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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

New in the Second Edition

Readers of the first edition will recognize that this book is substantially longer than that one. That is partly because there are two new chapters—a chapter devoted to treating missing values and another one on pre-processing data for predictive analysis. The insufficient coverage of missing values, and the absence of coverage of pre-processing data for machine learning applications were important omissions. The pre-processing coverage is further improved by new recipes on data pipelines in the final chapter that take the reader from raw data to model evaluation.

The recipes in all chapters have been revised. This is to make sure that they all work well with the most recent versions of pandas. pandas went from version 1.5.3 to 2.2.1 during the writing of this book. I have tried to make sure that all code works fine on all versions of pandas released from January 2023 through February 2024. Since AI tools are becoming increasingly common in our work, I have included discussion of OpenAI tools in four of the chapters. Altogether, 22 of the 82 recipes are new. All of the datasets used have also been updated.

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