I need to compute distribution statistics for all numeric columns in the ai_supercomputers table. From the inspect_columns result, I can see several numeric columns with their types and some basic stats. Let me write a comprehensive SQL query to get min, max, mean, median, and standard deviation for all numeric columns in one query. ```json { "action": "tool_call", "reasoning": "I need to compute distribution statistics (min, max, mean, median, stddev) for all numeric columns. The inspect_columns result shows many numeric columns. I'll write one SELECT with aggregates for all numeric columns to efficiently get all statistics in a single query rather than querying each column separately.", "tool": "query_sql", "args": { "sql": "SELECT 'Max OP/s (log)' as column_name, MIN(\"Max OP/s (log)\") as min_val, MAX(\"Max OP/s (log)\") as max_val, AVG(\"Max OP/s (log)\") as mean_val, MEDIAN(\"Max OP/s (log)\") as median_val, STDDEV(\"Max OP/s (log)\") as stddev_val, COUNT(\"Max OP/s (log)\") as non_null_count FROM raw.ai_supercomputers UNION ALL SELECT 'H100 equivalents', MIN(\"H100 equivalents\"), MAX(\"H100 equivalents\"), AVG(\"H100 equivalents\"), MEDIAN(\"H100 equivalents\"), STDDEV(\"H100 equivalents\"), COUNT(\"H100 equivalents\") FROM raw.ai_supercomputers UNION ALL SELECT 'Chip quantity (primary)', MIN(\"Chip quantity (primary)\"), MAX(\"Chip quantity (primary)\"), AVG(\"Chip quantity (primary)\"), MEDIAN(\"Chip quantity (primary)\"), STDDEV(\"Chip quantity (primary)\"), COUNT(\"Chip quantity (primary)\") FROM raw.ai_supercomputers UNION ALL SELECT 'Power Capacity (MW)', MIN(\"Power Capacity (MW)\"), MAX(\"Power Capacity (MW)\"), AVG(\"Power Capacity (MW)\"), MEDIAN(\"Power Capacity (MW)\"), STDDEV(\"Power Capacity (MW)\"), COUNT(\"Power Capacity (MW)\") FROM raw.ai_supercomputers UNION ALL SELECT 'Hardware Cost', MIN(\"Hardware Cost\"), MAX(\"Hardware Cost\"), AVG(\"Hardware Cost\"), MEDIAN(\"Hardware Cost\"), STDDEV(\"Hardware Cost\"), COUNT(\"Hardware Cost\") FROM raw.ai_supercomputers UNION ALL SELECT 'Energy Efficiency (log)', MIN(\"Energy Efficiency (log)\"), MAX(\"Energy Efficiency (log)\"), AVG(\"Energy Efficiency (log)\"), MEDIAN(\"Energy Efficiency (log)\"), STDDEV(\"Energy Efficiency (log)\"), COUNT(\"Energy Efficiency (log)\") FROM raw.ai_supercomputers UNION ALL SELECT 'Chip quantity (secondary)', MIN(\"Chip quantity (secondary)\"), MAX(\"Chip quantity (secondary)\"), AVG(\"Chip quantity (secondary)\"), MEDIAN(\"Chip quantity (secondary)\"), STDDEV(\"Chip quantity (secondary)\"), COUNT(\"Chip quantity (secondary)\") FROM raw.ai_supercomputers UNION ALL SELECT 'Total number of AI chips', MIN(\"Total number of AI chips\"), MAX(\"Total number of AI chips\"), AVG(\"Total number of AI chips\"), MEDIAN(\"Total number of AI chips\"), STDDEV(\"Total number of AI chips\"), COUNT(\"Total number of AI chips\") FROM raw.ai_supercomputers", "purpose": "Get distribution statistics (min, max, mean, median, stddev) for the first 8 numeric columns visible in inspect_columns", "source": "raw" } } ```