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feat: add solutions to lc problems: No.3705,3706 (#4767)
* feat: add solutions to lc problem: No.3705 * feat: add solutions to lc problem: No.3706
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‎solution/3700-3799/3705.Find Golden Hour Customers/README.md‎

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---
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comments: true
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difficulty: 中等
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edit_url: https://github.com/doocs/leetcode/edit/main/solution/3700-3799/3705.Find%20Golden%20Hour%20Customers/README.md
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tags:
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- 数据库
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---
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<!-- problem:start -->
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# [3705. Find Golden Hour Customers](https://leetcode.cn/problems/find-golden-hour-customers)
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[English Version](/solution/3700-3799/3705.Find%20Golden%20Hour%20Customers/README_EN.md)
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## 题目描述
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<!-- description:start -->
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<p>Table: <code>restaurant_orders</code></p>
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<pre>
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+------------------+----------+
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| Column Name | Type |
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+------------------+----------+
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| order_id | int |
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| customer_id | int |
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| order_timestamp | datetime |
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| order_amount | decimal |
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| payment_method | varchar |
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| order_rating | int |
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+------------------+----------+
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order_id is the unique identifier for this table.
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payment_method can be cash, card, or app.
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order_rating is between 1 and 5, where 5 is the best (NULL if not rated).
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order_timestamp contains both date and time information.
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</pre>
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<p>Write a solution to find <strong>golden hour customers</strong>&nbsp;- 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>
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<ul>
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<li>Made <strong>at least</strong> <code>3</code> orders.</li>
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<li><strong>At least</strong> <code>60%</code> of their orders are during <strong>peak hours&nbsp;</strong>(<code>11:00</code>-<code>14:00</code> or <code>18:00</code>-<code>21:00</code>).</li>
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<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>
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<li>Have rated <strong>at least</strong> <code>50%</code> of their orders.</li>
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</ul>
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<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>
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<p>The result format is in the following example.</p>
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<p>&nbsp;</p>
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<p><strong class="example">Example:</strong></p>
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<div class="example-block">
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<p><strong>Input:</strong></p>
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<p>restaurant_orders table:</p>
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<pre class="example-io">
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+----------+-------------+---------------------+--------------+----------------+--------------+
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| order_id | customer_id | order_timestamp | order_amount | payment_method | order_rating |
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+----------+-------------+---------------------+--------------+----------------+--------------+
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| 1 | 101 | 2024-03-01 12:30:00 | 25.50 | card | 5 |
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| 2 | 101 | 2024-03-02 19:15:00 | 32.00 | app | 4 |
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| 3 | 101 | 2024-03-03 13:45:00 | 28.75 | card | 5 |
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| 4 | 101 | 2024-03-04 20:30:00 | 41.00 | app | NULL |
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| 5 | 102 | 2024-03-01 11:30:00 | 18.50 | cash | 4 |
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| 6 | 102 | 2024-03-02 12:00:00 | 22.00 | card | 3 |
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| 7 | 102 | 2024-03-03 15:30:00 | 19.75 | cash | NULL |
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| 8 | 103 | 2024-03-01 19:00:00 | 55.00 | app | 5 |
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| 9 | 103 | 2024-03-02 20:45:00 | 48.50 | app | 4 |
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| 10 | 103 | 2024-03-03 18:30:00 | 62.00 | card | 5 |
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| 11 | 104 | 2024-03-01 10:00:00 | 15.00 | cash | 3 |
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| 12 | 104 | 2024-03-02 09:30:00 | 18.00 | cash | 2 |
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| 13 | 104 | 2024-03-03 16:00:00 | 20.00 | card | 3 |
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| 14 | 105 | 2024-03-01 12:15:00 | 30.00 | app | 4 |
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| 15 | 105 | 2024-03-02 13:00:00 | 35.50 | app | 5 |
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| 16 | 105 | 2024-03-03 11:45:00 | 28.00 | card | 4 |
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+----------+-------------+---------------------+--------------+----------------+--------------+
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</pre>
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<p><strong>Output:</strong></p>
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<pre class="example-io">
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+-------------+--------------+----------------------+----------------+
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| customer_id | total_orders | peak_hour_percentage | average_rating |
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+-------------+--------------+----------------------+----------------+
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| 103 | 3 | 100 | 4.67 |
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| 101 | 4 | 75 | 4.67 |
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| 105 | 3 | 100 | 4.33 |
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+-------------+--------------+----------------------+----------------+
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</pre>
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<p><strong>Explanation:</strong></p>
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<ul>
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<li><strong>Customer 101</strong>:
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<ul>
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<li>Total orders: 4 (at least 3)&nbsp;</li>
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<li>Peak hour orders: 3 out of 4 (12:30, 19:15, 13:45, and 20:30 are in peak hours)</li>
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<li>Peak hour percentage: 3/4 = 75% (at least 60%)&nbsp;</li>
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<li>Rated orders: 3 out of 4 (75% rating completion)&nbsp;</li>
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<li>Average rating: (5+4+5)/3 = 4.67 (at least 4.0)&nbsp;</li>
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<li>Result: <strong>Golden hour customer</strong></li>
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</ul>
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</li>
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<li><strong>Customer 102</strong>:
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<ul>
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<li>Total orders: 3 (at least 3)&nbsp;</li>
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<li>Peak hour orders: 2 out of 3 (11:30, 12:00 are in peak hours; 15:30 is not)</li>
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<li>Peak hour percentage: 2/3 = 66.67% (at least 60%)&nbsp;</li>
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<li>Rated orders: 2 out of 3 (66.67% rating completion)&nbsp;</li>
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<li>Average rating: (4+3)/2 = 3.5 (less than 4.0)&nbsp;</li>
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<li>Result: <strong>Not a golden hour customer</strong> (average rating too low)</li>
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</ul>
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</li>
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<li><strong>Customer 103</strong>:
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<ul>
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<li>Total orders: 3 (at least 3)&nbsp;</li>
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<li>Peak hour orders: 3 out of 3 (19:00, 20:45, 18:30 all in evening peak)</li>
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<li>Peak hour percentage: 3/3 = 100% (at least 60%)&nbsp;</li>
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<li>Rated orders: 3 out of 3 (100% rating completion)&nbsp;</li>
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<li>Average rating: (5+4+5)/3 = 4.67 (at least 4.0)&nbsp;</li>
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<li>Result: <strong>Golden hour customer</strong></li>
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</ul>
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</li>
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<li><strong>Customer 104</strong>:
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<ul>
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<li>Total orders: 3 (at least 3)&nbsp;</li>
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<li>Peak hour orders: 0 out of 3 (10:00, 09:30, 16:00 all outside peak hours)</li>
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<li>Peak hour percentage: 0/3 = 0% (less than 60%)&nbsp;</li>
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<li>Result: <strong>Not a golden hour customer</strong> (insufficient peak hour orders)</li>
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</ul>
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</li>
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<li><strong>Customer 105</strong>:
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<ul>
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<li>Total orders: 3 (at least 3)&nbsp;</li>
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<li>Peak hour orders: 3 out of 3 (12:15, 13:00, 11:45 all in lunch peak)</li>
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<li>Peak hour percentage: 3/3 = 100% (at least 60%)&nbsp;</li>
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<li>Rated orders: 3 out of 3 (100% rating completion)&nbsp;</li>
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<li>Average rating: (4+5+4)/3 = 4.33 (at least 4.0)&nbsp;</li>
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<li>Result: <strong>Golden hour customer</strong></li>
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</ul>
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</li>
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</ul>
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<p>The results table is ordered by average_rating DESC, then customer_id DESC.</p>
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</div>
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<!-- description:end -->
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## 解法
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<!-- solution:start -->
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### 方法一：分组统计
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我们可以将订单按照 `customer_id` 进行分组，统计每个顾客的总订单数、峰值时段订单数、评分订单数和平均评分，然后根据题目中的条件进行筛选，最后按照平均评分降序、顾客 ID 降序排序。
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<!-- tabs:start -->
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#### MySQL
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```sql
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# Write your MySQL query statement below
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SELECT
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customer_id,
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COUNT(1) total_orders,
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ROUND(
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SUM(
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TIME(order_timestamp) BETWEEN '11:00:00' AND '14:00:00'
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OR TIME(order_timestamp) BETWEEN '18:00:00' AND '21:00:00'
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) / COUNT(1) * 100
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) peak_hour_percentage,
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ROUND(AVG(order_rating), 2) average_rating
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FROM restaurant_orders
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GROUP BY customer_id
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HAVING
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total_orders >= 3
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AND peak_hour_percentage >= 60
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AND average_rating >= 4.0
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AND SUM(order_rating IS NOT NULL) / total_orders >= 0.5
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ORDER BY average_rating DESC, customer_id DESC;
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```
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#### Pandas
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```python
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import pandas as pd
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import numpy as np
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def find_golden_hour_customers(restaurant_orders: pd.DataFrame) -> pd.DataFrame:
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df = restaurant_orders.copy()
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df["order_timestamp"] = pd.to_datetime(df["order_timestamp"])
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df["is_peak_hour"] = df["order_timestamp"].dt.time.between(
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pd.to_datetime("11:00:00").time(), pd.to_datetime("14:00:00").time()
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) | df["order_timestamp"].dt.time.between(
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pd.to_datetime("18:00:00").time(), pd.to_datetime("21:00:00").time()
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)
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grouped = (
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df.groupby("customer_id")
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.agg(
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total_orders=("order_timestamp", "count"),
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peak_hour_count=("is_peak_hour", "sum"),
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average_rating=("order_rating", lambda x: x.dropna().mean()),
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non_null_rating_count=("order_rating", lambda x: x.notna().sum()),
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)
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.reset_index()
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)
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grouped["average_rating"] = grouped["average_rating"].round(2)
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grouped["peak_hour_percentage"] = (
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grouped["peak_hour_count"] / grouped["total_orders"] * 100
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).round()
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filtered = grouped[
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(grouped["total_orders"] >= 3)
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& (grouped["peak_hour_percentage"] >= 60)
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& (grouped["average_rating"] >= 4.0)
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& (grouped["non_null_rating_count"] / grouped["total_orders"] >= 0.5)
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]
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filtered = filtered.sort_values(
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by=["average_rating", "customer_id"], ascending=[False, False]
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)
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return filtered[
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["customer_id", "total_orders", "peak_hour_percentage", "average_rating"]
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]
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```
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<!-- tabs:end -->
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<!-- solution:end -->
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<!-- problem:end -->

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