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Quiz: Data Cleaning

5 questionsNo time limitPass: 60%+10 XP on first pass

Real data is messy. Find and fix missing values, duplicates, wrong data types, inconsistent text and dates with pandas 3 — using a messy sign-up dataset as a case study, plus the copy-on-write rule that changed in pandas 3.

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  1. Question 1

    A CGPA column contains "six" among numbers. What safely converts it?

  2. Question 2

    Why normalise text before removing duplicates?

  3. Question 3

    Which fill value resists outliers for a numeric column?

  4. Question 4

    In pandas 3, you change a filtered selection: toppers["cgpa"] = 10. What happens to the original df?

  5. Question 5

    What does pd.to_datetime(col, errors="coerce") do with "not known"?