Guided Notes
Cleaning Empty Cells
Finding missing values, removing rows with dropna, and filling gaps with fillna, mean, median, and mode.
Why data needs cleaning
Slide 1 / 5Bad data leads to wrong answers. The four most common problems are:
• Empty cells — values that are missing. • Data in the wrong format — dates stored as text, numbers with symbols. • Wrong data — values that can't be true, like a 450-minute workout. • Duplicates — the same row recorded twice.
This chapter handles empty cells; the next handles the other three.