I need to profile all numeric columns in the dataset. From the card, I can see the raw.boxing_matches table has many numeric columns (measures). Let me write a single comprehensive SQL query to get min, max, mean, median, and standard deviation for all of them at once. ```json { "action": "tool_call", "reasoning": "The dataset has one table (raw.boxing_matches) with 22 numeric columns listed as measures. I'll write one SELECT with aggregates for all numeric columns to get their distributions (min, max, mean, median, stddev) in a single query rather than querying each column individually.", "tool": "query_sql", "args": { "sql": "SELECT 'age_A' as column_name, MIN(age_A) as min_val, MAX(age_A) as max_val, AVG(age_A) as mean_val, MEDIAN(age_A) as median_val, STDDEV(age_A) as stddev_val FROM raw.boxing_matches UNION ALL SELECT 'age_B', MIN(age_B), MAX(age_B), AVG(age_B), MEDIAN(age_B), STDDEV(age_B) FROM raw.boxing_matches UNION ALL SELECT 'height_A', MIN(height_A), MAX(height_A), AVG(height_A), MEDIAN(height_A), STDDEV(height_A) FROM raw.boxing_matches UNION ALL SELECT 'height_B', MIN(height_B), MAX(height_B), AVG(height_B), MEDIAN(height_B), STDDEV(height_B) FROM raw.boxing_matches UNION ALL SELECT 'reach_A', MIN(reach_A), MAX(reach_A), AVG(reach_A), MEDIAN(reach_A), STDDEV(reach_A) FROM raw.boxing_matches UNION ALL SELECT 'reach_B', MIN(reach_B), MAX(reach_B), AVG(reach_B), MEDIAN(reach_B), STDDEV(reach_B) FROM raw.boxing_matches UNION ALL SELECT 'weight_A', MIN(weight_A), MAX(weight_A), AVG(weight_A), MEDIAN(weight_A), STDDEV(weight_A) FROM raw.boxing_matches UNION ALL SELECT 'weight_B', MIN(weight_B), MAX(weight_B), AVG(weight_B), MEDIAN(weight_B), STDDEV(weight_B) FROM raw.boxing_matches UNION ALL SELECT 'won_A', MIN(won_A), MAX(won_A), AVG(won_A), MEDIAN(won_A), STDDEV(won_A) FROM raw.boxing_matches UNION ALL SELECT 'won_B', MIN(won_B), MAX(won_B), AVG(won_B), MEDIAN(won_B), STDDEV(won_B) FROM raw.boxing_matches UNION ALL SELECT 'lost_A', MIN(lost_A), MAX(lost_A), AVG(lost_A), MEDIAN(lost_A), STDDEV(lost_A) FROM raw.boxing_matches UNION ALL SELECT 'lost_B', MIN(lost_B), MAX(lost_B), AVG(lost_B), MEDIAN(lost_B), STDDEV(lost_B) FROM raw.boxing_matches UNION ALL SELECT 'drawn_A', MIN(drawn_A), MAX(drawn_A), AVG(drawn_A), MEDIAN(drawn_A), STDDEV(drawn_A) FROM raw.boxing_matches UNION ALL SELECT 'drawn_B', MIN(drawn_B), MAX(drawn_B), AVG(drawn_B), MEDIAN(drawn_B), STDDEV(drawn_B) FROM raw.boxing_matches UNION ALL SELECT 'kos_A', MIN(kos_A), MAX(kos_A), AVG(kos_A), MEDIAN(kos_A), STDDEV(kos_A) FROM raw.boxing_matches UNION ALL SELECT 'kos_B', MIN(kos_B), MAX(kos_B), AVG(kos_B), MEDIAN(kos_B), STDDEV(kos_B) FROM raw.boxing_matches UNION ALL SELECT 'judge1_A', MIN(judge1_A), MAX(judge1_A), AVG(judge1_A), MEDIAN(judge1_A), STDDEV(judge1_A) FROM raw.boxing_matches UNION ALL SELECT 'judge1_B', MIN(judge1_B), MAX(judge1_B), AVG(judge1_B), MEDIAN(judge1_B), STDDEV(judge1_B) FROM raw.boxing_matches UNION ALL SELECT 'judge2_A', MIN(judge2_A), MAX(judge2_A), AVG(judge2_A), MEDIAN(judge2_A), STDDEV(judge2_A) FROM raw.boxing_matches UNION ALL SELECT 'judge2_B', MIN(judge2_B), MAX(judge2_B), AVG(judge2_B), MEDIAN(judge2_B), STDDEV(judge2_B) FROM raw.boxing_matches UNION ALL SELECT 'judge3_A', MIN(judge3_A), MAX(judge3_A), AVG(judge3_A), MEDIAN(judge3_A), STDDEV(judge3_A) FROM raw.boxing_matches UNION ALL SELECT 'judge3_B', MIN(judge3_B), MAX(judge3_B), AVG(judge3_B), MEDIAN(judge3_B), STDDEV(judge3_B) FROM raw.boxing_matches", "purpose": "Get distribution statistics (min, max, mean, median, standard deviation) for all 22 numeric columns in the boxing_matches table", "source": "raw" } } ```