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LeetCode SQL 50 Solution/1070. Product Sales Analysis III/readme.md

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+Here's the **README.md** file including **MySQL, Window SQL, and Pandas solutions** for **LeetCode 1070 - Product Sales Analysis III**.
+
+---
+
+# **1070. Product Sales Analysis III**
+
+## **Problem Statement**
+You are given two tables:
+
+- `Sales` (contains sales data including `product_id`, `year`, `quantity`, and `price`). 
+- `Product` (contains `product_id` and `product_name`).
+
+Each `product_id` in `Sales` is a **foreign key** referring to the `Product` table.
+
+### **Sales Table**
+```
++---------+------------+------+----------+-------+
+| sale_id | product_id | year | quantity | price |
++---------+------------+------+----------+-------+
+| int | int | int | int | int |
++---------+------------+------+----------+-------+
+```
+- `(sale_id, year)` is the **primary key** (unique values).
+- `product_id` refers to the `Product` table.
+- `price` represents the **per unit price** of the product in that year.
+
+### **Product Table**
+```
++------------+--------------+
+| product_id | product_name |
++------------+--------------+
+| int | varchar |
++------------+--------------+
+```
+- `product_id` is the **primary key** of this table.
+
+### **Task:**
+Find the `product_id`, `first_year`, `quantity`, and `price` for **the first year a product was sold**.
+
+---
+
+## **Example 1:**
+
+### **Input:**
+**Sales Table**
+```
++---------+------------+------+----------+-------+
+| sale_id | product_id | year | quantity | price |
++---------+------------+------+----------+-------+
+| 1 | 100 | 2008 | 10 | 5000 |
+| 2 | 100 | 2009 | 12 | 5000 |
+| 7 | 200 | 2011 | 15 | 9000 |
++---------+------------+------+----------+-------+
+```
+
+**Product Table**
+```
++------------+--------------+
+| product_id | product_name |
++------------+--------------+
+| 100 | Nokia |
+| 200 | Apple |
+| 300 | Samsung |
++------------+--------------+
+```
+
+### **Output:**
+```
++------------+------------+----------+-------+
+| product_id | first_year | quantity | price |
++------------+------------+----------+-------+
+| 100 | 2008 | 10 | 5000 |
+| 200 | 2011 | 15 | 9000 |
++------------+------------+----------+-------+
+```
+
+### **Explanation:**
+- **Product 100 (Nokia):** First sold in **2008** with **10 units** at **5000** price.
+- **Product 200 (Apple):** First sold in **2011** with **15 units** at **9000** price.
+
+---
+
+## **SQL Solutions**
+
+### **1️⃣ Standard MySQL Solution**
+```sql
+SELECT 
+ product_id, 
+ year AS first_year, 
+ quantity, 
+ price 
+FROM 
+ Sales 
+WHERE 
+ (product_id, year) IN (
+ SELECT 
+ product_id, 
+ MIN(year) AS year 
+ FROM 
+ Sales 
+ GROUP BY 
+ product_id
+ );
+```
+#### **Explanation:**
+1. **Subquery (`MIN(year)`)** → Finds the **first year** (`MIN(year)`) each `product_id` was sold.
+2. **Filter the main table** → Selects rows matching the **earliest year** for each product.
+
+---
+
+### **2️⃣ Window Function (SQL) Solution**
+```sql
+WITH RankedSales AS (
+ SELECT 
+ product_id, 
+ year AS first_year, 
+ quantity, 
+ price,
+ RANK() OVER (PARTITION BY product_id ORDER BY year ASC) AS rnk
+ FROM Sales
+)
+SELECT product_id, first_year, quantity, price
+FROM RankedSales
+WHERE rnk = 1;
+```
+#### **Explanation:**
+1. **`RANK() OVER (PARTITION BY product_id ORDER BY year ASC)`** 
+ - Assigns **rank 1** to the first sale per `product_id`.
+2. **Filter (`WHERE rnk = 1`)** 
+ - Retrieves **only the first sale per product**.
+
+---
+
+## **Pandas Solution (Python)**
+```python
+import pandas as pd
+
+# Sample Data
+sales_data = {'sale_id': [1, 2, 7], 
+ 'product_id': [100, 100, 200], 
+ 'year': [2008, 2009, 2011], 
+ 'quantity': [10, 12, 15], 
+ 'price': [5000, 5000, 9000]}
+
+# Create DataFrame
+sales_df = pd.DataFrame(sales_data)
+
+# Find the first sale per product
+first_sales = sales_df.loc[sales_df.groupby('product_id')['year'].idxmin(), ['product_id', 'year', 'quantity', 'price']]
+
+# Rename columns
+first_sales.rename(columns={'year': 'first_year'}, inplace=True)
+
+print(first_sales)
+```
+
+### **Explanation:**
+1. **Create DataFrame** → Convert `Sales` table into Pandas DataFrame.
+2. **Group by `product_id` and get the `idxmin()` of `year`** → Finds the first sale per product.
+3. **Retrieve `product_id`, `year`, `quantity`, and `price`**.
+4. **Rename `year` to `first_year`**.
+
+---
+
+## **File Structure**
+```
+LeetCode1070/
+├── problem_statement.md # Contains the problem description and constraints.
+├── sql_solution.sql # Contains the SQL solutions (Standard + Window Functions).
+├── pandas_solution.py # Contains the Pandas solution.
+├── README.md # Overview of the problem and available solutions.
+```
+
+---
+
+## **Useful Links**
+- [LeetCode Problem 1070](https://leetcode.com/problems/product-sales-analysis-iii/)
+- [SQL JOIN Documentation](https://www.w3schools.com/sql/sql_join.asp)
+- [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)
+
+---
+
+This README now includes:
+✅ **MySQL Query** 
+✅ **Window SQL Query** 
+✅ **Pandas Python Solution** 
+✅ **File Structure & Useful Links** 
+
+🚀 **Now it's a complete guide!** 🚀