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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 CSV files

Some text files contain tabular data separated by commas. This is a convenient way of creating structured data, instead of using proprietary, more complex binary formats such as Excel or others. These files are called Comma Separated Values or CSV files, and most spreadsheet packages allow us to work directly with them.

Getting ready

We've prepared a CSV file using the data for the top 10 movies by theatre attendance, as described by this page: http://www.mrob.com/pub/film-video/topadj.html.

We copied the first 10 elements of the table into a spreadsheet program (Numbers) and exported the file as a CSV. The file is available in the GitHub repository in the Chapter04/documents directory as top_films.csv:

Figure 4.1: Content of the CSV file

How to do it...

  1. Import the csv module:
    >>> import csv
    
  2. Open the file, create a reader, and iterate through it to show the tabular data of all rows (only...
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