Table: Customer
\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| customer_id | int |\n| name | varchar |\n| visited_on | date |\n| amount | int |\n+---------------+---------+\nIn SQL,(customer_id, visited_on) is the primary key for this table.\nThis table contains data about customer transactions in a restaurant.\nvisited_on is the date on which the customer with ID (customer_id) has visited the restaurant.\namount is the total paid by a customer.\n\n\n
\n\n
You are the restaurant owner and you want to analyze a possible expansion (there will be at least one customer every day).
\n\nCompute the moving average of how much the customer paid in a seven days window (i.e., current day + 6 days before). average_amount should be rounded to two decimal places.
Return the result table ordered by visited_on in ascending order.
The result format is in the following example.
\n\n\n
Example 1:
\n\n\nInput: \nCustomer table:\n+-------------+--------------+--------------+-------------+\n| customer_id | name | visited_on | amount |\n+-------------+--------------+--------------+-------------+\n| 1 | Jhon | 2019-01-01 | 100 |\n| 2 | Daniel | 2019-01-02 | 110 |\n| 3 | Jade | 2019-01-03 | 120 |\n| 4 | Khaled | 2019-01-04 | 130 |\n| 5 | Winston | 2019-01-05 | 110 | \n| 6 | Elvis | 2019-01-06 | 140 | \n| 7 | Anna | 2019-01-07 | 150 |\n| 8 | Maria | 2019-01-08 | 80 |\n| 9 | Jaze | 2019-01-09 | 110 | \n| 1 | Jhon | 2019-01-10 | 130 | \n| 3 | Jade | 2019-01-10 | 150 | \n+-------------+--------------+--------------+-------------+\nOutput: \n+--------------+--------------+----------------+\n| visited_on | amount | average_amount |\n+--------------+--------------+----------------+\n| 2019-01-07 | 860 | 122.86 |\n| 2019-01-08 | 840 | 120 |\n| 2019-01-09 | 840 | 120 |\n| 2019-01-10 | 1000 | 142.86 |\n+--------------+--------------+----------------+\nExplanation: \n1st moving average from 2019-01-01 to 2019-01-07 has an average_amount of (100 + 110 + 120 + 130 + 110 + 140 + 150)/7 = 122.86\n2nd moving average from 2019-01-02 to 2019-01-08 has an average_amount of (110 + 120 + 130 + 110 + 140 + 150 + 80)/7 = 120\n3rd moving average from 2019-01-03 to 2019-01-09 has an average_amount of (120 + 130 + 110 + 140 + 150 + 80 + 110)/7 = 120\n4th moving average from 2019-01-04 to 2019-01-10 has an average_amount of (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150)/7 = 142.86\n\n", "translatedTitle": null, "translatedContent": null, "isPaidOnly": false, "difficulty": "Medium", "likes": 630, "dislikes": 225, "isLiked": null, "similarQuestions": "[]", "exampleTestcases": "{\"headers\":{\"Customer\":[\"customer_id\",\"name\",\"visited_on\",\"amount\"]},\"rows\":{\"Customer\":[[1,\"Jhon\",\"2019-01-01\",100],[2,\"Daniel\",\"2019-01-02\",110],[3,\"Jade\",\"2019-01-03\",120],[4,\"Khaled\",\"2019-01-04\",130],[5,\"Winston\",\"2019-01-05\",110],[6,\"Elvis\",\"2019-01-06\",140],[7,\"Anna\",\"2019-01-07\",150],[8,\"Maria\",\"2019-01-08\",80],[9,\"Jaze\",\"2019-01-09\",110],[1,\"Jhon\",\"2019-01-10\",130],[3,\"Jade\",\"2019-01-10\",150]]}}", "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", "__typename": "CodeSnippetNode" }, { "lang": "MS SQL Server", "langSlug": "mssql", "code": "/* Write your T-SQL query statement below */\n", "__typename": "CodeSnippetNode" }, { "lang": "Oracle", "langSlug": "oraclesql", "code": 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"{\"headers\":{\"Customer\":[\"customer_id\",\"name\",\"visited_on\",\"amount\"]},\"rows\":{\"Customer\":[[1,\"Jhon\",\"2019-01-01\",100],[2,\"Daniel\",\"2019-01-02\",110],[3,\"Jade\",\"2019-01-03\",120],[4,\"Khaled\",\"2019-01-04\",130],[5,\"Winston\",\"2019-01-05\",110],[6,\"Elvis\",\"2019-01-06\",140],[7,\"Anna\",\"2019-01-07\",150],[8,\"Maria\",\"2019-01-08\",80],[9,\"Jaze\",\"2019-01-09\",110],[1,\"Jhon\",\"2019-01-10\",130],[3,\"Jade\",\"2019-01-10\",150]]}}", "metaData": "{\"mysql\": [\"Create table If Not Exists Customer (customer_id int, name varchar(20), visited_on date, amount int)\"], \"mssql\": [\"Create table Customer (customer_id int, name varchar(20), visited_on date, amount int)\"], \"oraclesql\": [\"Create table Customer (customer_id int, name varchar(20), visited_on date, amount int)\", \"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"], \"database\": true, \"name\": \"restaurant_growth\", \"pythondata\": [\"Customer = pd.DataFrame([], columns=['customer_id', 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