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Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Modern techniques and Python tools to detect and remove dirty data and extract key insights

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800565661
Length 436 pages
Edition 1st Edition
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Authors (2):
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Michael B Walker Michael B Walker
Author Profile Icon Michael B Walker
Michael B Walker
Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Anticipating Data Cleaning Issues when Importing Tabular Data into pandas 2. Chapter 2: Anticipating Data Cleaning Issues when Importing HTML and JSON into pandas FREE CHAPTER 3. Chapter 3: Taking the Measure of Your Data 4. Chapter 4: Identifying Missing Values and Outliers in Subsets of Data 5. Chapter 5: Using Visualizations for the Identification of Unexpected Values 6. Chapter 6: Cleaning and Exploring Data with Series Operations 7. Chapter 7: Fixing Messy Data when Aggregating 8. Chapter 8: Addressing Data Issues When Combining DataFrames 9. Chapter 9: Tidying and Reshaping Data 10. Chapter 10: User-Defined Functions and Classes to Automate Data Cleaning 11. Other Books You May Enjoy

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 in identifying a good merge-by column for the data on...

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