即时食物配送 II

1174. 即时食物配送 II

配送表: Delivery

+-----------------------------+---------+
| Column Name                 | Type    |
+-----------------------------+---------+
| delivery_id                 | int     |
| customer_id                 | int     |
| order_date                  | date    |
| customer_pref_delivery_date | date    |
+-----------------------------+---------+
delivery_id 是表的主键。
该表保存着顾客的食物配送信息，顾客在某个日期下了订单，并指定了一个期望的配送日期（和下单日期相同或者在那之后）。

如果顾客期望的配送日期和下单日期相同，则该订单称为「即时订单」，否则称为「计划订单」。

「首次订单」是顾客最早创建的订单。我们保证一个顾客只会有一个「首次订单」。

写一条 SQL 查询语句获取即时订单在所有用户的首次订单中的比例。保留两位小数。

查询结果如下所示：

Delivery 表：
+-------------+-------------+------------+-----------------------------+
| delivery_id | customer_id | order_date | customer_pref_delivery_date |
+-------------+-------------+------------+-----------------------------+
| 1           | 1           | 2019-08-01 | 2019-08-02                  |
| 2           | 2           | 2019-08-02 | 2019-08-02                  |
| 3           | 1           | 2019-08-11 | 2019-08-12                  |
| 4           | 3           | 2019-08-24 | 2019-08-24                  |
| 5           | 3           | 2019-08-21 | 2019-08-22                  |
| 6           | 2           | 2019-08-11 | 2019-08-13                  |
| 7           | 4           | 2019-08-09 | 2019-08-09                  |
+-------------+-------------+------------+-----------------------------+

Result 表：
+----------------------+
| immediate_percentage |
+----------------------+
| 50.00                |
+----------------------+
1 号顾客的 1 号订单是首次订单，并且是计划订单。
2 号顾客的 2 号订单是首次订单，并且是即时订单。
3 号顾客的 5 号订单是首次订单，并且是计划订单。
4 号顾客的 7 号订单是首次订单，并且是即时订单。
因此，一半顾客的首次订单是即时的。

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# Write your MySQL query statement below

pythondata 解法, 执行用时: 400 ms, 内存消耗: 67.6 MB, 提交时间: 2024-05-27 12:56:46

import pandas as pd

def immediate_food_delivery2(delivery: pd.DataFrame) -> pd.DataFrame:
    delivery.sort_values(by='order_date',inplace=True) #对日期进行升序排序
    delivery.drop_duplicates(subset='customer_id',keep='first',inplace=True) #去除顾客号相同的并保留第一个记录
    equal=delivery[delivery['order_date']==delivery['customer_pref_delivery_date']] #找到即时订单
    bili=round(len(equal)/len(delivery)*100,2)
    return pd.DataFrame({'immediate_percentage':[bili]})
    

def immediate_food_delivery(delivery: pd.DataFrame) -> pd.DataFrame:
    # 使用groupby和min找到每个客户的首次订单日期
    first_order_dates = delivery.groupby('customer_id')['order_date'].min().reset_index()

    # 合并首次订单日期回到原始数据集，以便筛选出首次订单的记录
    first_orders = pd.merge(delivery, first_order_dates, on=['customer_id', 'order_date'], how='inner')

    # 计算订单日期与客户首选配送日期相同的情况的百分比
    immediate_delivery_percentage = round(100 * (first_orders['order_date'] == first_orders['customer_pref_delivery_date']).sum() / len(first_orders), 2)

    # 创建包含结果的DataFrame
    result_df = pd.DataFrame({'immediate_percentage': [immediate_delivery_percentage]})

    return result_df

mysql 解法, 执行用时: 416 ms, 内存消耗: 0 B, 提交时间: 2023-04-02 12:10:25

# Write your MySQL query statement below
SELECT ROUND(COUNT(a.customer_id)/COUNT(b.customer_id)*100,2) AS 'immediate_percentage'
FROM (
    SELECT customer_id,MIN(order_date) AS 'fir_ins_o'
    FROM Delivery
    WHERE order_date = customer_pref_delivery_date
    GROUP BY customer_id
)a RIGHT JOIN (
    SELECT customer_id,MIN(order_date) AS 'fir_o'
    FROM Delivery
    GROUP BY customer_id
)b ON a.customer_id = b.customer_id AND a.fir_ins_o = b.fir_o

mysql 解法, 执行用时: 396 ms, 内存消耗: 0 B, 提交时间: 2023-04-02 12:09:52

# Write your MySQL query statement below
select round (
    sum(order_date = customer_pref_delivery_date) * 100 /
    count(*),
    2
) as immediate_percentage
from Delivery
where (customer_id, order_date) in (
    select customer_id, min(order_date)
    from delivery
    group by customer_id
);

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