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96 lines
24 KiB
JSON
96 lines
24 KiB
JSON
{
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"data": {
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"question": {
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"questionId": "4091",
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"questionFrontendId": "3705",
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"categoryTitle": "Database",
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"boundTopicId": 3798940,
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"title": "Find Golden Hour Customers",
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"titleSlug": "find-golden-hour-customers",
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"content": "<p>Table: <code>restaurant_orders</code></p>\n\n<pre>\n+------------------+----------+\n| Column Name | Type | \n+------------------+----------+\n| order_id | int |\n| customer_id | int |\n| order_timestamp | datetime |\n| order_amount | decimal |\n| payment_method | varchar |\n| order_rating | int |\n+------------------+----------+\norder_id is the unique identifier for this table.\npayment_method can be cash, card, or app.\norder_rating is between 1 and 5, where 5 is the best (NULL if not rated).\norder_timestamp contains both date and time information.\n</pre>\n\n<p>Write a solution to find <strong>golden hour customers</strong> - customers who consistently order during peak hours and provide high satisfaction. A customer is a <strong>golden hour customer</strong> if they meet ALL the following criteria:</p>\n\n<ul>\n\t<li>Made <strong>at least</strong> <code>3</code> orders.</li>\n\t<li><strong>At least</strong> <code>60%</code> of their orders are during <strong>peak hours </strong>(<code>11:00</code>-<code>14:00</code> or <code>18:00</code>-<code>21:00</code>).</li>\n\t<li>Their <strong>average rating</strong> for rated orders is at least <code>4.0,</code> round it to<code> 2 </code>decimal places.</li>\n\t<li>Have rated <strong>at least</strong> <code>50%</code> of their orders.</li>\n</ul>\n\n<p>Return <em>the result table ordered by</em> <code>average_rating</code> <em>in <strong>descending</strong> order, then by</em> <code>customer_id</code> <em>in <strong>descending</strong> order</em>.</p>\n\n<p>The result format is in the following example.</p>\n\n<p> </p>\n<p><strong class=\"example\">Example:</strong></p>\n\n<div class=\"example-block\">\n<p><strong>Input:</strong></p>\n\n<p>restaurant_orders table:</p>\n\n<pre class=\"example-io\">\n+----------+-------------+---------------------+--------------+----------------+--------------+\n| order_id | customer_id | order_timestamp | order_amount | payment_method | order_rating |\n+----------+-------------+---------------------+--------------+----------------+--------------+\n| 1 | 101 | 2024-03-01 12:30:00 | 25.50 | card | 5 |\n| 2 | 101 | 2024-03-02 19:15:00 | 32.00 | app | 4 |\n| 3 | 101 | 2024-03-03 13:45:00 | 28.75 | card | 5 |\n| 4 | 101 | 2024-03-04 20:30:00 | 41.00 | app | NULL |\n| 5 | 102 | 2024-03-01 11:30:00 | 18.50 | cash | 4 |\n| 6 | 102 | 2024-03-02 12:00:00 | 22.00 | card | 3 |\n| 7 | 102 | 2024-03-03 15:30:00 | 19.75 | cash | NULL |\n| 8 | 103 | 2024-03-01 19:00:00 | 55.00 | app | 5 |\n| 9 | 103 | 2024-03-02 20:45:00 | 48.50 | app | 4 |\n| 10 | 103 | 2024-03-03 18:30:00 | 62.00 | card | 5 |\n| 11 | 104 | 2024-03-01 10:00:00 | 15.00 | cash | 3 |\n| 12 | 104 | 2024-03-02 09:30:00 | 18.00 | cash | 2 |\n| 13 | 104 | 2024-03-03 16:00:00 | 20.00 | card | 3 |\n| 14 | 105 | 2024-03-01 12:15:00 | 30.00 | app | 4 |\n| 15 | 105 | 2024-03-02 13:00:00 | 35.50 | app | 5 |\n| 16 | 105 | 2024-03-03 11:45:00 | 28.00 | card | 4 |\n+----------+-------------+---------------------+--------------+----------------+--------------+\n</pre>\n\n<p><strong>Output:</strong></p>\n\n<pre class=\"example-io\">\n+-------------+--------------+----------------------+----------------+\n| customer_id | total_orders | peak_hour_percentage | average_rating |\n+-------------+--------------+----------------------+----------------+\n| 103 | 3 | 100 | 4.67 |\n| 101 | 4 | 75 | 4.67 |\n| 105 | 3 | 100 | 4.33 |\n+-------------+--------------+----------------------+----------------+\n</pre>\n\n<p><strong>Explanation:</strong></p>\n\n<ul>\n\t<li><strong>Customer 101</strong>:\n\n\t<ul>\n\t\t<li>Total orders: 4 (at least 3) </li>\n\t\t<li>Peak hour orders: 3 out of 4 (12:30, 19:15, 13:45, and 20:30 are in peak hours)</li>\n\t\t<li>Peak hour percentage: 3/4 = 75% (at least 60%) </li>\n\t\t<li>Rated orders: 3 out of 4 (75% rating completion) </li>\n\t\t<li>Average rating: (5+4+5)/3 = 4.67 (at least 4.0) </li>\n\t\t<li>Result: <strong>Golden hour customer</strong></li>\n\t</ul>\n\t</li>\n\t<li><strong>Customer 102</strong>:\n\t<ul>\n\t\t<li>Total orders: 3 (at least 3) </li>\n\t\t<li>Peak hour orders: 2 out of 3 (11:30, 12:00 are in peak hours; 15:30 is not)</li>\n\t\t<li>Peak hour percentage: 2/3 = 66.67% (at least 60%) </li>\n\t\t<li>Rated orders: 2 out of 3 (66.67% rating completion) </li>\n\t\t<li>Average rating: (4+3)/2 = 3.5 (less than 4.0) </li>\n\t\t<li>Result: <strong>Not a golden hour customer</strong> (average rating too low)</li>\n\t</ul>\n\t</li>\n\t<li><strong>Customer 103</strong>:\n\t<ul>\n\t\t<li>Total orders: 3 (at least 3) </li>\n\t\t<li>Peak hour orders: 3 out of 3 (19:00, 20:45, 18:30 all in evening peak)</li>\n\t\t<li>Peak hour percentage: 3/3 = 100% (at least 60%) </li>\n\t\t<li>Rated orders: 3 out of 3 (100% rating completion) </li>\n\t\t<li>Average rating: (5+4+5)/3 = 4.67 (at least 4.0) </li>\n\t\t<li>Result: <strong>Golden hour customer</strong></li>\n\t</ul>\n\t</li>\n\t<li><strong>Customer 104</strong>:\n\t<ul>\n\t\t<li>Total orders: 3 (at least 3) </li>\n\t\t<li>Peak hour orders: 0 out of 3 (10:00, 09:30, 16:00 all outside peak hours)</li>\n\t\t<li>Peak hour percentage: 0/3 = 0% (less than 60%) </li>\n\t\t<li>Result: <strong>Not a golden hour customer</strong> (insufficient peak hour orders)</li>\n\t</ul>\n\t</li>\n\t<li><strong>Customer 105</strong>:\n\t<ul>\n\t\t<li>Total orders: 3 (at least 3) </li>\n\t\t<li>Peak hour orders: 3 out of 3 (12:15, 13:00, 11:45 all in lunch peak)</li>\n\t\t<li>Peak hour percentage: 3/3 = 100% (at least 60%) </li>\n\t\t<li>Rated orders: 3 out of 3 (100% rating completion) </li>\n\t\t<li>Average rating: (4+5+4)/3 = 4.33 (at least 4.0) </li>\n\t\t<li>Result: <strong>Golden hour customer</strong></li>\n\t</ul>\n\t</li>\n</ul>\n\n<p>The results table is ordered by average_rating DESC, then customer_id DESC.</p>\n</div>\n",
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"translatedTitle": "寻找黄金时段客户",
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"translatedContent": "<p>表：<code>restaurant_orders</code></p>\n\n<pre>\n+------------------+----------+\n| Column Name | Type | \n+------------------+----------+\n| order_id | int |\n| customer_id | int |\n| order_timestamp | datetime |\n| order_amount | decimal |\n| payment_method | varchar |\n| order_rating | int |\n+------------------+----------+\norder_id 是这张表的唯一主键。\npayment_method 可以是 cash，card 或 app。\norder_rating 在 1 到 5 之间，其中 5 是最佳（如果没有评分则是 NULL）。\norder_timestamp 同时包含日期和时间信息。\n</pre>\n\n<p>编写一个解决方案来寻找 <strong>黄金时间客户</strong> - 高峰时段持续订购且满意度高的客户。客户若满足以下所有条件，则被视为 <strong>黄金时段客户</strong>：</p>\n\n<ul>\n\t<li>进行 <strong>至少</strong> <code>3</code> 笔订单。</li>\n\t<li>他们有 <strong>至少</strong> <code>60%</code> 的订单在 <strong>高峰时间</strong> 中（<code>11:00</code>-<code>14:00</code> 或 <code>18:00</code>-<code>21:00</code>）。</li>\n\t<li>他们的 <strong>平均评分</strong> 至少为 <code>4.0</code>，四舍五入到小数点后 <code>2</code> 位。</li>\n\t<li>已评价至少 <code>50%</code> 的订单。</li>\n</ul>\n\n<p>返回结果表按 <code>average_rating</code> <strong>降序</strong> 排序，然后按 <code>customer_id</code> <strong>降序 </strong>排序。</p>\n\n<p>结果格式如下所示。</p>\n\n<p> </p>\n\n<p><strong class=\"example\">示例：</strong></p>\n\n<div class=\"example-block\">\n<p><b>输入：</b></p>\n\n<p>restaurant_orders 表：</p>\n\n<pre class=\"example-io\">\n+----------+-------------+---------------------+--------------+----------------+--------------+\n| order_id | customer_id | order_timestamp | order_amount | payment_method | order_rating |\n+----------+-------------+---------------------+--------------+----------------+--------------+\n| 1 | 101 | 2024-03-01 12:30:00 | 25.50 | card | 5 |\n| 2 | 101 | 2024-03-02 19:15:00 | 32.00 | app | 4 |\n| 3 | 101 | 2024-03-03 13:45:00 | 28.75 | card | 5 |\n| 4 | 101 | 2024-03-04 20:30:00 | 41.00 | app | NULL |\n| 5 | 102 | 2024-03-01 11:30:00 | 18.50 | cash | 4 |\n| 6 | 102 | 2024-03-02 12:00:00 | 22.00 | card | 3 |\n| 7 | 102 | 2024-03-03 15:30:00 | 19.75 | cash | NULL |\n| 8 | 103 | 2024-03-01 19:00:00 | 55.00 | app | 5 |\n| 9 | 103 | 2024-03-02 20:45:00 | 48.50 | app | 4 |\n| 10 | 103 | 2024-03-03 18:30:00 | 62.00 | card | 5 |\n| 11 | 104 | 2024-03-01 10:00:00 | 15.00 | cash | 3 |\n| 12 | 104 | 2024-03-02 09:30:00 | 18.00 | cash | 2 |\n| 13 | 104 | 2024-03-03 16:00:00 | 20.00 | card | 3 |\n| 14 | 105 | 2024-03-01 12:15:00 | 30.00 | app | 4 |\n| 15 | 105 | 2024-03-02 13:00:00 | 35.50 | app | 5 |\n| 16 | 105 | 2024-03-03 11:45:00 | 28.00 | card | 4 |\n+----------+-------------+---------------------+--------------+----------------+--------------+\n</pre>\n\n<p><strong>输出：</strong></p>\n\n<pre class=\"example-io\">\n+-------------+--------------+----------------------+----------------+\n| customer_id | total_orders | peak_hour_percentage | average_rating |\n+-------------+--------------+----------------------+----------------+\n| 103 | 3 | 100 | 4.67 |\n| 101 | 4 | 75 | 4.67 |\n| 105 | 3 | 100 | 4.33 |\n+-------------+--------------+----------------------+----------------+\n</pre>\n\n<p><strong>解释：</strong></p>\n\n<ul>\n\t<li><strong>客户 101：</strong>\n\n\t<ul>\n\t\t<li>总订单数：4（至少 3 笔）</li>\n\t\t<li>高峰时间订单：4 笔中有 3 笔（12:30，19:15，13:45 和 20:30 在高峰时间）</li>\n\t\t<li>高峰时间占比：3/4 = 75%（至少 60%）</li>\n\t\t<li>已评分的订单：4 笔中有 3 笔（75% 评分完成率）</li>\n\t\t<li>平均评分：(5+4+5)/3 = 4.67（至少 4.0）</li>\n\t\t<li>结果：<strong>黄金时段客户</strong></li>\n\t</ul>\n\t</li>\n\t<li><strong>客户 102</strong>:\n\t<ul>\n\t\t<li>总订单数：3（至少 3 笔）</li>\n\t\t<li>高峰时间订单：3 笔中有 2 笔（11:30，12:00 都在高峰时间，但 15:30 不是）</li>\n\t\t<li>高峰时间占比：2/3 = 66.67%（至少 60%）</li>\n\t\t<li>已评分的订单：3 笔中有 2 笔（66.67% 评分完成率）</li>\n\t\t<li>平均评分：(4+3)/2 = 3.5（少于 4.0）</li>\n\t\t<li>结果：<strong>不是黄金时段客户</strong>（平均评分太低）</li>\n\t</ul>\n\t</li>\n\t<li><strong>客户 103</strong>:\n\t<ul>\n\t\t<li>总订单数：3（至少 3 笔）</li>\n\t\t<li>高峰时间订单：3 笔中有 3 （19:00，20:45，18:30 都在傍晚高峰时间）</li>\n\t\t<li>高峰时间占比：3/3 = 100%（至少 60%）</li>\n\t\t<li>已评分的订单：3 笔中有 3 笔（100% 评分完成率）</li>\n\t\t<li>平均评分：(5+4+5)/3 = 4.67（至少 4.0）</li>\n\t\t<li>结果：<strong>黄金时段客户</strong></li>\n\t</ul>\n\t</li>\n\t<li><strong>客户 104</strong>:\n\t<ul>\n\t\t<li>总订单数：3（至少 3 笔）</li>\n\t\t<li>高峰时间订单：3 笔中有 0 笔（10:00，09:30，16:00 都不在高峰时间）</li>\n\t\t<li>高峰时间占比：0/3 = 0%（至少 60%）</li>\n\t\t<li>结果：<strong>不是黄金时段客户</strong>（高峰时段订单不足）</li>\n\t</ul>\n\t</li>\n\t<li><strong>客户 105</strong>:\n\t<ul>\n\t\t<li>总订单数：3（至少 3 笔）</li>\n\t\t<li>高峰时间订单：3 笔中有 3 笔（12:15，13:00，11:45 都在中午高峰时间）</li>\n\t\t<li>高峰时间占比：3/3 = 100%（至少 60%）</li>\n\t\t<li>已评分的订单：3 笔中有 3 笔（100% 评分完成率）</li>\n\t\t<li>平均评分：(4+5+4)/3 = 4.33（至少 4.0）</li>\n\t\t<li>结果：<strong>黄金时段客户</strong></li>\n\t</ul>\n\t</li>\n</ul>\n\n<p>结果表按 average_rating 降序排序，然后按 customer_id 降序排序。</p>\n</div>\n",
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"sampleTestCase": "{\"headers\":{\"restaurant_orders\":[\"order_id\",\"customer_id\",\"order_timestamp\",\"order_amount\",\"payment_method\",\"order_rating\"]},\"rows\":{\"restaurant_orders\":[[1,101,\"2024-03-01 12:30:00\",25.50,\"card\",5],[2,101,\"2024-03-02 19:15:00\",32.00,\"app\",4],[3,101,\"2024-03-03 13:45:00\",28.75,\"card\",5],[4,101,\"2024-03-04 20:30:00\",41.00,\"app\",null],[5,102,\"2024-03-01 11:30:00\",18.50,\"cash\",4],[6,102,\"2024-03-02 12:00:00\",22.00,\"card\",3],[7,102,\"2024-03-03 15:30:00\",19.75,\"cash\",null],[8,103,\"2024-03-01 19:00:00\",55.00,\"app\",5],[9,103,\"2024-03-02 20:45:00\",48.50,\"app\",4],[10,103,\"2024-03-03 18:30:00\",62.00,\"card\",5],[11,104,\"2024-03-01 10:00:00\",15.00,\"cash\",3],[12,104,\"2024-03-02 09:30:00\",18.00,\"cash\",2],[13,104,\"2024-03-03 16:00:00\",20.00,\"card\",3],[14,105,\"2024-03-01 12:15:00\",30.00,\"app\",4],[15,105,\"2024-03-02 13:00:00\",35.50,\"app\",5],[16,105,\"2024-03-03 11:45:00\",28.00,\"card\",4]]}}",
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"CREATE TABLE restaurant_orders (\n order_id INT,\n customer_id INT,\n order_timestamp DATETIME,\n order_amount DECIMAL(10,2),\n payment_method VARCHAR(10),\n order_rating INT\n)",
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"Truncate table restaurant_orders",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('1', '101', '2024-03-01 12:30:00', '25.5', 'card', '5')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('2', '101', '2024-03-02 19:15:00', '32.0', 'app', '4')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('3', '101', '2024-03-03 13:45:00', '28.75', 'card', '5')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('4', '101', '2024-03-04 20:30:00', '41.0', 'app', NULL)",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('5', '102', '2024-03-01 11:30:00', '18.5', 'cash', '4')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('6', '102', '2024-03-02 12:00:00', '22.0', 'card', '3')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('7', '102', '2024-03-03 15:30:00', '19.75', 'cash', NULL)",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('8', '103', '2024-03-01 19:00:00', '55.0', 'app', '5')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('9', '103', '2024-03-02 20:45:00', '48.5', 'app', '4')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('10', '103', '2024-03-03 18:30:00', '62.0', 'card', '5')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('11', '104', '2024-03-01 10:00:00', '15.0', 'cash', '3')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('12', '104', '2024-03-02 09:30:00', '18.0', 'cash', '2')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('13', '104', '2024-03-03 16:00:00', '20.0', 'card', '3')",
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"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('14', '105', '2024-03-01 12:15:00', '30.0', 'app', '4')",
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||
"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('15', '105', '2024-03-02 13:00:00', '35.5', 'app', '5')",
|
||
"insert into restaurant_orders (order_id, customer_id, order_timestamp, order_amount, payment_method, order_rating) values ('16', '105', '2024-03-03 11:45:00', '28.0', 'card', '4')"
|
||
],
|
||
"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 2.2.2 and NumPy 1.26.4<\\/p>\"],\"postgresql\":[\"PostgreSQL\",\"<p>PostgreSQL 16<\\/p>\"]}",
|
||
"book": null,
|
||
"isSubscribed": false,
|
||
"isDailyQuestion": false,
|
||
"dailyRecordStatus": null,
|
||
"editorType": "CKEDITOR",
|
||
"ugcQuestionId": null,
|
||
"style": "LEETCODE",
|
||
"exampleTestcases": "{\"headers\":{\"restaurant_orders\":[\"order_id\",\"customer_id\",\"order_timestamp\",\"order_amount\",\"payment_method\",\"order_rating\"]},\"rows\":{\"restaurant_orders\":[[1,101,\"2024-03-01 12:30:00\",25.50,\"card\",5],[2,101,\"2024-03-02 19:15:00\",32.00,\"app\",4],[3,101,\"2024-03-03 13:45:00\",28.75,\"card\",5],[4,101,\"2024-03-04 20:30:00\",41.00,\"app\",null],[5,102,\"2024-03-01 11:30:00\",18.50,\"cash\",4],[6,102,\"2024-03-02 12:00:00\",22.00,\"card\",3],[7,102,\"2024-03-03 15:30:00\",19.75,\"cash\",null],[8,103,\"2024-03-01 19:00:00\",55.00,\"app\",5],[9,103,\"2024-03-02 20:45:00\",48.50,\"app\",4],[10,103,\"2024-03-03 18:30:00\",62.00,\"card\",5],[11,104,\"2024-03-01 10:00:00\",15.00,\"cash\",3],[12,104,\"2024-03-02 09:30:00\",18.00,\"cash\",2],[13,104,\"2024-03-03 16:00:00\",20.00,\"card\",3],[14,105,\"2024-03-01 12:15:00\",30.00,\"app\",4],[15,105,\"2024-03-02 13:00:00\",35.50,\"app\",5],[16,105,\"2024-03-03 11:45:00\",28.00,\"card\",4]]}}",
|
||
"__typename": "QuestionNode"
|
||
}
|
||
}
|
||
} |