{
    "data": {
        "question": {
            "questionId": "3257",
            "questionFrontendId": "100175",
            "categoryTitle": "Database",
            "boundTopicId": 2653972,
            "title": "Maximize Items",
            "titleSlug": "maximize-items",
            "content": null,
            "translatedTitle": null,
            "translatedContent": null,
            "isPaidOnly": true,
            "difficulty": "Hard",
            "likes": 0,
            "dislikes": 0,
            "isLiked": null,
            "similarQuestions": "[]",
            "contributors": [],
            "langToValidPlayground": null,
            "topicTags": [],
            "companyTagStats": null,
            "codeSnippets": null,
            "stats": "{\"totalAccepted\": \"13\", \"totalSubmission\": \"15\", \"totalAcceptedRaw\": 13, \"totalSubmissionRaw\": 15, \"acRate\": \"86.7%\"}",
            "hints": [],
            "solution": null,
            "status": null,
            "sampleTestCase": "{\"headers\":{\"Inventory\":[\"item_id\",\"item_type\",\"item_category\",\"square_footage\"]},\"rows\":{\"Inventory\":[[1374,\"prime_eligible\",\"Watches\",68.00],[4245,\"not_prime\",\"Art\",26.40],[5743,\"prime_eligible\",\"Software\",325.00],[8543,\"not_prime\",\"Clothing\",64.50],[2556,\"not_prime\",\"Shoes\",15.00],[2452,\"prime_eligible\",\"Scientific\",85.00],[3255,\"not_prime\",\"Furniture\",22.60],[1672,\"prime_eligible\",\"Beauty\",8.50],[4256,\"prime_eligible\",\"Furniture\",55.50],[6325,\"prime_eligible\",\"Food\",13.20]]}}",
            "metaData": "{\"mysql\":[\"Create table If Not Exists Inventory ( item_id int, item_type varchar(50), item_category varchar(50), square_footage decimal(10,2))\"],\"mssql\":[\"Create table  Inventory ( item_id int, item_type varchar(50), item_category varchar(50), square_footage decimal(10,2))\"],\"oraclesql\":[\"Create table Inventory ( item_id int, item_type varchar(50), item_category varchar(50), square_footage decimal(10,2))\"],\"database\":true,\"name\":\"maximize_items\",\"pythondata\":[\"Inventory = pd.DataFrame([], columns=['item_id', 'item_type', 'item_category', 'square_footage']).astype({'item_id':'Int64', 'item_type':'object', 'item_category':'object', 'square_footage':'Float64'})\\n\"],\"postgresql\":[\"Create table If Not Exists Inventory ( item_id int, item_type varchar(50), item_category varchar(50), square_footage decimal(10,2))\\n\"],\"database_schema\":{\"Inventory\":{\"item_id\":\"INT\",\"item_type\":\"VARCHAR(50)\",\"item_category\":\"VARCHAR(50)\",\"square_footage\":\"DECIMAL(10, 2)\"}}}",
            "judgerAvailable": true,
            "judgeType": "large",
            "mysqlSchemas": [
                "Create table If Not Exists Inventory ( item_id int, item_type varchar(50), item_category varchar(50), square_footage decimal(10,2))",
                "Truncate table Inventory",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('1374', 'prime_eligible', 'Watches', '68.0')",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('4245', 'not_prime', 'Art', '26.4')",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('5743', 'prime_eligible', 'Software', '325.0')",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('8543', 'not_prime', 'Clothing', '64.5')",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('2556', 'not_prime', 'Shoes', '15.0')",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('2452', 'prime_eligible', 'Scientific', '85.0')",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('3255', 'not_prime', 'Furniture', '22.6')",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('1672', 'prime_eligible', 'Beauty', '8.5')",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('4256', 'prime_eligible', 'Furniture', '55.5')",
                "insert into Inventory (item_id, item_type, item_category, square_footage) values ('6325', 'prime_eligible', 'Food', '13.2')"
            ],
            "enableRunCode": true,
            "envInfo": "{\"mysql\":[\"MySQL\",\"<p>\\u7248\\u672c\\uff1a<code>MySQL 8.0<\\/code><\\/p>\"],\"mssql\":[\"MS SQL Server\",\"<p>mssql server 2019.<\\/p>\"],\"oraclesql\":[\"Oracle\",\"<p>Oracle Sql 11.2.<\\/p>\"],\"pythondata\":[\"Pandas\",\"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
            "book": null,
            "isSubscribed": false,
            "isDailyQuestion": false,
            "dailyRecordStatus": null,
            "editorType": "CKEDITOR",
            "ugcQuestionId": null,
            "style": "LEETCODE",
            "exampleTestcases": "{\"headers\":{\"Inventory\":[\"item_id\",\"item_type\",\"item_category\",\"square_footage\"]},\"rows\":{\"Inventory\":[[1374,\"prime_eligible\",\"Watches\",68.00],[4245,\"not_prime\",\"Art\",26.40],[5743,\"prime_eligible\",\"Software\",325.00],[8543,\"not_prime\",\"Clothing\",64.50],[2556,\"not_prime\",\"Shoes\",15.00],[2452,\"prime_eligible\",\"Scientific\",85.00],[3255,\"not_prime\",\"Furniture\",22.60],[1672,\"prime_eligible\",\"Beauty\",8.50],[4256,\"prime_eligible\",\"Furniture\",55.50],[6325,\"prime_eligible\",\"Food\",13.20]]}}",
            "__typename": "QuestionNode"
        }
    }
}