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Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Modern techniques and Python tools to detect and remove dirty data and extract key insights

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800565661
Length 436 pages
Edition 1st Edition
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Authors (2):
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Michael B Walker Michael B Walker
Author Profile Icon Michael B Walker
Michael B Walker
Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Anticipating Data Cleaning Issues when Importing Tabular Data into pandas 2. Chapter 2: Anticipating Data Cleaning Issues when Importing HTML and JSON into pandas FREE CHAPTER 3. Chapter 3: Taking the Measure of Your Data 4. Chapter 4: Identifying Missing Values and Outliers in Subsets of Data 5. Chapter 5: Using Visualizations for the Identification of Unexpected Values 6. Chapter 6: Cleaning and Exploring Data with Series Operations 7. Chapter 7: Fixing Messy Data when Aggregating 8. Chapter 8: Addressing Data Issues When Combining DataFrames 9. Chapter 9: Tidying and Reshaping Data 10. Chapter 10: User-Defined Functions and Classes to Automate Data Cleaning 11. Other Books You May Enjoy

Using groupby to change the unit of analysis of a DataFrame

The DataFrame that we created in the last step of the previous recipe was something of a fortunate by-product of our efforts to generate multiple summary statistics by groups. There are times when we really do need to aggregate data to change the unit of analysis—say, from monthly utility expenses per family to annual utility expenses per family, or from students' grades per course to students' overall grade point average (GPA).

groupby is a good tool for collapsing the unit of analysis, particularly when summary operations are required. When we only need to select unduplicated rows—perhaps the first or last row for each individual over a given interval—then the combination of sort_values and drop_duplicates will do the trick. But we often need to do some calculation across the rows for each group before collapsing. That is when groupby comes in very handy.

Getting ready

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