Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Length 486 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas FREE CHAPTER 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data 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

Removing duplicated rows

There are several reasons why we might have data duplicated at the unit of analysis:

  • The existing DataFrame may be the result of a one-to-many merge, and the one side is the unit of analysis.
  • The DataFrame is repeated measures or panel data collapsed into a flat file, which is just a special case of the first situation.
  • We may be working with an analysis file where multiple one-to-many relationships have been flattened, creating many-to-many relationships.

When the one side is the unit of analysis, data on the many side may need to be collapsed in some way. For example, if we are analyzing outcomes for a cohort of students at a college, students are the unit of analysis; but we may also have course enrollment data for each student. To prepare the data for analysis, we might need to first count the number of courses, sum the total credits, or calculate the GPA for each student, before ending up with one row per student. To generalize...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image