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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

Doing many-to-many merges

Many-to-many merges have duplicate merge-by values in both the left and right DataFrames. We should only rarely need to do a many-to-many merge. Even when data comes to us in that form, it is often because we are missing the central file in multiple one-to-many relationships. For example, there are donor, donor contributions, and donor contact information data tables, and the last two files contain multiple rows per donor. However, in this case, we do not have access to the donor file, which has a one-to-many relationship with both the contributions and contact information files. This happens more frequently than you may think. People sometimes give us data with little awareness of the underlying structure. When I do a many-to-many merge, it is typically because I am missing some key information rather than because that was how the database was designed.

Many-to-many merges return the Cartesian product of the merge-by column values. So, if a donor ID appears...

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