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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

7.1.2 Approach

This project is based on the initial inspection notebook from Chapter 6, Project 2.1: Data Inspection Notebook. Some of the essential cell content will be reused in this notebook. We’ll add components to the components shown in the earlier chapter – specifically, the samples_iter() function to iterate over samples in an open file. This feature will be central to working with the raw data.

In the previous chapter, we suggested avoiding conversion functions. When starting down the path of inspecting data, it’s best to assume nothing and look at the text values first.

There are some common patterns in the source data values:

  • The values appear to be all numeric values. The int() or float() function works on all of the values. There are two sub-cases here:

    • All of the values seem to be proper counts or measures in some expected range. This is ideal.

    • A few “outlier” values are present. These are values that seem to be outside the expected...

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