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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

Looping through data with itertuples (an anti-pattern)

In this recipe, we will iterate over the rows of a DataFrame and generate our own totals for a variable. In subsequent recipes in this chapter, we will use NumPy arrays, and then some pandas-specific techniques, to accomplish the same tasks.

It may seem odd to begin this chapter with a technique that we are often cautioned against using. But I used to do the equivalent of looping every day 35 years ago in SAS, and on select occasions as recently as 10 years ago in R. That is why I still find myself thinking conceptually about iterating over rows of data, sometimes sorted by groups, even though I rarely implement my code in this manner. I think it is good to hold onto that conceptualization, even when using other pandas methods that work for us more efficiently.

I do not want to leave the impression that pandas-specific techniques are always markedly more efficient either. pandas users probably find themselves using apply...

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