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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

Classes that contain the logic for updating Series values

We sometimes work with a particular dataset for an extended period of time, occasionally years. The data might be updated regularly, for a new month or year, or with additional individuals, but the data structure might be fairly stable. If that dataset also has a large number of columns, we might be able to improve the reliability and readability of our code by implementing classes.

When we create classes, we define the attributes and methods of objects. When I use classes for my data-cleaning work, I tend to conceptualize a class as representing my unit of analysis. So, if my unit of analysis is a student, then I have a student class. Each instance of a student created by that class might have birth date and gender attributes and a course registration method. I might also create a subclass for alumni that inherits methods and attributes from the student class.

Data cleaning for the NLS DataFrame could be implemented...

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