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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

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, for accomplishing 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 30 years ago in SAS, and on select occasions as recently as 7 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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