{ "data": { "question": { "questionId": "1174", "questionFrontendId": "1084", "boundTopicId": null, "title": "Sales Analysis III", "titleSlug": "sales-analysis-iii", "content": "

Table: Product

\n\n
\n+--------------+---------+\n| Column Name  | Type    |\n+--------------+---------+\n| product_id   | int     |\n| product_name | varchar |\n| unit_price   | int     |\n+--------------+---------+\nproduct_id is the primary key (column with unique values) of this table.\nEach row of this table indicates the name and the price of each product.\n
\n\n

Table: Sales

\n\n
\n+-------------+---------+\n| Column Name | Type    |\n+-------------+---------+\n| seller_id   | int     |\n| product_id  | int     |\n| buyer_id    | int     |\n| sale_date   | date    |\n| quantity    | int     |\n| price       | int     |\n+-------------+---------+\nThis table can have duplicate rows.\nproduct_id is a foreign key (reference column) to the Product table.\nEach row of this table contains some information about one sale.\n
\n\n

 

\n\n

Write a solution to report the products that were only sold in the first quarter of 2019. That is, between 2019-01-01 and 2019-03-31 inclusive.

\n\n

Return the result table in any order.

\n\n

The result format is in the following example.

\n\n

 

\n

Example 1:

\n\n
\nInput: \nProduct table:\n+------------+--------------+------------+\n| product_id | product_name | unit_price |\n+------------+--------------+------------+\n| 1          | S8           | 1000       |\n| 2          | G4           | 800        |\n| 3          | iPhone       | 1400       |\n+------------+--------------+------------+\nSales table:\n+-----------+------------+----------+------------+----------+-------+\n| seller_id | product_id | buyer_id | sale_date  | quantity | price |\n+-----------+------------+----------+------------+----------+-------+\n| 1         | 1          | 1        | 2019-01-21 | 2        | 2000  |\n| 1         | 2          | 2        | 2019-02-17 | 1        | 800   |\n| 2         | 2          | 3        | 2019-06-02 | 1        | 800   |\n| 3         | 3          | 4        | 2019-05-13 | 2        | 2800  |\n+-----------+------------+----------+------------+----------+-------+\nOutput: \n+-------------+--------------+\n| product_id  | product_name |\n+-------------+--------------+\n| 1           | S8           |\n+-------------+--------------+\nExplanation: \nThe product with id 1 was only sold in the spring of 2019.\nThe product with id 2 was sold in the spring of 2019 but was also sold after the spring of 2019.\nThe product with id 3 was sold after spring 2019.\nWe return only product 1 as it is the product that was only sold in the spring of 2019.\n
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"sampleTestCase": "{\"headers\":{\"Product\":[\"product_id\",\"product_name\",\"unit_price\"],\"Sales\":[\"seller_id\",\"product_id\",\"buyer_id\",\"sale_date\",\"quantity\",\"price\"]},\"rows\":{\"Product\":[[1,\"S8\",1000],[2,\"G4\",800],[3,\"iPhone\",1400]],\"Sales\":[[1,1,1,\"2019-01-21\",2,2000],[1,2,2,\"2019-02-17\",1,800],[2,2,3,\"2019-06-02\",1,800],[3,3,4,\"2019-05-13\",2,2800]]}}", "metaData": "{\"mysql\": [\"Create table If Not Exists Product (product_id int, product_name varchar(10), unit_price int)\", \"Create table If Not Exists Sales (seller_id int, product_id int, buyer_id int, sale_date date, quantity int, price int)\"], \"mssql\": [\"Create table Product (product_id int, product_name varchar(10), unit_price int)\", \"Create table Sales (seller_id int, product_id int, buyer_id int, sale_date date, quantity int, price int)\"], \"oraclesql\": [\"Create table Product (product_id int, product_name varchar(10), unit_price int)\", \"Create table Sales (seller_id int, product_id int, buyer_id int, sale_date date, quantity int, price int)\", \"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"], \"database\": true, \"name\": \"sales_analysis\", \"pythondata\": [\"Product = pd.DataFrame([], columns=['product_id', 'product_name', 'unit_price']).astype({'product_id':'Int64', 'product_name':'object', 'unit_price':'Int64'})\", \"Sales = pd.DataFrame([], columns=['seller_id', 'product_id', 'buyer_id', 'sale_date', 'quantity', 'price']).astype({'seller_id':'Int64', 'product_id':'Int64', 'buyer_id':'Int64', 'sale_date':'datetime64[ns]', 'quantity':'Int64', 'price':'Int64'})\"], \"postgresql\": [\"Create table If Not Exists Product (product_id int, product_name varchar(10), unit_price int)\\n\", \"Create table If Not Exists Sales (seller_id int, product_id int, buyer_id int, sale_date date, quantity int, price int)\"], \"database_schema\": {\"Product\": {\"product_id\": \"INT\", \"product_name\": \"VARCHAR(10)\", \"unit_price\": \"INT\"}, \"Sales\": {\"seller_id\": \"INT\", \"product_id\": \"INT\", \"buyer_id\": \"INT\", \"sale_date\": \"DATE\", \"quantity\": \"INT\", \"price\": \"INT\"}}}", "judgerAvailable": true, "judgeType": "large", "mysqlSchemas": [ "Create table If Not Exists Product (product_id int, product_name varchar(10), unit_price int)", "Create table If Not Exists Sales (seller_id int, product_id int, buyer_id int, sale_date date, quantity int, price int)", "Truncate table Product", "insert into Product (product_id, product_name, unit_price) values ('1', 'S8', '1000')", "insert into Product (product_id, product_name, unit_price) values ('2', 'G4', '800')", "insert into Product (product_id, product_name, unit_price) values ('3', 'iPhone', '1400')", "Truncate table Sales", "insert into Sales (seller_id, product_id, buyer_id, sale_date, quantity, price) values ('1', '1', '1', '2019-01-21', '2', '2000')", "insert into Sales (seller_id, product_id, buyer_id, sale_date, quantity, price) values ('1', '2', '2', '2019-02-17', '1', '800')", "insert into Sales (seller_id, product_id, buyer_id, sale_date, quantity, price) values ('2', '2', '3', '2019-06-02', '1', '800')", "insert into Sales (seller_id, product_id, buyer_id, sale_date, quantity, price) values ('3', '3', '4', '2019-05-13', '2', '2800')" ], "enableRunCode": true, "enableTestMode": false, "enableDebugger": false, "envInfo": "{\"mysql\": [\"MySQL\", \"

MySQL 8.0.

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mssql server 2019.

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Oracle Sql 11.2.

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Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0

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PostgreSQL 16

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