I need to identify the numeric columns in this dataset and compute their distribution statistics in a single query. From the dataset card, the table `raw.olympic_table_tennis_medals_1988_2024` has these columns with their types: - `year` (BIGINT) - numeric - `is_china` (BIGINT) - numeric (binary flag) - `medal_value` (BIGINT) - numeric The other columns are VARCHAR or TIMESTAMP, so not numeric in the statistical sense. Let me query the distribution statistics for all three 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 dataset has 3 numeric columns: year, is_china, and medal_value. I'll write one SELECT with aggregates for all three columns to get their distributions efficiently.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as total_rows, COUNT(year) as year_count, MIN(year) as year_min, MAX(year) as year_max, AVG(year) as year_mean, MEDIAN(year) as year_median, STDDEV(year) as year_stddev, COUNT(is_china) as is_china_count, MIN(is_china) as is_china_min, MAX(is_china) as is_china_max, AVG(is_china) as is_china_mean, MEDIAN(is_china) as is_china_median, STDDEV(is_china) as is_china_stddev, COUNT(medal_value) as medal_value_count, MIN(medal_value) as medal_value_min, MAX(medal_value) as medal_value_max, AVG(medal_value) as medal_value_mean, MEDIAN(medal_value) as medal_value_median, STDDEV(medal_value) as medal_value_stddev FROM raw.olympic_table_tennis_medals_1988_2024", "purpose": "Compute distribution statistics (min, max, mean, median, standard deviation) for all numeric columns in the dataset", "source": "raw" } } ```