Data Cleaning With Pandas

Summary

Using Pandas to handle missing values, duplicates, and inconsistent types programmatically — the same cleaning tasks from Excel (Phase 2) and Phase 8, but faster and repeatable via code.

• 1Practice .isnull(), .dropna(), and .fillna() on a dataset with missing values.
• 2Use .drop_duplicates() and verify the row count before and after.
• 3Write a reusable cleaning function you could apply to future similar datasets.