Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python Real-World Projects

You're reading from   Python Real-World Projects Craft your Python portfolio with deployable applications

Arrow left icon
Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803246765
Length 478 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
Arrow right icon
View More author details
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

6.1 Description

When confronted with raw data acquired from a source application, database, or web API, it’s prudent to inspect the data to be sure it really can be used for the desired analysis. It’s common to find that data doesn’t precisely match the given descriptions. It’s also possible to discover that the metadata is out of date or incomplete.

The foundational principle behind this project is the following:

We don’t always know what the actual data looks like.

Data may have errors because source applications have bugs. There could be ”undocumented features,” which are similar to bugs but have better explanations. There may have been actions made by users that have introduced new codes or status flags. For example, an application may have a ”comments” field on an accounts-payable record, and accounting clerks may have invented their own set of coded values, which they put in the last few characters of this field. This...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at R$50/month. Cancel anytime