Product sales analysis ii

# Table: Sales
# 
# +-------------+-------+
# | Column Name | Type  |
# +-------------+-------+
# | sale_id     | int   |
# | product_id  | int   |
# | year        | int   |
# | quantity    | int   |
# | price       | int   |
# +-------------+-------+
# (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.
# 
#  
# 
# Table: Product
# 
# +--------------+---------+
# | Column Name  | Type    |
# +--------------+---------+
# | product_id   | int     |
# | product_name | varchar |
# +--------------+---------+
# 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 that reports the total quantity sold for every product id.
# 
# Return the resulting table in any order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Sales table:
# +---------+------------+------+----------+-------+
# | sale_id | product_id | year | quantity | price |
# +---------+------------+------+----------+-------+
# | 1       | 100        | 2008 | 10       | 5000  |
# | 2       | 100        | 2009 | 12       | 5000  |
# | 7       | 200        | 2011 | 15       | 9000  |
# +---------+------------+------+----------+-------+
# Product table:
# +------------+--------------+
# | product_id | product_name |
# +------------+--------------+
# | 100        | Nokia        |
# | 200        | Apple        |
# | 300        | Samsung      |
# +------------+--------------+
# Output:
# +--------------+----------------+
# | product_id   | total_quantity |
# +--------------+----------------+
# | 100          | 22             |
# | 200          | 15             |
# +--------------+----------------+

import pandas as pd

def sales_analysis(sales: pd.DataFrame, product: pd.DataFrame) -> pd.DataFrame:
  return sales.groupby('product_id', as_index=False)['quantity'].sum().rename(columns={'quantity': 'total_quantity'})