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LeetCode SQL 50 Solution/1251. Average Selling Price/readme.md

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+Here's a well-structured `README.md` file for **LeetCode 1251 - Average Selling Price**, formatted for a GitHub repository:
+
+```md
+# 🛒 Average Selling Price - LeetCode 1251
+
+## 📌 Problem Statement
+You are given two tables, **Prices** and **UnitsSold**, which contain information about product pricing and sales.
+
+### 📊 Prices Table
+| Column Name | Type |
+| ----------- | ---- |
+| product_id  | int  |
+| start_date  | date |
+| end_date    | date |
+| price       | int  |
+
+- `(product_id, start_date, end_date)` is the **primary key**.
+- Each row defines the price for `product_id` **within a specific date range**.
+- **No two price periods overlap** for the same product.
+
+### 📊 UnitsSold Table
+| Column Name   | Type |
+| ------------- | ---- |
+| product_id    | int  |
+| purchase_date | date |
+| units         | int  |
+
+- Each row records the number of units sold for `product_id` on `purchase_date`.
+- **Table may contain duplicate rows**.
+
+### 🔢 Goal:
+Find the **average selling price** for each `product_id`, rounded to **2 decimal places**.  
+If a product has **no sales**, its average price should be **0**.
+
+---
+
+## 📊 Example 1:
+### Input:
+### **Prices Table**
+| product_id | start_date | end_date   | price |
+| ---------- | ---------- | ---------- | ----- |
+| 1          | 2019-02-17 | 2019-02-28 | 5     |
+| 1          | 2019-03-01 | 2019-03-22 | 20    |
+| 2          | 2019-02-01 | 2019-02-20 | 15    |
+| 2          | 2019-02-21 | 2019-03-31 | 30    |
+
+### **UnitsSold Table**
+| product_id | purchase_date | units |
+| ---------- | ------------- | ----- |
+| 1          | 2019-02-25    | 100   |
+| 1          | 2019-03-01    | 15    |
+| 2          | 2019-02-10    | 200   |
+| 2          | 2019-03-22    | 30    |
+
+### Output:
+| product_id | average_price |
+| ---------- | ------------- |
+| 1          | 6.96          |
+| 2          | 16.96         |
+
+---
+
+## 🔍 Explanation:
+### **Formula**
+\[
+\text{Average Selling Price} = \frac{\sum (\text{price} \times \text{units sold})}{\sum (\text{units sold})}
+\]
+
+### **Product 1 Calculation**
+- **Feb 25, 2019:** 100 units sold at **$5**
+- **Mar 01, 2019:** 15 units sold at **$20**
+- **Total Price Contribution:**  
+  \[
+  (100 \times 5) + (15 \times 20) = 500 + 300 = 800
+  \]
+- **Total Units Sold:**  
+  \[
+  100 + 15 = 115
+  \]
+- **Average Price:**  
+  \[
+  800 / 115 = 6.96
+  \]
+
+### **Product 2 Calculation**
+- **Feb 10, 2019:** 200 units sold at **$15**
+- **Mar 22, 2019:** 30 units sold at **$30**
+- **Total Price Contribution:**  
+  \[
+  (200 \times 15) + (30 \times 30) = 3000 + 900 = 3900
+  \]
+- **Total Units Sold:**  
+  \[
+  200 + 30 = 230
+  \]
+- **Average Price:**  
+  \[
+  3900 / 230 = 16.96
+  \]
+
+---
+
+## 🖥 SQL Solution
+
+### 1️⃣ Standard MySQL Query
+#### **Explanation:**
+- **Join `Prices` and `UnitsSold`** on `product_id`, ensuring `purchase_date` falls **within the valid price period** (`start_date` ≤ `purchase_date` ≤ `end_date`).
+- **Multiply `price * units`** for total revenue.
+- **Sum total units** for each product.
+- **Use `ROUND(..., 2)`** to get 2 decimal places.
+- **Use `IFNULL(..., 0)`** to handle cases where no units were sold.
+
+```sql
+SELECT p.product_id, 
+       IFNULL(ROUND(SUM(p.price * u.units) / SUM(u.units), 2), 0) AS average_price
+FROM Prices p
+LEFT JOIN UnitsSold u
+ON p.product_id = u.product_id 
+AND u.purchase_date BETWEEN p.start_date AND p.end_date
+GROUP BY p.product_id;
+```
+
+---
+
+### 📝 Step-by-Step Breakdown:
+
+1️⃣ **Join Tables Based on Matching Date Ranges**
+```sql
+LEFT JOIN UnitsSold u
+ON p.product_id = u.product_id 
+AND u.purchase_date BETWEEN p.start_date AND p.end_date
+```
+- Ensures we only match **valid** sales based on pricing periods.
+
+2️⃣ **Calculate Revenue Per Product**
+```sql
+SUM(p.price * u.units)
+```
+- Computes total revenue for each product.
+
+3️⃣ **Compute Total Sold Units Per Product**
+```sql
+SUM(u.units)
+```
+- Sums up all sold units.
+
+4️⃣ **Calculate Average Price and Handle Edge Cases**
+```sql
+ROUND(SUM(p.price * u.units) / SUM(u.units), 2)
+```
+- Ensures precision with 2 decimal places.
+
+5️⃣ **Handle Products with No Sales**
+```sql
+IFNULL(..., 0)
+```
+- If `SUM(u.units)` is `NULL`, return `0`.
+
+---
+
+### 2️⃣ Alternative MySQL Query (Using `COALESCE`)
+```sql
+SELECT p.product_id, 
+       ROUND(SUM(COALESCE(p.price, 0) * COALESCE(u.units, 0)) / SUM(COALESCE(u.units, 0)), 2) AS average_price
+FROM Prices p
+LEFT JOIN UnitsSold u
+ON p.product_id = u.product_id 
+AND u.purchase_date BETWEEN p.start_date AND p.end_date
+GROUP BY p.product_id;
+```
+- Uses **`COALESCE(value, 0)`** instead of `IFNULL()` for robustness.
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### **Explanation:**
+- **Merge DataFrames on `product_id`** where `purchase_date` falls in the price range.
+- **Compute total price & units**.
+- **Handle cases where no units were sold**.
+
+```python
+import pandas as pd
+
+def average_selling_price(prices: pd.DataFrame, units_sold: pd.DataFrame) -> pd.DataFrame:
+    # Merge on product_id where purchase_date is within the valid price period
+    merged = prices.merge(units_sold, on="product_id")
+    merged = merged[(merged["purchase_date"] >= merged["start_date"]) & 
+                    (merged["purchase_date"] <= merged["end_date"])]
+
+    # Calculate total revenue and total units per product
+    merged["total_price"] = merged["price"] * merged["units"]
+    result = merged.groupby("product_id").agg(
+        average_price=("total_price", lambda x: round(x.sum() / merged.loc[x.index, "units"].sum(), 2))
+    ).reset_index()
+
+    # Handle products with no sales
+    result["average_price"] = result["average_price"].fillna(0)
+
+    return result
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Average-Selling-Price
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/average-selling-price/)
+- 📚 [SQL `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)
+```
+
+### Features of this `README.md`:
+✅ **Clear problem statement with table structure**  
+✅ **Examples with detailed calculations**  
+✅ **SQL and Pandas solutions with explanations**  
+✅ **Alternative SQL query for flexibility**  
+✅ **File structure for GitHub organization**  
+✅ **Useful reference links**  
+
+Would you like any refinements? 🚀