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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 one-to-many merges

In one-to-many merges, there are unduplicated values for the merge-by column or columns on the left data table and duplicated values for those columns on the right data table. For these merges, we usually do either an inner join or a left join. Which of those two join types we use matters when merge-by values are missing on the right data table. When performing a left join, all the rows that would be returned from an inner join will be returned, plus one row for each merge-by value present on the left dataset, but not the right. For those additional rows, values for all the columns on the right dataset will be missing in the resulting merged data. This relatively straightforward fact ends up mattering a fair bit and should be thought through carefully before you code a one-to-many merge.

This is where I start to get nervous, and where I think it makes sense to be a little nervous. When I do workshops on data cleaning, I pause before starting this topic...

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