Highest salaries difference

# Table: Salaries
# 
# +-------------+---------+
# | Column Name | Type    |
# +-------------+---------+
# | emp_name    | varchar |
# | department  | varchar |
# | salary      | int     |
# +-------------+---------+
# (emp_name, department) is the primary key (combination of unique values) for this table.
# Each row of this table contains emp_name, department and salary. There will be at least one entry for the engineering and marketing departments.
# 
# Write a solution to calculate the difference between the highest salaries in the marketing and engineering department. Output the absolute difference in salaries.
# 
# Return the result table.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Salaries table:
# +----------+-------------+--------+
# | emp_name | department  | salary |
# +----------+-------------+--------+
# | Kathy    | Engineering | 50000  |
# | Roy      | Marketing   | 30000  |
# | Charles  | Engineering | 45000  |
# | Jack     | Engineering | 85000  |
# | Benjamin | Marketing   | 34000  |
# | Anthony  | Marketing   | 42000  |
# | Edward   | Engineering | 102000 |
# | Terry    | Engineering | 44000  |
# | Evelyn   | Marketing   | 53000  |
# | Arthur   | Engineering | 32000  |
# +----------+-------------+--------+
# Output:
# +-------------------+
# | salary_difference |
# +-------------------+
# | 49000             |
# +-------------------+
# Explanation:
# - The Engineering and Marketing departments have the highest salaries of 102,000 and 53,000, respectively. Resulting in an absolute difference of 49,000.

import pandas as pd

def salaries_difference(salaries: pd.DataFrame) -> pd.DataFrame:
  eng_max = salaries[salaries['department'] == 'Engineering']['salary'].max()
  mkt_max = salaries[salaries['department'] == 'Marketing']['salary'].max()
  return pd.DataFrame({'salary_difference': [abs(eng_max - mkt_max)]})