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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

13.2 Overall approach

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

  • Context: For this project, the context diagram has two use cases: the acquire-to-clean process and this analysis notebook.

  • Containers: There’s one container for analysis application: the user’s personal computer.

  • Components: The software components include the existing analysis models that provide handy definitions for the Python objects.

  • Code: The code is scattered in two places: supporting modules as well as the notebook itself.

A context diagram for this application is shown in Figure 13.1.

Figure 13.1: Context diagram
Figure 13.1: Context diagram

The analyst will often need to share their analytical results with stakeholders. An initial notebook might provide confirmation that some data does not conform to the null hypothesis, suggesting an interesting relationship that deserves deeper exploration. This could be part of justifying a budget allocation to do more...

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