Cleaning messy data

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

32m
Beginner

Video by Corey Schafer · YouTube

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Overview

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.

In this lesson you will:

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

Resources

pandas documentationThe official reference for the tools used in this lesson.