I need to identify the main categorical columns and find the most common values with their frequencies for each. From the evidence, the main categorical columns are: - event (~7 distinct values) - medal (~3 distinct values) - country (~13 distinct values) - event_type (~3 distinct values) - gender (~3 distinct values) - era (~3 distinct values) Let me query the frequency distribution for all these categorical columns in one efficient query. ```json { "action": "tool_call", "reasoning": "I'll write one SQL query to get the top values and their frequencies for all main categorical columns. This is more efficient than querying each column separately.", "tool": "query_sql", "args": { "sql": "SELECT 'event' as column_name, event as value, COUNT(*) as frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) as percentage FROM raw.olympic_table_tennis_medals_1988_2024 GROUP BY event UNION ALL SELECT 'medal', medal, COUNT(*), ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) FROM raw.olympic_table_tennis_medals_1988_2024 GROUP BY medal UNION ALL SELECT 'country', country, COUNT(*), ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) FROM raw.olympic_table_tennis_medals_1988_2024 GROUP BY country UNION ALL SELECT 'event_type', event_type, COUNT(*), ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) FROM raw.olympic_table_tennis_medals_1988_2024 GROUP BY event_type UNION ALL SELECT 'gender', gender, COUNT(*), ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) FROM raw.olympic_table_tennis_medals_1988_2024 GROUP BY gender UNION ALL SELECT 'era', era, COUNT(*), ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) FROM raw.olympic_table_tennis_medals_1988_2024 GROUP BY era ORDER BY column_name, frequency DESC", "purpose": "Get frequency distribution for all main categorical columns with percentages", "source": "raw" } } ```