Cleaning messy data

Cleaning is most of the job: missing values, duplicate rows, inconsistent categories, and columns that arrived as strings.

What this lesson covers

• Handle missing values without silently distorting results
• Remove duplicates and normalise categories
• Convert columns to the correct dtype

This lesson sits in pandas Essentials, part of Python for Data Work. It assumes what came before it and leads directly into the next lesson in the module.