First, clarify the table schemas and how to join them (e.g., member_id). Then, write a SQL query that aggregates total paid amount per specialty, and use a window function or subquery to compute each specialty's share of the overall total. Finally, explain how you would handle potential data issues like missing specialties or duplicate claims.
Pro tip: Mention that you would validate the join to ensure no claims are dropped or duplicated, and consider using a CTE for readability. Also, discuss how you would handle NULL specialties by grouping them into an 'Unknown' category.
Identify the relevant columns in the claims and member tables, such as member_id, specialty, and paid_amount. Confirm the join key and any filters needed (e.g., claim status).
Write a JOIN (likely INNER or LEFT) between claims and members on member_id to associate each claim with a member's specialty. Be mindful of potential duplicates if a member has multiple specialties.
Use GROUP BY specialty and SUM(paid_amount) to compute the total paid for each specialty. Consider using COALESCE to handle NULL specialties.
Calculate the overall total paid using a window function like SUM(SUM(paid_amount)) OVER () or a subquery. Then divide each specialty's total by the overall total to get the share.
Round the share to a reasonable number of decimals, order by total paid descending, and sanity-check that shares sum to 1. Discuss any edge cases like zero total paid.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The tie-handling part is what they actually care about.
Break the problem into three phases: filtering claims to 2017, combining the two member tables with UNION, and joining to claims to count claims per age band. Then determine the maximum count and return all age bands that achieve it, ensuring ties are handled. Use SQL with CTEs or subqueries for clarity and to avoid errors.
Pro tip: Always clarify whether the member tables have identical schemas and whether duplicates should be removed (UNION vs UNION ALL). Also, confirm that 'age band' is a column in the member tables and that the join key is consistent across tables.
Select all claims where the claim date falls within the calendar year 2017. Use a date range or YEAR() function, but be mindful of performance and index usage.
Combine the two member tables using UNION (or UNION ALL if duplicates are acceptable) to create a single member dataset. Ensure both tables have the same columns and data types.
Join the filtered claims to the unioned member table on the member ID. This associates each claim with the member's age band.
Group by age band and count the number of claims. Use COUNT(*) or COUNT(claim_id) depending on the grain.
Identify the maximum claim count and return all age bands that have that count. Use a subquery or window function like RANK() or DENSE_RANK() to handle ties.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the cleanest recode.
Walk through a clear, step-by-step Pandas solution: first recode the gender column using map or replace, then filter the DataFrame to 2017, extract the month from the date column, and finally group by gender and month to sum the paid amount. Emphasize data quality checks and explain each transformation so the interviewer sees your thought process, not just the final code.
Pro tip: Mention that you would validate the recoding by checking unique values before and after, and handle potential missing or unexpected categories (e.g., 'Unknown') to avoid silent data loss. Also, note that using .dt.to_period('M') or .dt.month depends on whether you need month names or numeric months for downstream analysis.
Check the gender column's unique values and the date column's dtype. Handle missing values or unexpected categories (e.g., 'U', NaN) by deciding whether to drop, impute, or map them to a separate category.
Use df['gender'] = df['gender'].map({'M': 'male', 'F': 'female'}) or .replace() to recode. Verify the mapping worked by checking unique values again.
Convert the date column to datetime if needed, then filter rows where the year is 2017. Create a new 'month' column using .dt.month or .dt.to_period('M') depending on the desired output format.
Group the filtered DataFrame by ['gender', 'month'] and compute the sum of the paid amount column using .groupby().sum() or .agg({'paid_amount': 'sum'}).
Display the resulting DataFrame, check for any anomalies (e.g., missing months, zero sums), and optionally reset the index or pivot for readability. Discuss how you would verify the totals against a known benchmark.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.