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  • LeetCode SQL 50 Solution/1068. Product Sales Analysis I

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# **1068. Product Sales Analysis I**
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## **Problem Statement**
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You are given two tables:
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- `Sales` (contains sales data including `product_id`, `year`, `quantity`, and `price`).
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- `Product` (contains `product_id` and `product_name`).
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Each `product_id` in `Sales` is a **foreign key** referring to the `Product` table.
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### **Sales Table**
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```
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+---------+------------+------+----------+-------+
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| sale_id | product_id | year | quantity | price |
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+---------+------------+------+----------+-------+
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| int | int | int | int | int |
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+---------+------------+------+----------+-------+
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```
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- `(sale_id, year)` is the **primary key** (unique values).
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- `product_id` refers to the `Product` table.
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- `price` represents the **per unit price** of the product in that year.
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### **Product Table**
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```
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+------------+--------------+
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| product_id | product_name |
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+------------+--------------+
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| int | varchar |
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+------------+--------------+
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```
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- `product_id` is the **primary key** of this table.
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### **Task:**
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Find the `product_name`, `year`, and `price` for each sale in the `Sales` table.
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---
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## **Example 1:**
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### **Input:**
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**Sales Table**
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```
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+---------+------------+------+----------+-------+
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| sale_id | product_id | year | quantity | price |
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+---------+------------+------+----------+-------+
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| 1 | 100 | 2008 | 10 | 5000 |
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| 2 | 100 | 2009 | 12 | 5000 |
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| 7 | 200 | 2011 | 15 | 9000 |
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+---------+------------+------+----------+-------+
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```
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**Product Table**
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```
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+------------+--------------+
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| product_id | product_name |
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+------------+--------------+
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| 100 | Nokia |
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| 200 | Apple |
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| 300 | Samsung |
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+------------+--------------+
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```
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### **Output:**
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```
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+--------------+-------+-------+
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| product_name | year | price |
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+--------------+-------+-------+
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| Nokia | 2008 | 5000 |
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| Nokia | 2009 | 5000 |
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| Apple | 2011 | 9000 |
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+--------------+-------+-------+
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```
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### **Explanation:**
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- **Sale ID 1:** `Nokia` was sold in **2008** for **5000**.
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- **Sale ID 2:** `Nokia` was sold in **2009** for **5000**.
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- **Sale ID 7:** `Apple` was sold in **2011** for **9000**.
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---
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## **SQL Solutions**
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### **1️⃣ Standard MySQL Solution**
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```sql
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SELECT p.product_name, s.year, s.price
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FROM Sales s
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JOIN Product p ON s.product_id = p.product_id;
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```
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#### **Explanation:**
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1. **JOIN** the `Sales` table with the `Product` table using `product_id`.
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2. **Select `product_name`, `year`, and `price`** from the joined result.
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---
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### **2️⃣ Window Function (SQL) Solution**
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```sql
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WITH SalesData AS (
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SELECT s.product_id, s.year, s.price, p.product_name
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FROM Sales s
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JOIN Product p ON s.product_id = p.product_id
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)
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SELECT product_name, year, price
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FROM SalesData;
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```
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#### **Explanation:**
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1. **CTE `SalesData`** → Stores the joined data from `Sales` and `Product`.
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2. **Final SELECT** → Retrieves `product_name`, `year`, and `price`.
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---
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## **Pandas Solution (Python)**
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```python
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import pandas as pd
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# Sample Data
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sales_data = {'sale_id': [1, 2, 7],
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'product_id': [100, 100, 200],
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'year': [2008, 2009, 2011],
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'quantity': [10, 12, 15],
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'price': [5000, 5000, 9000]}
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product_data = {'product_id': [100, 200, 300],
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'product_name': ['Nokia', 'Apple', 'Samsung']}
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# Create DataFrames
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sales_df = pd.DataFrame(sales_data)
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product_df = pd.DataFrame(product_data)
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# Perform Join
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result = sales_df.merge(product_df, on='product_id')[['product_name', 'year', 'price']]
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print(result)
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```
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### **Explanation:**
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1. **Create DataFrames** → Convert `Sales` and `Product` tables into Pandas DataFrames.
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2. **Perform `merge()` on `product_id`** → Equivalent to SQL `JOIN`.
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3. **Select required columns (`product_name`, `year`, `price`)**.
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---
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## **File Structure**
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```
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LeetCode1068/
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├── problem_statement.md # Contains the problem description and constraints.
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├── sql_solution.sql # Contains the SQL solutions (Standard + Window Functions).
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├── pandas_solution.py # Contains the Pandas solution.
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├── README.md # Overview of the problem and available solutions.
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```
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---
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## **Useful Links**
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- [LeetCode Problem 1068](https://leetcode.com/problems/product-sales-analysis-i/)
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- [SQL JOIN Documentation](https://www.w3schools.com/sql/sql_join.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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This README now includes:
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✅ **MySQL Query**
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✅ **Window SQL Query**
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✅ **Pandas Python Solution**
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✅ **File Structure & Useful Links**
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🚀 **Now it's a complete guide!** 🚀

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