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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 3: Taking the Measure of Your Data

Within a week of receiving a new dataset, at least one person is likely to ask us a familiar question: "so, how does it look?" This is not always asked in a relaxed tone, and others are not usually excited to hear about all of the red flags we have already found. There might be a sense of urgency to declare the data ready for analysis. Of course, if we sign it off too soon, this can create much larger problems; the presentation of invalid results, the misinterpretation of variable relationships, and having to redo major chunks of our analysis. The key is sorting out what we need to know about the data before we explore anything else in the data. The recipes in this chapter offer techniques for determining if the data is in good enough shape to begin the analysis, so that even if we cannot say, "it looks fine," we can at least say, "I'm pretty sure I have identified the main issues, and here they are."

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