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": null, "translatedContent": null, "isPaidOnly": false, "difficulty": "Easy", "likes": 860, "dislikes": 89, "isLiked": null, "similarQuestions": "[]", "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]]}}", "categoryTitle": "Database", "contributors": [], "topicTags": [ { "name": "Database", "slug": "database", "translatedName": null, "__typename": "TopicTagNode" } ], "companyTagStats": null, "codeSnippets": [ { "lang": "MySQL", "langSlug": "mysql", "code": "# Write your MySQL query statement below\n", 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"status": null, "sampleTestCase": "{\"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, 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