Leetcode #2668: Find Latest Salaries
In this guide, we solve Leetcode #2668 Find Latest Salaries in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
Table: Salary +---------------+---------+ | Column Name | Type | +---------------+---------+ | emp_id | int | | firstname | varchar | | lastname | varchar | | salary | varchar | | department_id | varchar | +---------------+---------+ (emp_id, salary) is the primary key (combination of columns with unique values) for this table. Each row contains employees details and their yearly salaries, however, some of the records are old and contain outdated salary information.
Quick Facts
- Difficulty: Easy
- Premium: Yes
- Tags: Database
Intuition
The task is relational in nature, which maps cleanly to DataFrame operations in Python.
By treating tables as DataFrames, joins and group-bys become concise and readable.
Approach
Load the inputs as DataFrames and apply the appropriate merge, filter, or group-by.
Select or rename the columns to match the required output.
Steps:
- Load inputs as DataFrames.
- Apply merge/groupby/filter operations.
- Select the output columns.
Example
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| emp_id | int |
| firstname | varchar |
| lastname | varchar |
| salary | varchar |
| department_id | varchar |
+---------------+---------+
(emp_id, salary) is the primary key (combination of columns with unique values) for this table.
Each row contains employees details and their yearly salaries, however, some of the records are old and contain outdated salary information.
Python Solution
import duckdb
import pandas as pd
# Pass input tables as keyword arguments matching the SQL table names.
def solution(**tables) -> pd.DataFrame:
con = duckdb.connect()
for name, df in tables.items():
con.register(name, df)
return con.execute("""SELECT
emp_id,
firstname,
lastname,
MAX(salary) AS salary,
department_id
FROM Salary
GROUP BY emp_id
ORDER BY emp_id;""").df()
Complexity
The time complexity is O(n log n) (typical). The space complexity is O(n).
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.