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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

Chapter 9: Tidying and Reshaping Data

As Leo Tolstoy and Hadley Wickham tell us, all tidy data is fundamentally alike, but all untidy data is messy in its own special way. How many times have we all stared at some rows of data and thought, "what..... how...... why did they do that?" This overstates the case somewhat. Although there are many ways that data can be poorly structured, there are limits to human creativity in this regard. It is possible to categorize the most frequent ways in which datasets deviate from normalized or tidy forms.

This was Hadley Wickham's observation in his seminal work on tidy data. We can lean on that work, and our own experiences with oddly structured data, to prepare for the reshaping we have to do. Untidy data often has one or more of the following characteristics: a lack of clarity about merge-by column relationships; data redundancy on the one side of one-to-many relationships; data redundancy due to many-to-many relationships; values...

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