Knew the mechanics well enough but I second-guessed myself on whether to use an inner join or left join here.
First, clarify the schema and assumptions (e.g., column names, data types, handling of missing values). Then, walk through the pandas operations step-by-step: merge with how='inner', group by country, aggregate total and average spend, sort descending. Finally, mention validation checks and potential edge cases.
Pro tip: Always validate the merge to ensure no unintended row duplication or loss, and explicitly state the join type. Also, consider whether average spend should be computed as total spend divided by number of transactions or number of unique users—clarify with the interviewer.
Ask about column names, data types, and any specific definitions (e.g., 'spend' column). Confirm that 'matched rows' means an inner join and that average spend is per transaction.
Use pd.merge(users, transactions, on='user_id', how='inner') to keep only rows with matching user_id in both DataFrames.
Group by 'country' and compute total spend (sum) and average spend (mean) using .agg({'spend': ['sum', 'mean']}) or named aggregation.
Sort the resulting DataFrame by total spend in descending order using .sort_values(by='total_spend', ascending=False). Optionally, reset the index or rename columns for clarity.
Check for missing values, duplicate user_ids, or negative spend. Mention how you would handle them (e.g., drop duplicates, filter out negatives) and verify the output shape.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.