This article is going to provide the solution to LeetCode 181 Employees Earning More Than Their Managers. It’s an easy difficulty question.
Write a solution to find the employees who earn more than their managers.
Return the result table in any order.
The result format is in the following example.
If you want to skip the article, check out the full YouTube video where I code the answer live.
LeetCode 181 SQL Solution
select e1.name as Employee
from Employee E1
left join Employee E2 on E2.id = E1.managerId
where e2.salary < e1.salary
The first part of the code selects the name of the employees from the Employee table (E1) and aliases it as Employee
Next perform a left join between the Employee table (E1) and itself (aliased as E2) based on the condition that the id of E2 matches the managerId of E1. This join is used to connect each employee with their respective manager.
Lastly, filter the joined result set to only include rows where the salary of the manager (E2.salary) is less than the salary of the employee (E1.salary).
LeetCode 181 Python Solution
def find_employees(employee: pd.DataFrame) -> pd.DataFrame:
results = pd.merge(employee, employee, left_on='managerId', right_on='id', suffixes=('_employee', '_manager'))
results2 = results[results['salary_employee'] > results['salary_manager']]
results2.rename(columns={'name_employee': 'Employee'}, inplace=True)
return results2[['Employee']]
LeetCode gives us a defined function named find_employees that takes a pandas DataFrame named employee as input and returns another pandas DataFrame.
The first line merges the employee DataFrame with itself based on two columns: managerId from the left DataFrame (aliased as _employee) and id from the right DataFrame (aliased as _manager). The result is stored in the results DataFrame.
Next, filter the results DataFrame to include only rows where the salary of the employee is greater than the salary of their manager. The filtered result is stored in results2
Rename the column name_employee to Employee.
Lastly, return the Employee column from the results2 DataFrame
