Calculate the influence of each salesperson

# Table: Salesperson
# 
# +----------------+---------+
# | Column Name    | Type    |
# +----------------+---------+
# | salesperson_id | int     |
# | name           | varchar |
# +----------------+---------+
# salesperson_id contains unique values.
# Each row in this table shows the ID of a salesperson.
# 
#  
# 
# Table: Customer
# 
# +----------------+------+
# | Column Name    | Type |
# +----------------+------+
# | customer_id    | int  |
# | salesperson_id | int  |
# +----------------+------+
# customer_id contains unique values.
# salesperson_id is a foreign key (reference column) from the Salesperson table.
# Each row in this table shows the ID of a customer and the ID of the salesperson.
# 
#  
# 
# Table: Sales
# 
# +-------------+------+
# | Column Name | Type |
# +-------------+------+
# | sale_id     | int  |
# | customer_id | int  |
# | price       | int  |
# +-------------+------+
# sale_id contains unique values.
# customer_id is a foreign key (reference column) from the Customer table.
# Each row in this table shows ID of a customer and the price they paid for the sale with sale_id.
# 
#  
# 
# Write a solution to report the sum of prices paid by the customers of each salesperson. If a salesperson does not have any customers, the total value should be 0.
# 
# Return the result table in any order.
# 
# The result format is shown in the following example.
#
# Example 1:
# Input:
# Salesperson table:
# +----------------+-------+
# | salesperson_id | name  |
# +----------------+-------+
# | 1              | Alice |
# | 2              | Bob   |
# | 3              | Jerry |
# +----------------+-------+
# Customer table:
# +-------------+----------------+
# | customer_id | salesperson_id |
# +-------------+----------------+
# | 1           | 1              |
# | 2           | 1              |
# | 3           | 2              |
# +-------------+----------------+
# Sales table:
# +---------+-------------+-------+
# | sale_id | customer_id | price |
# +---------+-------------+-------+
# | 1       | 2           | 892   |
# | 2       | 1           | 354   |
# | 3       | 3           | 988   |
# | 4       | 3           | 856   |
# +---------+-------------+-------+
# Output:
# +----------------+-------+-------+
# | salesperson_id | name  | total |
# +----------------+-------+-------+
# | 1              | Alice | 1246  |
# | 2              | Bob   | 1844  |
# | 3              | Jerry | 0     |
# +----------------+-------+-------+
# Explanation:
# Alice is the salesperson for customers 1 and 2.
# - Customer 1 made one purchase with 354.
# - Customer 2 made one purchase with 892.
# The total for Alice is 354 + 892 = 1246.
# 
# Bob is the salesperson for customers 3.
# - Customer 1 made one purchase with 988 and 856.
# The total for Bob is 988 + 856 = 1844.
# 
# Jerry is not the salesperson of any customer.
# The total for Jerry is 0.

import pandas as pd

def calculate_influence(salesperson: pd.DataFrame, customer: pd.DataFrame, sales: pd.DataFrame) -> pd.DataFrame:
  merged = customer.merge(sales, on='customer_id', how='left')
  totals = merged.groupby('salesperson_id')['price'].sum().reset_index()
  totals.columns = ['salesperson_id', 'total']
  result = salesperson.merge(totals, on='salesperson_id', how='left')
  result['total'] = result['total'].fillna(0).astype(int)
  return result[['salesperson_id', 'name', 'total']]