Data Cleaning and Transformation

Fix dates that won't sort, names with stray spaces, half-empty columns, and every other mess that real data throws at you.

Toolkit 7 lessons ~65 min

Lessons in this module

1. Handling missing values · 8 min
   NaN, dropna, fillna — what's missing and what to do about it.
2. Finding and removing duplicates · 6 min
   duplicated, drop_duplicates, and how to decide which copy to keep.
3. String cleanup at scale · 8 min
   str.strip, str.lower, str.replace, regex — apply to a whole column.
4. Working with dates in pandas · 9 min
   to_datetime, .dt accessor, date arithmetic, resampling.
5. Numeric cleanup — currencies, percentages, outliers · 7 min
   Strip $ and %, clip outliers, fix obvious typos.
6. Reshape — melt and pivot · 8 min
   Turn wide to long and back. The shape-shifting move you'll thank yourself for.
7. Categoricals and ordered types · 6 min
   Save memory and enable ordered comparisons.