1. Import the employee data and select the DEPARTMENT and BASE_SALARY columns in a new DataFrame:

>>> employee = pd.read_csv('data/employee.csv')
>>> dept_sal = employee[['DEPARTMENT', 'BASE_SALARY']]

2. Sort this smaller DataFrame by salary within each department:

>>> dept_sal = dept_sal.sort_values(['DEPARTMENT', 'BASE_SALARY'], 
                                      ascending=[True, False])

3. Use the drop_duplicates method to keep the first row of each DEPARTMENT:

>>> max_dept_sal = dept_sal.drop_duplicates(subset='DEPARTMENT')
>>> max_dept_sal.head()

4. Put the DEPARTMENT column into the index for each DataFrames:

>>> max_dept_sal = max_dept_sal.set_index('DEPARTMENT')
>>> employee = employee.set_index('DEPARTMENT')

5. Now that the indexes contain matching values, we can append a new column to the employee DataFrame:

>>> employee['MAX_DEPT_SALARY'] = max_dept_sal['BASE_SALARY']
>>> employee.head()

6. We can validate our results with the query method to check whether there exist any rows where BASE_SALARY is greater than MAX_DEPT_SALARY:

>>> employee.query('BASE_SALARY > MAX_DEPT_SALARY')