Leetcode HARD 3236 - RECURSIVE CTE SQL Explained - CEO Subordinate Hierarchy | Everyday Data Science

Question: https://leetcode.com/problems/ceo-subordinate-hierarchy/ SQL Schema: Create table if not exists employees(employee_id int, employee_name varchar(100), manager_id int, salary int) Truncate table Employees insert into Employees (employee_id, employee_name, manager_id, salary) values ('1', 'Alice', NULL, '150000') insert into Employees (employee_id, employee_name, manager_id, salary) values ('2', 'Bob', '1', '120000') insert into Employees (employee_id, employee_name, manager_id, salary) values ('3', 'Charlie', '1', '110000') insert into Employees (employee_id, employee_name, manager_id, salary) values ('4', 'David', '2', '105000') insert into Employees (employee_id, employee_name, manager_id, salary) values ('5', 'Eve', '2', '100000') insert into Employees (employee_id, employee_name, manager_id, salary) values ('6', 'Frank', '3', '95000') insert into Employees (employee_id, employee_name, manager_id, salary) values ('7', 'Grace', '3', '98000') insert into Employees (employee_id, employee_name, manager_id, salary) values ('8', 'Helen', '5', '90000') Pandas Schema: data = [[1, 'Alice', None, 150000], [2, 'Bob', 1, 120000], [3, 'Charlie', 1, 110000], [4, 'David', 2, 105000], [5, 'Eve', 2, 100000], [6, 'Frank', 3, 95000], [7, 'Grace', 3, 98000], [8, 'Helen', 5, 90000]] employees = pd.DataFrame(columns=['employee_id', 'employee_name', 'manager_id', 'salary']).astype({ 'employee_id': pd.Int64Dtype(), 'employee_name': 'str', 'manager_id': pd.Int64Dtype(), 'salary': pd.Int64Dtype() }) #leetcode #datascience #tutorial