1327. List the Products Ordered in a Period

Description

Pandas Schema

data = [[1, 'Leetcode Solutions', 'Book'], [2, 'Jewels of Stringology', 'Book'], [3, 'HP', 'Laptop'], [4, 'Lenovo', 'Laptop'], [5, 'Leetcode Kit', 'T-shirt']]
products = pd.DataFrame(data, columns=['product_id', 'product_name', 'product_category']).astype({'product_id':'Int64', 'product_name':'object', 'product_category':'object'})
data = [[1, '2020-02-05', 60], [1, '2020-02-10', 70], [2, '2020-01-18', 30], [2, '2020-02-11', 80], [3, '2020-02-17', 2], [3, '2020-02-24', 3], [4, '2020-03-01', 20], [4, '2020-03-04', 30], [4, '2020-03-04', 60], [5, '2020-02-25', 50], [5, '2020-02-27', 50], [5, '2020-03-01', 50]]
orders = pd.DataFrame(data, columns=['product_id', 'order_date', 'unit']).astype({'product_id':'Int64', 'order_date':'datetime64[ns]', 'unit':'Int64'})

Table: Products

+------------------+---------+
| Column Name      | Type    |
+------------------+---------+
| product_id       | int     |
| product_name     | varchar |
| product_category | varchar |
+------------------+---------+
product_id is the primary key (column with unique values) for this table.
This table contains data about the company's products.

Table: Orders

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| product_id    | int     |
| order_date    | date    |
| unit          | int     |
+---------------+---------+
This table may have duplicate rows.
product_id is a foreign key (reference column) to the Products table.
unit is the number of products ordered in order_date.

Write a solution to get the names of products that have at least 100 units ordered in February 2020 and their amount.

Return the result table in any order.

The result format is in the following example.

Example 1:

• Input:

    Products table:
    +-------------+-----------------------+------------------+
    | product_id  | product_name          | product_category |
    +-------------+-----------------------+------------------+
    | 1           | Leetcode Solutions    | Book             |
    | 2           | Jewels of Stringology | Book             |
    | 3           | HP                    | Laptop           |
    | 4           | Lenovo                | Laptop           |
    | 5           | Leetcode Kit          | T-shirt          |
    +-------------+-----------------------+------------------+
    Orders table:
    +--------------+--------------+----------+
    | product_id   | order_date   | unit     |
    +--------------+--------------+----------+
    | 1            | 2020-02-05   | 60       |
    | 1            | 2020-02-10   | 70       |
    | 2            | 2020-01-18   | 30       |
    | 2            | 2020-02-11   | 80       |
    | 3            | 2020-02-17   | 2        |
    | 3            | 2020-02-24   | 3        |
    | 4            | 2020-03-01   | 20       |
    | 4            | 2020-03-04   | 30       |
    | 4            | 2020-03-04   | 60       |
    | 5            | 2020-02-25   | 50       |
    | 5            | 2020-02-27   | 50       |
    | 5            | 2020-03-01   | 50       |
    +--------------+--------------+----------+
    

• Output:

    +--------------------+---------+
    | product_name       | unit    |
    +--------------------+---------+
    | Leetcode Solutions | 130     |
    | Leetcode Kit       | 100     |
    +--------------------+---------+
    

• Explanation:
  Products with product_id = 1 is ordered in February a total of (60 + 70) = 130. Products with product_id = 2 is ordered in February a total of 80. Products with product_id = 3 is ordered in February a total of (2 + 3) = 5. Products with product_id = 4 was not ordered in February 2020. Products with product_id = 5 is ordered in February a total of (50 + 50) = 100.

Submitted Code

import pandas as pd

def list_products(products: pd.DataFrame, orders: pd.DataFrame) -> pd.DataFrame:
    # 테이블 병합
    df = products.merge(orders)
    
    # 2020년 2월만 필터링
    df = df.loc[(df['order_date'] >= '2020-02-01') & (df['order_date'] < '2020-03-01')]
    
    # 제품당 주문 개수
    df = df.groupby('product_name')['unit'].sum().reset_index()
    
    # unit이 100 이상인 제품만 필터링
    df = df.loc[df['unit'] >= 100]
    
    return df

Runtime: 314 ms | Beats 85.52%
Memory: 67.97 MB | Beats 94.80%

product_id 기준으로 두 테이블을 병합하고 product_name 기준으로 그룹화했다.

Other Solutions

1st

SELECT p.product_name AS product_name, sum(o.unit) AS unit FROM Products p
JOIN Orders o USING (product_id)
WHERE YEAR(o.order_date)='2020' AND MONTH(o.order_date)='02'
GROUP BY p.product_id
HAVING SUM(o.unit)>=100

2nd

SELECT 
    p.product_name, 
    SUM(o.unit) AS unit
FROM Products p
JOIN Orders o 
    ON p.product_id = o.product_id
WHERE o.order_date BETWEEN DATE '2020-02-01' AND DATE '2020-02-29'
GROUP BY p.product_name
HAVING SUM(o.unit) >= 100;