Table: Prices
\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| product_id | int |\n| start_date | date |\n| end_date | date |\n| price | int |\n+---------------+---------+\n(product_id, start_date, end_date) is the primary key (combination of columns with unique values) for this table.\nEach row of this table indicates the price of the product_id in the period from start_date to end_date.\nFor each product_id there will be no two overlapping periods. That means there will be no two intersecting periods for the same product_id.\n\n\n
\n\n
Table: UnitsSold
\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| product_id | int |\n| purchase_date | date |\n| units | int |\n+---------------+---------+\nThis table may contain duplicate rows.\nEach row of this table indicates the date, units, and product_id of each product sold. \n\n\n
\n\n
Write a solution to find the average selling price for each product. average_price should be rounded to 2 decimal places.
Return the result table in any order.
\n\nThe result format is in the following example.
\n\n\n
Example 1:
\n\n\nInput: \nPrices table:\n+------------+------------+------------+--------+\n| product_id | start_date | end_date | price |\n+------------+------------+------------+--------+\n| 1 | 2019-02-17 | 2019-02-28 | 5 |\n| 1 | 2019-03-01 | 2019-03-22 | 20 |\n| 2 | 2019-02-01 | 2019-02-20 | 15 |\n| 2 | 2019-02-21 | 2019-03-31 | 30 |\n+------------+------------+------------+--------+\nUnitsSold table:\n+------------+---------------+-------+\n| product_id | purchase_date | units |\n+------------+---------------+-------+\n| 1 | 2019-02-25 | 100 |\n| 1 | 2019-03-01 | 15 |\n| 2 | 2019-02-10 | 200 |\n| 2 | 2019-03-22 | 30 |\n+------------+---------------+-------+\nOutput: \n+------------+---------------+\n| product_id | average_price |\n+------------+---------------+\n| 1 | 6.96 |\n| 2 | 16.96 |\n+------------+---------------+\nExplanation: \nAverage selling price = Total Price of Product / Number of products sold.\nAverage selling price for product 1 = ((100 * 5) + (15 * 20)) / 115 = 6.96\nAverage selling price for product 2 = ((200 * 15) + (30 * 30)) / 230 = 16.96\n\n", "translatedTitle": "平均售价", "translatedContent": "
表：Prices
\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| product_id | int |\n| start_date | date |\n| end_date | date |\n| price | int |\n+---------------+---------+\n(product_id，start_date，end_date) 是\n\nprices表的主键（具有唯一值的列的组合）。\nprices表的每一行表示的是某个产品在一段时期内的价格。\n每个产品的对应时间段是不会重叠的，这也意味着同一个产品的价格时段不会出现交叉。
\n\n
表：UnitsSold
\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| product_id | int |\n| purchase_date | date |\n| units | int |\n+---------------+---------+\n该表可能包含重复数据。\n该表的每一行表示的是每种产品的出售日期，单位和产品 id。\n\n
\n\n
编写解决方案以查找每种产品的平均售价。average_price 应该 四舍五入到小数点后两位。
返回结果表 无顺序要求 。
\n\n结果格式如下例所示。
\n\n\n\n
示例 1：
\n\n\n输入：\nPrices table:\n+------------+------------+------------+--------+\n| product_id | start_date | end_date | price |\n+------------+------------+------------+--------+\n| 1 | 2019-02-17 | 2019-02-28 | 5 |\n| 1 | 2019-03-01 | 2019-03-22 | 20 |\n| 2 | 2019-02-01 | 2019-02-20 | 15 |\n| 2 | 2019-02-21 | 2019-03-31 | 30 |\n+------------+------------+------------+--------+\nUnitsSold table:\n+------------+---------------+-------+\n| product_id | purchase_date | units |\n+------------+---------------+-------+\n| 1 | 2019-02-25 | 100 |\n| 1 | 2019-03-01 | 15 |\n| 2 | 2019-02-10 | 200 |\n| 2 | 2019-03-22 | 30 |\n+------------+---------------+-------+\n输出：\n+------------+---------------+\n| product_id | average_price |\n+------------+---------------+\n| 1 | 6.96 |\n| 2 | 16.96 |\n+------------+---------------+\n解释：\n平均售价 = 产品总价 / 销售的产品数量。\n产品 1 的平均售价 = ((100 * 5)+(15 * 20) )/ 115 = 6.96\n产品 2 的平均售价 = ((200 * 15)+(30 * 30) )/ 230 = 16.96\n", "isPaidOnly": false, "difficulty": "Easy", 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"{\"headers\":{\"Prices\":[\"product_id\",\"start_date\",\"end_date\",\"price\"],\"UnitsSold\":[\"product_id\",\"purchase_date\",\"units\"]},\"rows\":{\"Prices\":[[1,\"2019-02-17\",\"2019-02-28\",5],[1,\"2019-03-01\",\"2019-03-22\",20],[2,\"2019-02-01\",\"2019-02-20\",15],[2,\"2019-02-21\",\"2019-03-31\",30]],\"UnitsSold\":[[1,\"2019-02-25\",100],[1,\"2019-03-01\",15],[2,\"2019-02-10\",200],[2,\"2019-03-22\",30]]}}", "metaData": "{\"mysql\":[\"Create table If Not Exists Prices (product_id int, start_date date, end_date date, price int)\",\"Create table If Not Exists UnitsSold (product_id int, purchase_date date, units int)\"],\"mssql\":[\"Create table Prices (product_id int, start_date date, end_date date, price int)\",\"Create table UnitsSold (product_id int, purchase_date date, units int)\"],\"oraclesql\":[\"Create table Prices (product_id int, start_date date, end_date date, price int)\",\"Create table UnitsSold (product_id int, purchase_date date, units int)\",\"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"],\"database\":true,\"name\":\"average_selling_price\",\"pythondata\":[\"Prices = pd.DataFrame([], columns=['product_id', 'start_date', 'end_date', 'price']).astype({'product_id':'Int64', 'start_date':'datetime64[ns]', 'end_date':'datetime64[ns]', 'price':'Int64'})\",\"UnitsSold = pd.DataFrame([], columns=['product_id', 'purchase_date', 'units']).astype({'product_id':'Int64', 'purchase_date':'datetime64[ns]', 'units':'Int64'})\"],\"postgresql\":[\"\\nCreate table If Not Exists Prices (product_id int, start_date date, end_date date, price int)\\n\",\"Create table If Not Exists UnitsSold (product_id int, purchase_date date, units int)\"],\"database_schema\":{\"Prices\":{\"product_id\":\"INT\",\"start_date\":\"DATE\",\"end_date\":\"DATE\",\"price\":\"INT\"},\"UnitsSold\":{\"product_id\":\"INT\",\"purchase_date\":\"DATE\",\"units\":\"INT\"}}}", "judgerAvailable": true, "judgeType": "large", "mysqlSchemas": [ "Create table If Not Exists Prices (product_id int, 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"exampleTestcases": "{\"headers\":{\"Prices\":[\"product_id\",\"start_date\",\"end_date\",\"price\"],\"UnitsSold\":[\"product_id\",\"purchase_date\",\"units\"]},\"rows\":{\"Prices\":[[1,\"2019-02-17\",\"2019-02-28\",5],[1,\"2019-03-01\",\"2019-03-22\",20],[2,\"2019-02-01\",\"2019-02-20\",15],[2,\"2019-02-21\",\"2019-03-31\",30]],\"UnitsSold\":[[1,\"2019-02-25\",100],[1,\"2019-03-01\",15],[2,\"2019-02-10\",200],[2,\"2019-03-22\",30]]}}",
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