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

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

LeetCode SQL 50 Solution/2356. Number of Unique Subjects Taught by Each Teacher/readme.md

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+# 📚 Number of Unique Subjects Taught by Each Teacher - LeetCode 2356
+
+## 📌 Problem Statement
+You are given a table **Teacher** that provides information about the subjects taught by teachers in various departments of a university.
+
+Your task is to calculate the **number of unique subjects** each teacher teaches.  
+Note that the table can have multiple rows for the same subject taught in different departments, but you should count each subject only once per teacher.
+
+Return the result table in **any order**.
+
+---
+
+## 📊 Table Structure
+
+### **Teacher Table**
+| Column Name | Type |
+| ----------- | ---- |
+| teacher_id  | int  |
+| subject_id  | int  |
+| dept_id     | int  |
+
+- `(subject_id, dept_id)` is the **primary key**.
+- Each row indicates that the teacher with `teacher_id` teaches the subject `subject_id` in the department `dept_id`.
+
+---
+
+## 📊 Example 1:
+
+### **Input:**
+#### **Teacher Table**
+| teacher_id | subject_id | dept_id |
+| ---------- | ---------- | ------- |
+| 1          | 2          | 3       |
+| 1          | 2          | 4       |
+| 1          | 3          | 3       |
+| 2          | 1          | 1       |
+| 2          | 2          | 1       |
+| 2          | 3          | 1       |
+| 2          | 4          | 1       |
+
+### **Output:**
+| teacher_id | cnt |
+| ---------- | --- |
+| 1          | 2   |
+| 2          | 4   |
+
+### **Explanation:**
+- **Teacher 1:**  
+  - Teaches subject **2** (in departments 3 and 4) and subject **3** (in department 3).  
+  - Unique subjects = {2, 3} → **2 subjects**.
+- **Teacher 2:**  
+  - Teaches subjects **1**, **2**, **3**, and **4** (all in department 1).  
+  - Unique subjects = {1, 2, 3, 4} → **4 subjects**.
+
+---
+
+## 🖥 SQL Solution
+
+### ✅ **Approach:**
+- Use `COUNT(DISTINCT subject_id)` to count the number of unique subjects taught by each teacher.
+- Group the results by `teacher_id`.
+
+```sql
+SELECT teacher_id, COUNT(DISTINCT subject_id) AS cnt
+FROM Teacher
+GROUP BY teacher_id;
+```
+
+---
+
+## 🐍 Python (Pandas) Solution
+
+### ✅ **Approach:**
+1. **Group by `teacher_id`:**  
+   - Group the DataFrame by `teacher_id`.
+2. **Count Unique Subjects:**  
+   - Use the `nunique()` function on the `subject_id` column within each group to count unique subjects.
+3. **Reset Index and Rename:**  
+   - Reset the index and rename the column appropriately.
+
+```python
+import pandas as pd
+
+def count_unique_subjects(teacher: pd.DataFrame) -> pd.DataFrame:
+    # Group by teacher_id and count unique subject_id values
+    result = teacher.groupby('teacher_id')['subject_id'].nunique().reset_index()
+    result = result.rename(columns={'subject_id': 'cnt'})
+    return result
+
+# Example usage:
+# teacher_df = pd.DataFrame({
+#     'teacher_id': [1, 1, 1, 2, 2, 2, 2],
+#     'subject_id': [2, 2, 3, 1, 2, 3, 4],
+#     'dept_id': [3, 4, 3, 1, 1, 1, 1]
+# })
+# print(count_unique_subjects(teacher_df))
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Unique-Subjects-Per-Teacher
+│── README.md
+│── solution.sql
+│── solution_pandas.py
+│── test_cases.sql
+│── sample_data.csv
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/number-of-unique-subjects-taught-by-each-teacher/)
+- 🔍 [MySQL COUNT(DISTINCT) Documentation](https://www.w3schools.com/sql/sql_count_distinct.asp)
+- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)