I need to identify the main categorical columns and find their most common values with frequencies. From the column inspection, I can see the key categorical dimensions are: - Cuisines (~1,851 distinct) - Currency (~11 distinct) - Rating color (~6 distinct) - Rating text (~6 distinct) - Price range (~4 distinct) - Has Table booking (~2 distinct) - Has Online delivery (~2 distinct) - Is delivering now (~2 distinct) Let me query the most common values for each of these categorical columns in a single efficient query: ```json { "action": "tool_call", "reasoning": "I'll write one comprehensive 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": "WITH cuisines_top AS (SELECT 'Cuisines' as category, Cuisines as value, COUNT(*) as frequency FROM raw.\"Zomato Restaurant Dataset\" GROUP BY Cuisines ORDER BY COUNT(*) DESC LIMIT 10), currency_top AS (SELECT 'Currency' as category, Currency as value, COUNT(*) as frequency FROM raw.\"Zomato Restaurant Dataset\" GROUP BY Currency ORDER BY COUNT(*) DESC), rating_color_top AS (SELECT 'Rating color' as category, \"Rating color\" as value, COUNT(*) as frequency FROM raw.\"Zomato Restaurant Dataset\" GROUP BY \"Rating color\" ORDER BY COUNT(*) DESC), rating_text_top AS (SELECT 'Rating text' as category, \"Rating text\" as value, COUNT(*) as frequency FROM raw.\"Zomato Restaurant Dataset\" GROUP BY \"Rating text\" ORDER BY COUNT(*) DESC), price_range_top AS (SELECT 'Price range' as category, CAST(\"Price range\" AS VARCHAR) as value, COUNT(*) as frequency FROM raw.\"Zomato Restaurant Dataset\" GROUP BY \"Price range\" ORDER BY COUNT(*) DESC), table_booking_top AS (SELECT 'Has Table booking' as category, CAST(\"Has Table booking\" AS VARCHAR) as value, COUNT(*) as frequency FROM raw.\"Zomato Restaurant Dataset\" GROUP BY \"Has Table booking\" ORDER BY COUNT(*) DESC), online_delivery_top AS (SELECT 'Has Online delivery' as category, CAST(\"Has Online delivery\" AS VARCHAR) as value, COUNT(*) as frequency FROM raw.\"Zomato Restaurant Dataset\" GROUP BY \"Has Online delivery\" ORDER BY COUNT(*) DESC), delivering_now_top AS (SELECT 'Is delivering now' as category, CAST(\"Is delivering now\" AS VARCHAR) as value, COUNT(*) as frequency FROM raw.\"Zomato Restaurant Dataset\" GROUP BY \"Is delivering now\" ORDER BY COUNT(*) DESC) SELECT * FROM cuisines_top UNION ALL SELECT * FROM currency_top UNION ALL SELECT * FROM rating_color_top UNION ALL SELECT * FROM rating_text_top UNION ALL SELECT * FROM price_range_top UNION ALL SELECT * FROM table_booking_top UNION ALL SELECT * FROM online_delivery_top UNION ALL SELECT * FROM delivering_now_top ORDER BY category, frequency DESC", "source": "raw", "purpose": "Get the most common values and their frequencies for all main categorical columns in the dataset" } } ```