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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

Standardizing the date format

The date time format in the logs is different depending on the location. In the Canadian logs, the format is the standard ISO 8601 in the format YYYY-MM-DD. The logs coming from the USA use the format MM-DD-YYYY. In this recipe, we will append a new column with a standard format to unify both dates.

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. We should install the module, adding it to our requirements.txt file as follows:

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

In the GitHub repository, there are some log files to process with the following structure:

sale_logs/
  OH
    logs.txt
  ON
    logs.txt

The code can be found in the...

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