Python Data Cleaning Cookbook

Table of Contents

1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas
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