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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

13.5 Extras

Here are some ideas for the reader to add to these projects.

13.5.1 Use Seaborn for plotting

An alternative to the pyplot package is the Seaborn package. This package also provides statistical plotting functions. It provides a wider variety of styling options, permitting more colorful (and perhaps more informative) plots.

See https://seaborn.pydata.org for more information.

This module is based on matplotlib, making it compatible with JupyterLab.

Note that the Seaborn package can work directly with a list-of-dictionary structure. This matches the ND JSON format used for acquiring and cleaning the data.

Using a list-of-dictionary type suggests it might be better to avoid the analysis model structure, and stick with dictionaries created by the clean application. Doing this might sacrifice some model-specific processing and validation functionality.

On the other hand, the pydantic package offers a built-in dict() method that covers a sophisticated analysis model object into...

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