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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

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 relaxedly, 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 off on it 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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