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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

7.5 Extras

Here are some ideas for you to add to the projects in this chapter.

7.5.1 Markdown cells with dates and data source information

A minor feature of an inspection notebook is some identification of the date, time, and source of the data. It’s sometimes clear from the context what the data source is; there may, for example, be an obvious path to the data.

However, in many cases, it’s not perfectly clear what file is being inspected or how it was acquired. As a general solution, any processing application should produce a log. In some cases, a metadata file can include the details of the processing steps.

This additional metadata on the source and processing steps can be helpful when reviewing a data inspection notebook or sharing a preliminary inspection of data with others. In many cases, this extra data is pasted into ordinary markdown cells. In other cases, this data may be the result of scanning a log file for key INFO lines that summarize processing.

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