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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-one merges

The remainder of this chapter will explore combining data horizontally; that is, merging columns from a data table with columns from another data table. Borrowing from SQL development, we typically talk about such operations as join operations: left joins, right joins, inner joins, and outer joins. This recipe examines one-to-one merges, where the merge-by values are unduplicated in both files. Subsequent recipes will demonstrate one-to-many merges, where the merge-by values are duplicated on the right data table, and many-to-many merges, where merge-by values are duplicated on both the left and right data tables.

We often speak of the left and right sides of a merge, a convention that we will follow throughout this chapter. But this is of no real consequence, other than for clarity of exposition. We can accomplish exactly the same thing with a merge if A were the left data table and B were the right data table, as we could if the reverse were true.

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