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Merge pull request #92 from iamAntimPal/Branch-1
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  • LeetCode SQL 50 Solution
    • 1731. The Number of Employees Which Report to Each Employee
    • 1757. Recyclable and Low Fat Products

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# 👥 The Number of Employees Which Report to Each Employee - LeetCode 1731
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## 📌 Problem Statement
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You are given a table **Employees** that contains the following columns:
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- `employee_id`: The unique ID of the employee.
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- `name`: The name of the employee.
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- `reports_to`: The `employee_id` of the manager the employee reports to (can be `NULL` if the employee does not report to anyone).
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- `age`: The age of the employee.
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A manager is defined as an employee who has **at least 1 direct report**.
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Your task is to report:
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- The **IDs and names of all managers**.
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- The **number of employees** who report **directly** to them.
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- The **average age** of their direct reports, rounded to the nearest integer.
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Return the result **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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| reports_to | int |
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| age | int |
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- `employee_id` is the **primary key**.
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- `reports_to` may be `NULL` for employees who do not report to anyone.
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---
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## 📊 Example 1:
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### **Input:**
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| employee_id | name | reports_to | age |
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| ----------- | ------- | ---------- | --- |
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| 9 | Hercy | NULL | 43 |
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| 6 | Alice | 9 | 41 |
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| 4 | Bob | 9 | 36 |
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| 2 | Winston | NULL | 37 |
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### **Output:**
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| employee_id | name | reports_count | average_age |
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| ----------- | ----- | ------------- | ----------- |
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| 9 | Hercy | 2 | 39 |
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### **Explanation:**
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- **Hercy** (employee_id = 9) is a manager with two direct reports: **Alice** (age 41) and **Bob** (age 36).
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- The average age of these reports is (41 + 36) / 2 = 38.5, which is rounded to **39**.
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---
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## 🖥 SQL Solution
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### ✅ **Approach:**
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1. Use a **self-join** on the **Employees** table where the employee’s `reports_to` matches the manager’s `employee_id`.
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2. Count the number of direct reports for each manager.
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3. Compute the average age of the direct reports and round the result to the nearest integer.
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4. Group by the manager’s `employee_id` and order the results by `employee_id`.
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```sql
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SELECT
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Manager.employee_id,
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Manager.name,
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COUNT(Employee.employee_id) AS reports_count,
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ROUND(AVG(Employee.age)) AS average_age
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FROM Employees AS Manager
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INNER JOIN Employees AS Employee
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ON Employee.reports_to = Manager.employee_id
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GROUP BY Manager.employee_id
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ORDER BY Manager.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. Filter the DataFrame to create a join between managers and their direct reports.
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2. Group by the manager’s `employee_id` and compute:
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- The count of direct reports.
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- The average age of the reports, then round the average.
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3. Merge the results with the original manager information.
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4. Sort the result by `employee_id`.
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```python
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import pandas as pd
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def employees_reporting(employees: pd.DataFrame) -> pd.DataFrame:
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# Merge the table with itself: one for managers and one for employees reporting to them.
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merged = employees.merge(
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employees,
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left_on='employee_id',
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right_on='reports_to',
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suffixes=('_manager', '_report')
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)
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# Group by manager's employee_id and name, then compute the count and average age of reports.
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result = merged.groupby(['employee_id_manager', 'name_manager']).agg(
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reports_count=('employee_id_report', 'count'),
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average_age=('age_report', lambda x: round(x.mean()))
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).reset_index()
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# Rename columns to match expected output.
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result.rename(columns={
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'employee_id_manager': 'employee_id',
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'name_manager': 'name'
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}, inplace=True)
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# Sort by employee_id in ascending order.
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result = result.sort_values('employee_id').reset_index(drop=True)
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return result
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```
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---
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## 📁 File Structure
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```
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📂 Employees-Reporting
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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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```
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---
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## 🔗 Useful Links
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- 📖 [LeetCode Problem](https://leetcode.com/problems/the-number-of-employees-which-report-to-each-employee/)
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- 🔍 [MySQL GROUP BY Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
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- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
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# ♻️ Recyclable and Low Fat Products - LeetCode 1757
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## 📌 Problem Statement
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You are given a table **Products** that contains information about products with respect to their fat content and recyclability.
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- The **low_fats** column is an ENUM with values `'Y'` and `'N'`, where `'Y'` indicates the product is low fat.
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- The **recyclable** column is an ENUM with values `'Y'` and `'N'`, where `'Y'` indicates the product is recyclable.
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Your task is to **find the IDs of products that are both low fat and recyclable**.
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Return the result in **any order**.
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---
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## 📊 Table Structure
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### **Products Table**
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| Column Name | Type |
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| ----------- | ---- |
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| product_id | int |
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| low_fats | enum |
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| recyclable | enum |
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- `product_id` is the **primary key**.
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---
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## 📊 Example 1:
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### **Input:**
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#### **Products Table**
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| product_id | low_fats | recyclable |
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| ---------- | -------- | ---------- |
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| 0 | Y | N |
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| 1 | Y | Y |
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| 2 | N | Y |
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| 3 | Y | Y |
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| 4 | N | N |
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### **Output:**
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| product_id |
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| ---------- |
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| 1 |
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| 3 |
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### **Explanation:**
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- Only products with `product_id` **1** and **3** are **both low fat and recyclable**.
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---
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## 🖥 SQL Solution
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### ✅ **Approach:**
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- Filter the **Products** table for rows where `low_fats = 'Y'` and `recyclable = 'Y'`.
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- Return the corresponding `product_id`.
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```sql
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SELECT product_id
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FROM Products
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WHERE low_fats = 'Y' AND recyclable = 'Y';
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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. Load the **Products** table into a Pandas DataFrame.
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2. Filter the DataFrame to keep rows where both `low_fats` and `recyclable` are `'Y'`.
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3. Select and return the `product_id` column.
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```python
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import pandas as pd
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def recyclable_low_fat_products(products: pd.DataFrame) -> pd.DataFrame:
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# Filter rows where both low_fats and recyclable are 'Y'
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filtered = products[(products['low_fats'] == 'Y') & (products['recyclable'] == 'Y')]
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# Select only the product_id column
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result = filtered[['product_id']]
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return result
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```
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---
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## 📁 File Structure
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```
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📂 Recyclable-Low-Fat-Products
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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/recyclable-and-low-fat-products/)
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- 🔍 [MySQL WHERE Clause](https://www.w3schools.com/sql/sql_where.asp)
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- 🐍 [Pandas DataFrame Filtering](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html)

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