Sales analysis i

# 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.
# 
# Table: Sales
# 
# +-------------+---------+
# | Column Name | Type    |
# +-------------+---------+
# | seller_id   | int     |
# | product_id  | int     |
# | buyer_id    | int     |
# | sale_date   | date    |
# | quantity    | int     |
# | price       | int     |
# +-------------+---------+
# This table can have repeated rows.
# product_id is a foreign key (reference column) to the Product table.
# Each row of this table contains some information about one sale.
# 
#  
# 
# Write a solution that reports the best seller by total sales price, If there is a tie, report them all.
# 
# Return the result table in any order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Product table:
# +------------+--------------+------------+
# | product_id | product_name | unit_price |
# +------------+--------------+------------+
# | 1          | S8           | 1000       |
# | 2          | G4           | 800        |
# | 3          | iPhone       | 1400       |
# +------------+--------------+------------+
# Sales table:
# +-----------+------------+----------+------------+----------+-------+
# | seller_id | product_id | buyer_id | sale_date  | quantity | price |
# +-----------+------------+----------+------------+----------+-------+
# | 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  |
# +-----------+------------+----------+------------+----------+-------+
# Output:
# +-------------+
# | seller_id   |
# +-------------+
# | 1           |
# | 3           |
# +-------------+
# Explanation: Both sellers with id 1 and 3 sold products with the most total price of 2800.

import pandas as pd

def sales_analysis(product: pd.DataFrame, sales: pd.DataFrame) -> pd.DataFrame:
  total = sales.groupby('seller_id')['price'].sum()
  max_total = total.max()
  result = total[total == max_total].reset_index()
  return result[['seller_id']]