First, clarify the de-duplication rule and window definitions, then outline a single-query plan using CTEs with window functions to handle deduplication, rolling revenue, and ranking. Emphasize that the final filter for customers with at least two completed orders must be applied after aggregation, and ensure the query avoids temp tables and correlated subqueries.
Pro tip: Mention that you would validate the de-duplication rule with a quick data profile (e.g., checking for same-day duplicates) and confirm whether the 7-day window is inclusive of the current day, as these details often trip up candidates.
Ask about the exact de-duplication rule (e.g., keep the latest order per customer per day), the definition of 'completed' orders, and whether the 3-day and 7-day windows are rolling and inclusive. Confirm that the final output should only include customers with at least two completed orders in the 7-day window.
Use a window function like ROW_NUMBER() partitioned by customer and order date, ordered by a timestamp or order ID, to select one order per customer per day. This ensures no duplicate same-day orders are counted.
For each kept order, calculate the 3-day rolling revenue sum using a window function with ROWS BETWEEN 2 PRECEDING AND CURRENT ROW, and compute DENSE_RANK() over revenue within the last 7 days using a window frame of RANGE BETWEEN 6 PRECEDING AND CURRENT ROW (or equivalent).
After computing the metrics, apply a filter to include only customers who have at least two completed orders within the 7-day window. This can be done using a COUNT() window function or a HAVING clause in a subquery.
Combine the steps into a single SQL query using CTEs for readability, ensuring no temp tables or correlated subqueries. Walk through the logic with a small example to validate correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, filter the data to the last 7 days and apply same-day de-duplication by keeping the first transaction per customer-product-day (or per transaction ID). Then, aggregate revenue per customer and category, compute total revenue per customer, and derive the top category and its share using vectorized operations like groupby, transform, and idxmax.
Pro tip: Explicitly state your de-duplication rule (e.g., drop_duplicates on customer_id, product_id, date) and justify it; this shows you understand data quality and business logic. Also, mention that you avoid loops by using groupby and transform, which is crucial for scalability.
Filter the DataFrame to the last 7 days based on the date column. Apply same-day de-duplication by dropping duplicate rows per customer, product, and date, keeping the first occurrence.
Group by customer and product category, summing revenue to get total revenue per category per customer. Use groupby and sum with reset_index.
Calculate the total revenue per customer by summing across categories, and merge or broadcast this back to the category-level DataFrame using transform or merge.
For each customer, find the category with the highest revenue (alphabetical tiebreak) using sort_values and drop_duplicates or idxmax. Compute the share as category revenue divided by total revenue.
Select and rename columns to customer_id, total_revenue, top_category, and category_share. Ensure the output is a clean DataFrame with one row per customer.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.