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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

8.1 Description

Data validation is a common requirement when moving data between applications. It is extremely helpful to have a clear definition of what constitutes valid data. It helps even more when the definition exists outside a particular programming language or platform.

We can use the JSON Schema (https://json-schema.org) to define a schema that applies to the intermediate documents created by the acquisition process. Using JSON Schema enables the confident and reliable use of the JSON data format.

The JSON Schema definition can be shared and reused within separate Python projects and with non-Python environments, as well. It allows us to build data quality checks into the acquisition pipeline to positively affirm the data really fit the requirements for analysis and processing.

Additional metadata provided with a schema often includes the provenance of the data and details on how attribute values are derived. This isn’t a formal part of a JSON Schema, but we can add some...

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