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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 more complicated aggregation functions with groupby

In the previous recipe, we created a groupby DataFrame object and used it to run summary statistics by groups. We use chaining in this recipe to create the groups, choose the aggregation variable(s), and select the aggregation function(s), all in one line. We also take advantage of the flexibility of the groupby object, which allows us to choose the aggregation columns and functions in a variety of ways.

Getting ready

We will work with the National Longitudinal Survey of Youth (NLS) data in this recipe.

Data note

The National Longitudinal Surveys, administered by the United States Bureau of Labor Statistics, are longitudinal surveys of individuals who were in high school in 1997 when the surveys started. Participants were surveyed each year through 2023. The surveys are available for public use at nlsinfo.org.

How to do it…

We do more complicated aggregations with groupby than we...

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