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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

Technical requirements

You will need pandas, NumPy, and Matplotlib to complete the recipes in this chapter. I used pandas 2.1.4, but the code will run on pandas 1.5.3 or later.

The code in this chapter can be downloaded from the book’s GitHub repository, https://github.com/PacktPublishing/Python-Data-Cleaning-Cookbook-Second-Edition.

You have been reading a chapter from
Python Data Cleaning Cookbook - Second Edition
Published in: May 2024
Publisher: Packt
ISBN-13: 9781803239873
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