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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 7
Data Inspection Features

There are three broad kinds of data domains: cardinal, ordinal, and nominal. The first project in this chapter will guide you through the inspection of cardinal data; values like weights, measures, and durations where the data is continuous, as well as counts where the data is discrete. The second project will guide reasoners through the inspection of ordinal data involving things like dates, where order matters, but the data isn’t a proper measurement; it’s more of a code or designator. The nominal data is a code that happens to use digits but doesn’t represent numeric values. The third project will cover the more complex case of matching keys between separate data sources.

An inspection notebook is required when looking at new data. It’s a great place to keep notes and lessons learned. It’s helpful when diagnosing problems that arise in a more mature analysis pipeline.

This chapter will cover a number of skills...

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