```json { "action": "tool_call", "reasoning": "I have deal_stage distribution from step-2. Now I need to get the most common values for the other main categorical columns identified in step-0: sales_agent, account, product, sector, office_location, subsidiary_of, series, regional_office, and manager. I'll write a single query that gets top values for each of these columns.", "tool": "query_sql", "args": { "sql": "WITH deal_stage_dist AS (SELECT 'deal_stage' AS column_name, deal_stage AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.sales_pipeline GROUP BY deal_stage), sales_agent_dist AS (SELECT 'sales_agent' AS column_name, sales_agent AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.sales_pipeline GROUP BY sales_agent), account_dist AS (SELECT 'account' AS column_name, account AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.sales_pipeline GROUP BY account), product_dist AS (SELECT 'product' AS column_name, product AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.sales_pipeline GROUP BY product), sector_dist AS (SELECT 'sector' AS column_name, sector AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.accounts GROUP BY sector), office_dist AS (SELECT 'office_location' AS column_name, office_location AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.accounts GROUP BY office_location), subsidiary_dist AS (SELECT 'subsidiary_of' AS column_name, subsidiary_of AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.accounts GROUP BY subsidiary_of), series_dist AS (SELECT 'series' AS column_name, series AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.products GROUP BY series), regional_dist AS (SELECT 'regional_office' AS column_name, regional_office AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.sales_teams GROUP BY regional_office), manager_dist AS (SELECT 'manager' AS column_name, manager AS value, COUNT(*) AS frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) AS percentage FROM raw.sales_teams GROUP BY manager), top_per_column AS (SELECT *, ROW_NUMBER() OVER (PARTITION BY column_name ORDER BY frequency DESC) AS rank FROM (SELECT * FROM deal_stage_dist UNION ALL SELECT * FROM sales_agent_dist UNION ALL SELECT * FROM account_dist UNION ALL SELECT * FROM product_dist UNION ALL SELECT * FROM sector_dist UNION ALL SELECT * FROM office_dist UNION ALL SELECT * FROM subsidiary_dist UNION ALL SELECT * FROM series_dist UNION ALL SELECT * FROM regional_dist UNION ALL SELECT * FROM manager_dist) all_dists) SELECT column_name, value, frequency, percentage FROM top_per_column WHERE rank <= 5 ORDER BY column_name, rank", "purpose": "Get top 5 most common values with frequencies and percentages for all main categorical columns across the dataset", "source": "raw" } } ```