I need to compute distribution statistics (min, max, mean, median, spread) for all numeric columns in the dataset. From the evidence, I can see that step-7 gave me the column metadata for the `cleandata` table, which shows many numeric columns (BIGINT type). Let me write a single SQL query that computes these statistics for all numeric columns across all tables in one go. ```json { "action": "tool_call", "reasoning": "I need to compute min, max, mean, median, and spread (stddev) for all numeric columns. From step-7, I can see cleandata has many BIGINT columns. I'll write one comprehensive query that aggregates statistics for all numeric columns across the main tables, using UNION ALL to combine results from each table.", "tool": "query_sql", "args": { "sql": "SELECT 'cleandata' as table_name, 'f_boxer_age' as column_name, MIN(f_boxer_age) as min_val, MAX(f_boxer_age) as max_val, AVG(f_boxer_age) as mean_val, MEDIAN(f_boxer_age) as median_val, STDDEV(f_boxer_age) as spread FROM raw.cleandata UNION ALL SELECT 'cleandata', 'f_boxer_height', MIN(f_boxer_height), MAX(f_boxer_height), AVG(f_boxer_height), MEDIAN(f_boxer_height), STDDEV(f_boxer_height) FROM raw.cleandata UNION ALL SELECT 'cleandata', 'f_boxer_reach', MIN(f_boxer_reach), MAX(f_boxer_reach), AVG(f_boxer_reach), MEDIAN(f_boxer_reach), STDDEV(f_boxer_reach) FROM raw.cleandata UNION ALL SELECT 'cleandata', 'f_boxer_won', MIN(f_boxer_won), MAX(f_boxer_won), AVG(f_boxer_won), MEDIAN(f_boxer_won), STDDEV(f_boxer_won) FROM raw.cleandata UNION ALL SELECT 'cleandata', 'f_boxer_lost', MIN(f_boxer_lost), MAX(f_boxer_lost), AVG(f_boxer_lost), MEDIAN(f_boxer_lost), STDDEV(f_boxer_lost) FROM raw.cleandata UNION ALL SELECT 'cleandata', 'f_boxer_drawn', MIN(f_boxer_drawn), MAX(f_boxer_drawn), AVG(f_boxer_drawn), MEDIAN(f_boxer_drawn), STDDEV(f_boxer_drawn) FROM raw.cleandata UNION ALL SELECT 'cleandata', 'f_boxer_KOs', MIN(f_boxer_KOs), MAX(f_boxer_KOs), AVG(f_boxer_KOs), MEDIAN(f_boxer_KOs), STDDEV(f_boxer_KOs) FROM raw.cleandata UNION ALL SELECT 'cleandata', 'f_boxer_ranking', MIN(f_boxer_ranking), MAX(f_boxer_ranking), AVG(f_boxer_ranking), MEDIAN(f_boxer_ranking), STDDEV(f_boxer_ranking) FROM raw.cleandata UNION ALL SELECT 'cleandata', 's_boxer_ranking', MIN(s_boxer_ranking), MAX(s_boxer_ranking), AVG(s_boxer_ranking), MEDIAN(s_boxer_ranking), STDDEV(s_boxer_ranking) FROM raw.cleandata UNION ALL SELECT 'cleandata', 's_boxer_age', MIN(s_boxer_age), MAX(s_boxer_age), AVG(s_boxer_age), MEDIAN(s_boxer_age), STDDEV(s_boxer_age) FROM raw.cleandata UNION ALL SELECT 'cleandata', 's_boxer_reach', MIN(s_boxer_reach), MAX(s_boxer_reach), AVG(s_boxer_reach), MEDIAN(s_boxer_reach), STDDEV(s_boxer_reach) FROM raw.cleandata UNION ALL SELECT 'cleandata', 's_boxer_won', MIN(s_boxer_won), MAX(s_boxer_won), AVG(s_boxer_won), MEDIAN(s_boxer_won), STDDEV(s_boxer_won) FROM raw.cleandata UNION ALL SELECT 'cleandata', 's_boxer_lost', MIN(s_boxer_lost), MAX(s_boxer_lost), AVG(s_boxer_lost), MEDIAN(s_boxer_lost), STDDEV(s_boxer_lost) FROM raw.cleandata UNION ALL SELECT 'cleandata', 's_boxer_drawn', MIN(s_boxer_drawn), MAX(s_boxer_drawn), AVG(s_boxer_drawn), MEDIAN(s_boxer_drawn), STDDEV(s_boxer_drawn) FROM raw.cleandata UNION ALL SELECT 'cleandata', 's_boxer_KOs', MIN(s_boxer_KOs), MAX(s_boxer_KOs), AVG(s_boxer_KOs), MEDIAN(s_boxer_KOs), STDDEV(s_boxer_KOs) FROM raw.cleandata UNION ALL SELECT 'cleandata', 'matchRounds', MIN(matchRounds), MAX(matchRounds), AVG(matchRounds), MEDIAN(matchRounds), STDDEV(matchRounds) FROM raw.cleandata ORDER BY table_name, column_name", "purpose": "Compute distribution statistics (min, max, mean, median, stddev) for all numeric columns in cleandata table", "source": "raw" } } ```