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  • LeetCode SQL 50 Solution/1978. Employees Whose Manager Left the Company

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Below is a well-structured `README.md` for **LeetCode 1978 - Employees Whose Manager Left the Company**. It includes a detailed explanation of the SQL solution and a corresponding Python (Pandas) solution.
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```md
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# 🏢 Employees Whose Manager Left the Company - LeetCode 1978
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## 📌 Problem Statement
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You are given a table **Employees** that contains information about employees, including their salary and the ID of the manager they report to.
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When a manager leaves the company, their row is deleted from the **Employees** table, but the `manager_id` in the records of their reports still remains.
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Your task is to **find the IDs of employees** who:
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- Have a salary **strictly less than 30000ドル**.
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- Have a **manager** (i.e., `manager_id` is not `NULL`) whose record is **missing** in the table (i.e., the manager left the company).
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Return the result table **ordered by `employee_id`** in ascending order.
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---
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## 📊 Table Structure
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### **Employees Table**
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| Column Name | Type |
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| ----------- | ------- |
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| employee_id | int |
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| name | varchar |
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| manager_id | int |
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| salary | int |
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- `employee_id` is the **primary key**.
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- `manager_id` may be `NULL` if an employee does not have a manager.
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- When a manager leaves, their row is deleted, but the `manager_id` remains in the reports' records.
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---
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## 📊 Example 1:
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### **Input:**
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#### **Employees Table**
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| employee_id | name | manager_id | salary |
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| ----------- | --------- | ---------- | ------ |
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| 3 | Mila | 9 | 60301 |
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| 12 | Antonella | NULL | 31000 |
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| 13 | Emery | NULL | 67084 |
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| 1 | Kalel | 11 | 21241 |
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| 9 | Mikaela | NULL | 50937 |
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| 11 | Joziah | 6 | 28485 |
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### **Output:**
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| employee_id |
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| ----------- |
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| 11 |
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### **Explanation:**
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- **Employees with salary < 30000ドル:**
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- **Kalel (ID 1)** with salary 21241, whose manager is employee 11.
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- **Joziah (ID 11)** with salary 28485, whose manager is employee 6.
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- **Kalel's manager (ID 11)** is still in the table.
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- **Joziah's manager (ID 6)** is missing from the table, meaning that manager left the company.
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Thus, only **employee 11 (Joziah)** meets the criteria.
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---
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## 🖥 SQL Solution
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### ✅ **Approach:**
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1. **Self-Join:**
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- Use a `LEFT JOIN` on the **Employees** table with itself to check if an employee's manager exists.
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- Alias `e1` represents the employee, and alias `e2` represents the manager.
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2. **Filter Conditions:**
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- The employee's `salary` must be strictly less than 30000.
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- The employee must have a manager (`e1.manager_id IS NOT NULL`).
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- The join should fail for the manager (`e2.employee_id IS NULL`), indicating the manager left.
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3. **Order the Result:**
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- Order the final result by `employee_id`.
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### ✅ **SQL Query:**
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```sql
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SELECT e1.employee_id
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FROM Employees AS e1
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LEFT JOIN Employees AS e2 ON e1.manager_id = e2.employee_id
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WHERE e1.salary < 30000
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AND e1.manager_id IS NOT NULL
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AND e2.employee_id IS NULL
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ORDER BY e1.employee_id;
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```
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---
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## 🐍 Python (Pandas) Solution
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### ✅ **Approach:**
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1. **Self-Merge:**
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- Merge the **Employees** DataFrame with itself on `manager_id` (from the employee side) and `employee_id` (from the manager side) using a left join.
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2. **Filter Rows:**
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- Keep rows where:
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- `salary` is less than 30000.
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- `manager_id` is not null.
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- The merged manager information is missing (i.e., the manager left).
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3. **Sort Result:**
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- Sort the result by `employee_id`.
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### ✅ **Pandas Code:**
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```python
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import pandas as pd
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def employees_with_left_manager(employees: pd.DataFrame) -> pd.DataFrame:
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# Perform a left merge on the Employees table to find existing managers
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merged = employees.merge(
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employees[['employee_id']],
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left_on='manager_id',
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right_on='employee_id',
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how='left',
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suffixes=('', '_manager')
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)
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# Filter: salary < 30000, manager_id is not null, and manager does not exist (NaN in employee_id_manager)
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filtered = merged[
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(merged['salary'] < 30000) &
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(merged['manager_id'].notnull()) &
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(merged['employee_id_manager'].isna())
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]
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# Select the required column and sort by employee_id
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result = filtered[['employee_id']].sort_values('employee_id')
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return result
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# Example usage:
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# employees_df = pd.read_csv("employees.csv")
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# print(employees_with_left_manager(employees_df))
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```
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---
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## 📁 File Structure
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```
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📂 Employees-Manager-Left
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│── README.md
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│── solution.sql
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│── solution_pandas.py
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│── test_cases.sql
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│── sample_data.csv
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```
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---
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## 🔗 Useful Links
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- 📖 [LeetCode Problem](https://leetcode.com/problems/employees-whose-manager-left-the-company/)
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- 🔍 [MySQL LEFT JOIN Documentation](https://www.w3schools.com/sql/sql_join_left.asp)
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- 🐍 [Pandas Merge Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html)
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```
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---
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### Features of this `README.md`:
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- **Clear problem description** with table structure and an example.
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- **SQL solution** with step-by-step explanation.
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- **Python (Pandas) solution** with detailed code and explanation.
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- **Organized file structure** and useful external links for further learning.
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Let me know if you need any further modifications or additions!

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