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95 lines
9.6 KiB
JSON
95 lines
9.6 KiB
JSON
{
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"question": {
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"questionId": "1452",
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"questionFrontendId": "1321",
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"boundTopicId": null,
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"title": "Restaurant Growth",
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"titleSlug": "restaurant-growth",
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"content": "<p>Table: <code>Customer</code></p>\n\n<pre>\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</pre>\n\n<p> </p>\n\n<p>You are the restaurant owner and you want to analyze a possible expansion (there will be at least one customer every day).</p>\n\n<p>Compute the moving average of how much the customer paid in a seven days window (i.e., current day + 6 days before). <code>average_amount</code> should be <strong>rounded to two decimal places</strong>.</p>\n\n<p>Return the result table ordered by <code>visited_on</code> <strong>in ascending order</strong>.</p>\n\n<p>The result format is in the following example.</p>\n\n<p> </p>\n<p><strong class=\"example\">Example 1:</strong></p>\n\n<pre>\n<strong>Input:</strong> \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+-------------+--------------+--------------+-------------+\n<strong>Output:</strong> \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+--------------+--------------+----------------+\n<strong>Explanation:</strong> \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</pre>\n",
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"isPaidOnly": false,
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"difficulty": "Medium",
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"likes": 630,
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"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]]}}",
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"categoryTitle": "Database",
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"lang": "MySQL",
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"langSlug": "mysql",
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"code": "# Write your MySQL query statement below\n",
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"code": "/* Write your PL/SQL query statement below */\n",
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},
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{
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"lang": "Pandas",
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"langSlug": "pythondata",
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"code": "import pandas as pd\n\ndef restaurant_growth(customer: pd.DataFrame) -> pd.DataFrame:\n ",
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"lang": "PostgreSQL",
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"langSlug": "postgresql",
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"code": "-- Write your PostgreSQL query statement below\n",
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"sampleTestCase": "{\"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]]}}",
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"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', 'name', 'visited_on', 'amount']).astype({'customer_id':'Int64', 'name':'object', 'visited_on':'datetime64[ns]', 'amount':'Int64'})\"], \"postgresql\": [\"\\nCreate table If Not Exists Customer (customer_id int, name varchar(20), visited_on date, amount int)\"], \"database_schema\": {\"Customer\": {\"customer_id\": \"INT\", \"name\": \"VARCHAR(20)\", \"visited_on\": \"DATE\", \"amount\": \"INT\"}}}",
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"Create table If Not Exists Customer (customer_id int, name varchar(20), visited_on date, amount int)",
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"Truncate table Customer",
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"insert into Customer (customer_id, name, visited_on, amount) values ('1', 'Jhon', '2019-01-01', '100')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('2', 'Daniel', '2019-01-02', '110')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('3', 'Jade', '2019-01-03', '120')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('4', 'Khaled', '2019-01-04', '130')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('5', 'Winston', '2019-01-05', '110')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('6', 'Elvis', '2019-01-06', '140')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('7', 'Anna', '2019-01-07', '150')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('8', 'Maria', '2019-01-08', '80')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('9', 'Jaze', '2019-01-09', '110')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('1', 'Jhon', '2019-01-10', '130')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('3', 'Jade', '2019-01-10', '150')"
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],
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"envInfo": "{\"mysql\": [\"MySQL\", \"<p><code>MySQL 8.0</code>.</p>\"], \"mssql\": [\"MS SQL Server\", \"<p><code>mssql server 2019</code>.</p>\"], \"oraclesql\": [\"Oracle\", \"<p><code>Oracle Sql 11.2</code>.</p>\"], \"pythondata\": [\"Pandas\", \"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0</p>\"], \"postgresql\": [\"PostgreSQL\", \"<p>PostgreSQL 16</p>\"]}",
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