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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

Getting a first look at your data

We will work with two datasets in this chapter: The National Longitudinal Survey of Youth for 1997, a survey conducted by the United States government that surveyed the same group of individuals from 1997 through 2023; and the counts of COVID-19 cases and deaths by country from Our World in Data.

Getting ready…

We will mainly be using the pandas library for this recipe. We will use pandas tools to take a closer look at the National Longitudinal Survey (NLS) and COVID-19 case data.

Data note

The NLS of Youth was conducted by the United States Bureau of Labor Statistics. This survey started with a cohort of individuals in 1997 who were born between 1980 and 1985, with annual follow-ups each year through to 2023. For this recipe, I pulled 89 variables on grades, employment, income, and attitudes toward government from the hundreds of data items in the survey. Separate files for SPSS, Stata, and SAS can be downloaded from...

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