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Python Automation Cookbook

You're reading from   Python Automation Cookbook 75 Python automation recipes for web scraping; data wrangling; and Excel, report, and email processing

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781800207080
Length 526 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Jaime Buelta Jaime Buelta
Author Profile Icon Jaime Buelta
Jaime Buelta
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Toc

Table of Contents (16) Chapters Close

Preface 1. Let's Begin Our Automation Journey 2. Automating Tasks Made Easy FREE CHAPTER 3. Building Your First Web Scraping Application 4. Searching and Reading Local Files 5. Generating Fantastic Reports 6. Fun with Spreadsheets 7. Cleaning and Processing Data 8. Developing Stunning Graphs 9. Dealing with Communication Channels 10. Why Not Automate Your Marketing Campaign? 11. Machine Learning for Automation 12. Automatic Testing Routines 13. Debugging Techniques 14. Other Books You May Enjoy
15. Index

Reading images

Probably the most common data that is not text is image data. Images have their own set of specific metadata that can be read to filter values or perform other operations.

The main challenge is dealing with multiple formats and different metadata definitions. We'll show in this recipe how to get information from both a JPEG and a PNG, and how the same information can be encoded differently.

Getting ready

The best general toolkit for dealing with images in Python is, arguably, Pillow. This library allows you to easily read files in the most common formats, as well as perform operations on them. Pillow started as a fork of PIL (Python Imaging Library), a previous module that became stagnant some years ago.

We will also use the xmltodict module to transform some data from XML into a more convenient dictionary. We will add both modules to requirements.txt and reinstall them in the virtual environment:

$ echo "Pillow==7.0.0" ...
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