Product sales analysis v

# Table: Sales
# 
# +-------------+-------+
# | Column Name | Type  |
# +-------------+-------+
# | sale_id     | int   |
# | product_id  | int   |
# | user_id     | int   |
# | quantity    | int   |
# +-------------+-------+
# sale_id contains unique values.
# product_id is a foreign key (column with unique values) to Product table.
# Each row of this table shows the ID of the product and the quantity purchased by a user.
# 
#  
# 
# Table: Product
# 
# +-------------+------+
# | Column Name | Type |
# +-------------+------+
# | product_id  | int  |
# | price       | int  |
# +-------------+------+
# product_id contains unique values.
# Each row of this table indicates the price of each product.
# 
#  
# 
# Write a solution to report the spending of each user.
# 
# Return the resulting table ordered by spending in descending order. In case of a tie, order them by user_id in ascending order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Sales table:
# +---------+------------+---------+----------+
# | sale_id | product_id | user_id | quantity |
# +---------+------------+---------+----------+
# | 1       | 1          | 101     | 10       |
# | 2       | 2          | 101     | 1        |
# | 3       | 3          | 102     | 3        |
# | 4       | 3          | 102     | 2        |
# | 5       | 2          | 103     | 3        |
# +---------+------------+---------+----------+
# Product table:
# +------------+-------+
# | product_id | price |
# +------------+-------+
# | 1          | 10    |
# | 2          | 25    |
# | 3          | 15    |
# +------------+-------+
# Output:
# +---------+----------+
# | user_id | spending |
# +---------+----------+
# | 101     | 125      |
# | 102     | 75       |
# | 103     | 75       |
# +---------+----------+
# Explanation:
# User 101 spent 10 * 10 + 1 * 25 = 125.
# User 102 spent 3 * 15 + 2 * 15 = 75.
# User 103 spent 3 * 25 = 75.
# Users 102 and 103 spent the same amount and we break the tie by their ID while user 101 is on the top.

import pandas as pd

def product_sales_analysis(sales: pd.DataFrame, product: pd.DataFrame) -> pd.DataFrame:
  merged = sales.merge(product, on='product_id')
  merged['spending'] = merged['quantity'] * merged['price']
  result = merged.groupby('user_id')['spending'].sum().reset_index()
  result = result.sort_values(['spending', 'user_id'], ascending=[False, True])
  return result