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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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Toc

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

8.4 Summary

This chapter’s projects have shown examples of the following features of a data acquisition application:

  • Using the Pydantic module for crisp, complete definitions

  • Using JSON Schema to create an exportable language-independent definition that anyone can use

  • Creating test scenarios to use the formal schema definition

Having formalized schema definitions permits recording additional details about the data processing applications and the transformations applied to the data.

The docstrings for the class definitions become the descriptions in the schema. This permits writing details on data provenance and transformation that are exposed to all users of the data.

The JSON Schema standard permits recording examples of values. The Pydantic package has ways to include this metadata in field definitions, and class configuration objects. This can be helpful when explaining odd or unusual data encodings.

Further, for text fields, JSONSchema permits including a format attribute...

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