Sales Analysis III — LeetCode 1084 Python Solution

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

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

import duckdb
import pandas as pd

def solution(product: pd.DataFrame, sales: pd.DataFrame) -> pd.DataFrame:
    con = duckdb.connect()
    con.register("Product", product)
    con.register("Sales", sales)
    return con.execute("""SELECT product_id, product_name
FROM
    Sales
    JOIN Product USING (product_id)
GROUP BY 1
HAVING COUNT(1) = SUM(sale_date BETWEEN '2019-01-01' AND '2019-03-31');""").df()