Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI

Arrow left icon
Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Length 486 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas FREE CHAPTER 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data 3. Taking the Measure of Your Data 4. Identifying Outliers in Subsets of Data 5. Using Visualizations for the Identification of Unexpected Values 6. Cleaning and Exploring Data with Series Operations 7. Identifying and Fixing Missing Values 8. Encoding, Transforming, and Scaling Features 9. Fixing Messy Data When Aggregating 10. Addressing Data Issues When Combining DataFrames 11. Tidying and Reshaping Data 12. Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines 13. Index

Fixing many-to-many relationships

We sometimes have to work with a data table that was created from a many-to-many merge. This is a merge where merge-by column values are duplicated on both the left and right sides. As we discussed in the previous chapter, many-to-many relationships in a data file often represent multiple one-to-many relationships where the one side has been removed. There is a one-to-many relationship between dataset A and dataset B, and also a one-to-many relationship between dataset A and dataset C. The problem we sometimes have is that we receive a data file with B and C merged but with A excluded.

The best way to work with data structured in this way is to recreate the implied one-to-many relationships, if possible. We do this by first creating a dataset structured like A; that is, how A is likely structured given the many-to-many relationship we see between B and C. The key to being able to do this is to identify a good merge-by column for the data on both...

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 $19.99/month. Cancel anytime
Banner background image