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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

14.4 Summary

In this chapter, we have built two important working results of data analysis:

  • Slide decks that can be used as presentations to interested users and stakeholders

  • Reports in PDF format that can be distributed to stakeholders

The line between these two is always hazy. Some presentations have a lot of details and are essentially reports presented in small pages.

Some reports are filled with figures and bullet points; they often seem to be presentations written in portrait mode.

Generally, presentations don’t have the depth of detail reports do. Often, reports are designed for long-term retention and provide background, as well as a bibliography to help readers fill in missing knowledge. Both are first-class parts of a Jupyter notebook and creating these should be part of every analyst’s skills.

This chapter has emphasized the additional tools required to create outstanding results. In the next chapter, we’ll shift gears and look at some of the statistical...

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