Data Cleaning & Wrangling

Summary

Handling missing values, duplicates, inconsistent types and messy formats programmatically — the unglamorous work that determines whether any downstream model can be trusted.

• 1Practice .isnull(), .dropna(), .fillna() and .drop_duplicates() on a messy dataset.
• 2Write a reusable cleaning function you could apply to future similar datasets.
• 3Document every cleaning decision, since it materially affects your model's results later.