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Mastering Object-oriented Python

You're reading from   Mastering Object-oriented Python If you want to master object-oriented Python programming this book is a must-have. With 750 code samples and a relaxed tutorial, it's a seamless route to programming Python.

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Product type Paperback
Published in Apr 2014
Publisher Packt
ISBN-13 9781783280971
Length 634 pages
Edition Edition
Languages
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Author (1):
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Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
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Table of Contents (26) Chapters Close

Mastering Object-oriented Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Some Preliminaries
1. The __init__() Method FREE CHAPTER 2. Integrating Seamlessly with Python Basic Special Methods 3. Attribute Access, Properties, and Descriptors 4. The ABCs of Consistent Design 5. Using Callables and Contexts 6. Creating Containers and Collections 7. Creating Numbers 8. Decorators and Mixins – Cross-cutting Aspects 9. Serializing and Saving – JSON, YAML, Pickle, CSV, and XML 10. Storing and Retrieving Objects via Shelve 11. Storing and Retrieving Objects via SQLite 12. Transmitting and Sharing Objects 13. Configuration Files and Persistence 14. The Logging and Warning Modules 15. Designing for Testability 16. Coping With the Command Line 17. The Module and Package Design 18. Quality and Documentation Index

Dumping and loading with CSV


The csv module encodes and decodes simple list or dict instances into the CSV notation. As with the json module, discussed previously, this is not a very complete persistence solution. The wide adoption of CSV files, however, means that it often becomes necessary to convert between Python objects and CSV.

Working with CSV files involves a manual mapping between our objects and CSV structures. We need to design the mapping carefully, remaining cognizant of the limitations of the CSV notation. This can be difficult because of the mismatch between the expressive powers of objects and the tabular structure of a CSV file.

The content of each column of a CSV file is—by definition—pure text. When loading data from a CSV file, we'll need to convert these values to more useful types inside our applications. This conversion can be complicated by the way spreadsheets perform unexpected type coercion. We might, for example, have a spreadsheet where US ZIP codes have been changed...

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