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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

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Length 486 pages
Edition 2nd Edition
Languages
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Author (1):
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Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (14) Chapters Close

Preface 1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data FREE CHAPTER 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

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...

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