I need to identify the main categorical columns and find their most common values with frequencies. From the dataset card, I can see the CLASSIFIERS section lists the categorical columns: - `decision` (~11 distinct values) - `result` (~3 distinct values) - `stance_A` (~3 distinct values) - `stance_B` (~3 distinct values) Let me query the frequency distribution for each of these categorical columns in a single efficient query. ```json { "action": "tool_call", "reasoning": "I'll write one SQL query to get the top values and their frequencies for all four main categorical columns (decision, result, stance_A, stance_B). This is more efficient than querying each column separately.", "tool": "query_sql", "args": { "sql": "SELECT 'decision' as column_name, decision as value, COUNT(*) as frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) as percentage FROM raw.boxing_matches WHERE decision IS NOT NULL GROUP BY decision UNION ALL SELECT 'result' as column_name, result as value, COUNT(*) as frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) as percentage FROM raw.boxing_matches WHERE result IS NOT NULL GROUP BY result UNION ALL SELECT 'stance_A' as column_name, stance_A as value, COUNT(*) as frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) as percentage FROM raw.boxing_matches WHERE stance_A IS NOT NULL GROUP BY stance_A UNION ALL SELECT 'stance_B' as column_name, stance_B as value, COUNT(*) as frequency, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) as percentage FROM raw.boxing_matches WHERE stance_B IS NOT NULL GROUP BY stance_B ORDER BY column_name, frequency DESC", "purpose": "Get the most common values and their frequencies for all main categorical columns (decision, result, stance_A, stance_B)", "source": "raw" } } ```