3705. Find Golden Hour Customers

题目描述

Table: restaurant_orders

+------------------+----------+
| Column Name      | Type     |
+------------------+----------+
| order_id         | int      |
| customer_id      | int      |
| order_timestamp  | datetime |
| order_amount     | decimal  |
| payment_method   | varchar  |
| order_rating     | int      |
+------------------+----------+
order_id is the unique identifier for this table.
payment_method can be cash, card, or app.
order_rating is between 1 and 5, where 5 is the best (NULL if not rated).
order_timestamp contains both date and time information.

Write a solution to find golden hour customers - customers who consistently order during peak hours and provide high satisfaction. A customer is a golden hour customer if they meet ALL the following criteria:

Made at least 3 orders.
At least 60% of their orders are during peak hours (11:00-14:00 or 18:00-21:00).
Their average rating for rated orders is at least 4.0, round it to 2 decimal places.
Have rated at least 50% of their orders.

Return the result table ordered by average_rating in descending order, then by customer_id in descending order.

The result format is in the following example.

Example:

Input:

restaurant_orders table:

+----------+-------------+---------------------+--------------+----------------+--------------+
| order_id | customer_id | order_timestamp     | order_amount | payment_method | order_rating |
+----------+-------------+---------------------+--------------+----------------+--------------+
| 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            |
+----------+-------------+---------------------+--------------+----------------+--------------+

Output:

+-------------+--------------+----------------------+----------------+
| customer_id | total_orders | peak_hour_percentage | average_rating |
+-------------+--------------+----------------------+----------------+
| 103         | 3            | 100                  | 4.67           |
| 101         | 4            | 75                   | 4.67           |
| 105         | 3            | 100                  | 4.33           |
+-------------+--------------+----------------------+----------------+

Explanation:

Customer 101:
- Total orders: 4 (at least 3)
- Peak hour orders: 3 out of 4 (12:30, 19:15, 13:45, and 20:30 are in peak hours)
- Peak hour percentage: 3/4 = 75% (at least 60%)
- Rated orders: 3 out of 4 (75% rating completion)
- Average rating: (5+4+5)/3 = 4.67 (at least 4.0)
- Result: Golden hour customer
Customer 102:
- Total orders: 3 (at least 3)
- Peak hour orders: 2 out of 3 (11:30, 12:00 are in peak hours; 15:30 is not)
- Peak hour percentage: 2/3 = 66.67% (at least 60%)
- Rated orders: 2 out of 3 (66.67% rating completion)
- Average rating: (4+3)/2 = 3.5 (less than 4.0)
- Result: Not a golden hour customer (average rating too low)
Customer 103:
- Total orders: 3 (at least 3)
- Peak hour orders: 3 out of 3 (19:00, 20:45, 18:30 all in evening peak)
- Peak hour percentage: 3/3 = 100% (at least 60%)
- Rated orders: 3 out of 3 (100% rating completion)
- Average rating: (5+4+5)/3 = 4.67 (at least 4.0)
- Result: Golden hour customer
Customer 104:
- Total orders: 3 (at least 3)
- Peak hour orders: 0 out of 3 (10:00, 09:30, 16:00 all outside peak hours)
- Peak hour percentage: 0/3 = 0% (less than 60%)
- Result: Not a golden hour customer (insufficient peak hour orders)
Customer 105:
- Total orders: 3 (at least 3)
- Peak hour orders: 3 out of 3 (12:15, 13:00, 11:45 all in lunch peak)
- Peak hour percentage: 3/3 = 100% (at least 60%)
- Rated orders: 3 out of 3 (100% rating completion)
- Average rating: (4+5+4)/3 = 4.33 (at least 4.0)
- Result: Golden hour customer

The results table is ordered by average_rating DESC, then customer_id DESC.

解法

方法一：分组统计

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。

MySQLPython3

# Write your MySQL query statement below
SELECT
    customer_id,
    COUNT(1) total_orders,
    ROUND(
        SUM(
            TIME(order_timestamp) BETWEEN '11:00:00' AND '14:00:00'
            OR TIME(order_timestamp) BETWEEN '18:00:00' AND '21:00:00'
        ) / COUNT(1) * 100
    ) peak_hour_percentage,
    ROUND(AVG(order_rating), 2) average_rating
FROM restaurant_orders
GROUP BY customer_id
HAVING
    total_orders >= 3
    AND peak_hour_percentage >= 60
    AND average_rating >= 4.0
    AND SUM(order_rating IS NOT NULL) / total_orders >= 0.5
ORDER BY average_rating DESC, customer_id DESC;

import pandas as pd
import numpy as np


def find_golden_hour_customers(restaurant_orders: pd.DataFrame) -> pd.DataFrame:
    df = restaurant_orders.copy()
    df["order_timestamp"] = pd.to_datetime(df["order_timestamp"])
    df["is_peak_hour"] = df["order_timestamp"].dt.time.between(
        pd.to_datetime("11:00:00").time(), pd.to_datetime("14:00:00").time()
    ) | df["order_timestamp"].dt.time.between(
        pd.to_datetime("18:00:00").time(), pd.to_datetime("21:00:00").time()
    )
    grouped = (
        df.groupby("customer_id")
        .agg(
            total_orders=("order_timestamp", "count"),
            peak_hour_count=("is_peak_hour", "sum"),
            average_rating=("order_rating", lambda x: x.dropna().mean()),
            non_null_rating_count=("order_rating", lambda x: x.notna().sum()),
        )
        .reset_index()
    )
    grouped["average_rating"] = grouped["average_rating"].round(2)
    grouped["peak_hour_percentage"] = (
        grouped["peak_hour_count"] / grouped["total_orders"] * 100
    ).round()
    filtered = grouped[
        (grouped["total_orders"] >= 3)
        & (grouped["peak_hour_percentage"] >= 60)
        & (grouped["average_rating"] >= 4.0)
        & (grouped["non_null_rating_count"] / grouped["total_orders"] >= 0.5)
    ]
    filtered = filtered.sort_values(
        by=["average_rating", "customer_id"], ascending=[False, False]
    )
    return filtered[
        ["customer_id", "total_orders", "peak_hour_percentage", "average_rating"]
    ]

3705. Find Golden Hour Customers

题目描述

解法

方法一：分组统计

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