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Python Real-World Projects

You're reading from   Python Real-World Projects Craft your Python portfolio with deployable applications

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803246765
Length 478 pages
Edition 1st 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 (20) Chapters Close

Preface 1. Chapter 1: Project Zero: A Template for Other Projects 2. Chapter 2: Overview of the Projects FREE CHAPTER 3. Chapter 3: Project 1.1: Data Acquisition Base Application 4. Chapter 4: Data Acquisition Features: Web APIs and Scraping 5. Chapter 5: Data Acquisition Features: SQL Database 6. Chapter 6: Project 2.1: Data Inspection Notebook 7. Chapter 7: Data Inspection Features 8. Chapter 8: Project 2.5: Schema and Metadata 9. Chapter 9: Project 3.1: Data Cleaning Base Application 10. Chapter 10: Data Cleaning Features 11. Chapter 11: Project 3.7: Interim Data Persistence 12. Chapter 12: Project 3.8: Integrated Data Acquisition Web Service 13. Chapter 13: Project 4.1: Visual Analysis Techniques 14. Chapter 14: Project 4.2: Creating Reports 15. Chapter 15: Project 5.1: Modeling Base Application 16. Chapter 16: Project 5.2: Simple Multivariate Statistics 17. Chapter 17: Next Steps 18. Other Books You Might Enjoy 19. Index

8.5 Extras

Here are some ideas for you to add to this project.

8.5.1 Revise all previous chapter models to use Pydantic

The previous chapters used dataclass definitions from the dataclasses module. These can be shifted to use the pydantic.dataclasses module. This should have minimal impact on the previous projects.

We can also shift all of the previous acceptance test suites to use a formal schema definition for the source data.

8.5.2 Use the ORM layer

For SQL extracts, an ORM can be helpful. The pydantic module lets an application create Python objects from intermediate ORM objects. This two-layer processing seems complex but permits detailed validation in the Pydantic objects that aren’t handled by the database.

For example, a database may have a numeric column without any range provided. A Pydantic class definition can provide a field definition with ge and le attributes to define a range. Further, Pydantic permits the definition of a unique data type with unique validation...

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