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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

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

Summary

We carefully examined combining data vertically, also known as concatenating, and combining data horizontally, also known as merging, in this chapter. We went over key data issues when concatenating data, including different columns across files. We also considered key issues with merging data, such as missing merge-by column values and the unexpected duplication of data. We looked at how those issues change with the type of join used. In the next chapter, we will learn about tidying and reshaping messy data.

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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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