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Python Real-World Projects

You're reading from  Python Real-World Projects

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781803246765
Pages 478 pages
Edition 1st Edition
Languages
Author (1):
Steven F. Lott Steven F. Lott
Profile icon Steven F. Lott

Table of Contents (20) Chapters

Preface 1. Chapter 1: Project Zero: A Template for Other Projects 2. Chapter 2: Overview of the Projects 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

Chapter 8
Project 2.5: Schema and Metadata

It helps to keep the data schema separate from the various applications that share the schema. One way to do this is to have a separate module with class definitions that all of the applications in a suite can share. While this is helpful for a simple project, it can be awkward when sharing data schema more widely. A Python language module is particularly difficult for sharing data outside the Python environment.

This project will define a schema in JSON Schema Notation, first by building pydantic class definitions, then by extracting the JSON from the class definition. This will allow you to publish a formal definition of the data being created. The schema can be used by a variety of tools to validate data files and assure that the data is suitable for further analytical use.

The schema is also useful for diagnosing problems with data sources. Validator tools like jsonschema can provide detailed error reports that can help identify changes...

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