· intermediate
Cleaning and Reshaping Data with pandas
Real data arrives broken. Missing values, wrong types, duplicates, inconsistent text and dates that are strings. Fix each deliberately, then reshape the result into whatever a report needs.
Written in house, from the work rather than from a syllabus. Every example is one we would use ourselves, and every exercise is checked against the same engine that grades your answer.
What you’ll learn
- Quantify missing data and choose between dropping and filling
- Fix dtypes, including numbers that arrived as text
- Find duplicates at the level that matters, not just exact copies
- Standardise inconsistent text and parse dates safely
- Reshape between long and wide with pivot and melt
- Write a cleaning pipeline that reports what it changed
Before you start
- · Data Analysis with pandas
- · Exploratory Analysis in SQL is a useful companion but not required