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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 10: User-Defined Functions and Classes to Automate Data Cleaning

There are a number of great reasons to write code that is reusable. When we step back from the particular data cleaning problem at hand and consider its relationship to very similar problems, we can actually improve our understanding of the key issues involved. We are also more likely to address a task systematically when we set our sights more on solving it for the long term than on the before-lunch solution. This has the additional benefit of helping us to disentangle the substantive issues from the mechanics of data manipulation.

We will create several modules to accomplish routine data cleaning tasks in this chapter. The functions and classes in these modules are examples of code that can be reused across DataFrames, or for one DataFrame over an extended period of time. These functions handle many of the tasks we discussed in the first nine chapters, but in a manner that allows us to reuse our code.

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