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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

15.2 Approach

We’ll take some guidance from the C4 model ( https://c4model.com) when looking at our approach:

  • Context: For this project, a context diagram would show a user creating analytical reports. You may find it helpful to draw this diagram.

  • Containers: There only seems to be one container: the user’s personal computer.

  • Components: We’ll address the components below.

  • Code: We’ll touch on this to provide some suggested directions.

The heart of this application is a module to summarize data in a way that lets us test whether it fits the expectations of a model. The statistical model is a simplified reflection of the underlying real-world processes that created the source data. The model’s simplifications include assumptions about events, measurements, internal state changes, and other details of the processing being observed.

For very simple cases — like Anscombe’s Quartet data — there are only two variables, which leaves...

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