Quarterly Customer Activity Ranking

How would you work with a dataset in Pandas and also in SQL?

Using the supplied customer and order data, produce the SQL result below. Include qualifying orders from Q1 2025 and retain customers with no qualifying orders. The same logic should be reproducible in Pandas.

Output

1. One row per customer with customer_id, customer_name, region, completed_orders, total_completed_amount, last_completed_date, activity_level, and region_rank.
2. Include only orders with status completed dated from 2025-01-01 through 2025-03-31.
3. Classify customers as No activity, High, or Standard; rank customers within each region by total amount, with ties sharing a rank.
4. Order by region, region_rank, then customer_id.

Output details

• Rank customers by total_completed_amount in descending order.
• Classify customers with total_completed_amount >= 150 as High; classify other active customers as Standard.
• For customers with zero qualifying orders, set total_completed_amount to 0 and leave last_completed_date as NULL.