Last updated on October 5th, 2024 at 04:35 pm
This Leetcode problem Product Sales Analysis I LeetCode Solution is done in SQL.
List of all LeetCode Solution
Level of Question
Easy
Product Sales Analysis I LeetCode Solution
Table of Contents
Problem Statement
Column Name | Type |
sale_id | int |
product_id | int |
year | int |
quantity | int |
price | int |
Sales
(sale_id, year) is the primary key (combination of columns with unique values) of this table. product_id is a foreign key (reference column) to Product
table.
Each row of this table shows a sale on the product product_id in a certain year. Note that the price is per unit.
Column Name | Type |
product_id | int |
product_name | varchar |
Product
product_id is the primary key (column with unique values) of this table. Each row of this table indicates the product name of each product.
Write a solution to report the product_name
, year
, and price
for each sale_id
in the Sales
table.
Return the resulting table in any order.
The result format is in the following example.
Example 1:
Input:
sale_id | product_id | year | quantity | price |
1 | 100 | 2008 | 10 | 5000 |
2 | 100 | 2009 | 12 | 5000 |
7 | 200 | 2011 | 15 | 9000 |
product_id | product_name |
100 | Nokia |
200 | Apple |
300 | Samsung |
Output:
product_name | year | price |
Nokia | 2008 | 5000 |
Nokia | 2009 | 5000 |
Apple | 2011 | 9000 |
Explanation: From sale_id = 1, we can conclude that Nokia was sold for 5000 in the year 2008. From sale_id = 2, we can conclude that Nokia was sold for 5000 in the year 2009. From sale_id = 7, we can conclude that Apple was sold for 9000 in the year 2011.
1. Product Sales Analysis I LeetCode Solution MySQL
select distinct P.product_name, S.year, S.price from ( select distinct product_id, year, price from Sales ) S inner join Product as P using (product_id);
2. Product Sales Analysis I LeetCode Solution Pandas
import pandas as pd def sales_analysis(sales: pd.DataFrame, product: pd.DataFrame) -> pd.DataFrame: sales_and_product = sales.merge( product, on=["product_id"] ) df = sales_and_product[['product_name', 'year', 'price']] return df