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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 FREE CHAPTER 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data 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

Selecting and organizing columns

We explore several ways to select one or more columns from your DataFrame in this recipe. We can select columns by passing a list of column names to the [] bracket operator, or by using the pandas-specific loc and iloc data accessors.

When cleaning data or doing exploratory or statistical analyses, it is helpful to focus on the variables that are relevant to the issue or analysis at hand. This makes it important to group columns according to their substantive or statistical relationships with each other, or to limit the columns we are investigating at any one time. How many times have we said to ourselves something like, “Why does variable A have a value of x when variable B has a value of y?” We can only do that when the amount of data we are viewing at a given moment does not exceed our perceptive abilities at that moment.

Getting ready…

We will continue working with the National Longitudinal Survey (NLS) data in...

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