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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
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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

Aggregating results

Once the data is cleaned, we can process the results. For our example, we will calculate the average sale price by both location and day, as well as the total sales by both location and day in the data range. As our data is stored by location, this will be done in two steps. First, we'll create the files per location, and then by date, using the date on the location results.

Getting ready

We will use the resulting CSV file from the previous recipe that receives and transforms logs in the following format:

[<Timestamp>] - SALE - PRODUCT: <product id> - PRICE: <price>

Each line will represent a sale log.

We will use the parse module and the delorean module. We should install the modules, adding them to our requirements.txt file as follows:

$ echo "parse==1.14.0" >> requirements.txt
$ echo "delorean==1.0.0" >> requirements.txt
$ pip install -r requirements.txt

In the GitHub...

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