01
How do you deal with missing or duplicate data?
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Profile the data, remove exact duplicates using keys, impute missing values (mean/median/mode or a business rule), flag imputed rows and document every cleaning step.
Python Interview Questions for Data Analyst
3 Python questions with answers for data analyst roles. Tick what you have solved to track your progress.
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02
Show a Pandas groupby example.
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df.groupby('region')['sales'].agg(['sum', 'mean', 'count'])
# splits by region, aggregates, combines the results
03
How do you detect outliers?
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Visually with a box plot, with the IQR rule (below Q1 − 1.5×IQR or above Q3 + 1.5×IQR) or a z-score above 3. Then decide with the business whether to cap, remove or keep them.