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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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Toc

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

15.4 Summary

In this chapter we have created a foundation for building and using a statistical model of source data. We’ve looked at the following topics:

  • Designing and building a more complex pipeline of processes for gathering and analyzing data.

  • Some of the core concepts behind creating a statistical model of some data.

  • Use of the built-in statistics library.

  • Publishing the results of the statistical measures.

This application tends to be relatively small. The actual computations of the various statistical values leverage the built-in statistics library and tend to be very small. It often seems like there’s far more programming involved in parsing the CLI argument values, and creating the required output file, than doing the “real work” of this application.

This is a consequence of the way we’ve been separating the various concerns in data acquisition, cleaning, and analysis. We’ve partitioned the work into several, isolated stages along...

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