs that contain inline information about a product, such as whether it has been sold, its price, profit made, and other information.

Getting ready

Imagine that we need to parse information stored in sales logs. We'll use a sales log with the following structure:

[<Timestamp in iso format>] - SALE - PRODUCT: <product id> - PRICE: $<price of the sale>

For example, a specific log may look like this:

[2018-05-05T10:58:41.504054] - SALE - PRODUCT: 1345 - PRICE: $09.99

Note that the price has a leading zero. All prices will...

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Python Automation Cookbook
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