Track 14 · Lesson 6 of 12

Cleaning data

Find and handle missing values, duplicates, wrongly typed columns, inconsistent text and implausible values before analysing.

You will be able to

• Count missing values per column and decide whether to fill or drop them
• Find and remove duplicate rows
• Convert text columns to numbers with pd.to_numeric(errors="coerce")
• Normalise text with the str accessor (strip, lower, capitalize)
• Spot implausible values and exclude them with an explicit rule