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Mastering Object-Oriented Python

You're reading from   Mastering Object-Oriented Python Build powerful applications with reusable code using OOP design patterns and Python 3.7

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Product type Paperback
Published in Jun 2019
Publisher Packt
ISBN-13 9781789531367
Length 770 pages
Edition 2nd Edition
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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 (25) Chapters Close

Preface 1. Section 1: Tighter Integration Via Special Methods FREE CHAPTER
2. Preliminaries, Tools, and Techniques 3. The __init__() Method 4. Integrating Seamlessly - Basic Special Methods 5. Attribute Access, Properties, and Descriptors 6. The ABCs of Consistent Design 7. Using Callables and Contexts 8. Creating Containers and Collections 9. Creating Numbers 10. Decorators and Mixins - Cross-Cutting Aspects 11. Section 2: Object Serialization and Persistence
12. Serializing and Saving - JSON, YAML, Pickle, CSV, and XML 13. Storing and Retrieving Objects via Shelve 14. Storing and Retrieving Objects via SQLite 15. Transmitting and Sharing Objects 16. Configuration Files and Persistence 17. Section 3: Object-Oriented Testing and Debugging
18. Design Principles and Patterns 19. The Logging and Warning Modules 20. Designing for Testability 21. Coping with the Command Line 22. Module and Package Design 23. Quality and Documentation 24. Other Books You May Enjoy

Dumping and loading with CSV

The csv module encodes and decodes simple list or dict instances into a 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 potentially complex Python objects and very simplistic 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 into more useful types inside our applications. This...

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