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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

Using linear regression to identify data points with significant influence

The remaining recipes in this chapter use statistical modeling to identify outliers. The advantage of these techniques is that they are less dependent on the distribution of the variable of concern, and take more into account than can be revealed in either univariate or bivariate analyses. This allows us to identify outliers that are not otherwise apparent. On the other hand, by taking more factors into account, multivariate techniques may provide evidence that a previously suspect value is actually within an expected range, and provides meaningful information.

In this recipe, we use linear regression to identify observations (rows) that have an out-sized influence on models of a target or dependent variable. This can indicate that one or more values for a few observations are so extreme that they compromise the model fit for all of the other observations.

Getting ready

The code in this recipe requires...

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