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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

Functions for checking overall data quality

We can tighten up our data quality checks by being more explicit and upfront about what we are evaluating. We likely have some expectations about the distribution of variable values, about the range of allowable values, and about the number of missing values very early in a data analysis project. This may come from documentation, our knowledge of the underlying real-world processes represented by the data, or our understanding of statistics. It is a good idea to have a routine for delineating those initial assumptions, testing them, and then revising assumptions throughout a project. This recipe will demonstrate what that process might look like.

We set up data quality targets for each variable of interest. This includes allowable values and thresholds for missing values for categorical variables. It also includes ranges of values; missing value, skewness, and kurtosis thresholds; and checking for outliers for numeric values. We will...

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