LESSON 2.2
Cleaning messy data
Learn key techniques and workflows in Python for Data Work.
31m
beginner
24,950 students
Overview
Cleaning is most of the job: missing values, duplicate rows, inconsistent categories, and columns that arrived as strings.
In this lesson you will:
- Handle missing values without silently distorting results
- Remove duplicates and normalise categories
- Convert columns to the correct dtype
Pro Tip
Use caching and revalidation wisely to ensure your app stays fast and data remains fresh without unnecessary requests.