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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

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 nicely...

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