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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

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781803239873

Length 486 pages

Edition 2nd Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Analysis

Author (1):

Michael Walker

View More author details

Table of Contents (14) Chapters

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

Summary

We stepped through a wide range of strategies for aggregating data using NumPy and pandas in this chapter. We also discussed advantages and disadvantages of each technique, including how to select the most efficient and intuitive approach given your data and the aggregation task. Since most data cleaning and manipulation projects will involve some splitting-applying-combining, it is a good idea to become comfortable with each of these approaches. In the next chapter, we will learn how to combine DataFrames and deal with subsequent data issues.

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Authors (1)

Michael Walker

Michael Walker

Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006.

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