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Here's a well-structured `README.md` file for **LeetCode 1251 - Average Selling Price**, formatted for a GitHub repository:
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```md
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# 🛒 Average Selling Price - LeetCode 1251
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## 📌 Problem Statement
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You are given two tables, **Prices** and **UnitsSold**, which contain information about product pricing and sales.
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### 📊 Prices Table
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| Column Name | Type |
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| ----------- | ---- |
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| product_id | int |
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| start_date | date |
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| end_date | date |
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| price | int |
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- `(product_id, start_date, end_date)` is the **primary key**.
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- Each row defines the price for `product_id` **within a specific date range**.
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- **No two price periods overlap** for the same product.
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### 📊 UnitsSold Table
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| Column Name | Type |
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| ------------- | ---- |
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| product_id | int |
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| purchase_date | date |
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| units | int |
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- Each row records the number of units sold for `product_id` on `purchase_date`.
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- **Table may contain duplicate rows**.
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### 🔢 Goal:
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Find the **average selling price** for each `product_id`, rounded to **2 decimal places**.
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If a product has **no sales**, its average price should be **0**.
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---
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## 📊 Example 1:
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### Input:
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### **Prices Table**
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| product_id | start_date | end_date | price |
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| ---------- | ---------- | ---------- | ----- |
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| 1 | 2019年02月17日 | 2019年02月28日 | 5 |
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| 1 | 2019年03月01日 | 2019年03月22日 | 20 |
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| 2 | 2019年02月01日 | 2019年02月20日 | 15 |
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| 2 | 2019年02月21日 | 2019年03月31日 | 30 |
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### **UnitsSold Table**
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| product_id | purchase_date | units |
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| ---------- | ------------- | ----- |
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| 1 | 2019年02月25日 | 100 |
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| 1 | 2019年03月01日 | 15 |
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| 2 | 2019年02月10日 | 200 |
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| 2 | 2019年03月22日 | 30 |
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### Output:
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| product_id | average_price |
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| ---------- | ------------- |
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| 1 | 6.96 |
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| 2 | 16.96 |
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---
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## 🔍 Explanation:
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### **Formula**
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\[
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\text{Average Selling Price} = \frac{\sum (\text{price} \times \text{units sold})}{\sum (\text{units sold})}
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\]
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### **Product 1 Calculation**
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- **Feb 25, 2019:** 100 units sold at **5ドル**
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- **Mar 01, 2019:** 15 units sold at **20ドル**
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- **Total Price Contribution:**
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\[
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(100 \times 5) + (15 \times 20) = 500 + 300 = 800
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\]
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- **Total Units Sold:**
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\[
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100 + 15 = 115
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\]
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- **Average Price:**
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\[
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800 / 115 = 6.96
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\]
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### **Product 2 Calculation**
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- **Feb 10, 2019:** 200 units sold at **15ドル**
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- **Mar 22, 2019:** 30 units sold at **30ドル**
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- **Total Price Contribution:**
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\[
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(200 \times 15) + (30 \times 30) = 3000 +たす 900 =わ 3900
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\]
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- **Total Units Sold:**
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\[
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200 + 30 = 230
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\]
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- **Average Price:**
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\[
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3900 / 230 = 16.96
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\]
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---
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## 🖥 SQL Solution
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### 1️⃣ Standard MySQL Query
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#### **Explanation:**
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- **Join `Prices` and `UnitsSold`** on `product_id`, ensuring `purchase_date` falls **within the valid price period** (`start_date` ≤ `purchase_date` ≤ `end_date`).
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- **Multiply `price * units`** for total revenue.
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- **Sum total units** for each product.
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- **Use `ROUND(..., 2)`** to get 2 decimal places.
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- **Use `IFNULL(..., 0)`** to handle cases where no units were sold.
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```sql
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SELECT p.product_id,
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IFNULL(ROUND(SUM(p.price * u.units) / SUM(u.units), 2), 0) AS average_price
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FROM Prices p
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LEFT JOIN UnitsSold u
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ON p.product_id = u.product_id
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AND u.purchase_date BETWEEN p.start_date AND p.end_date
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GROUP BY p.product_id;
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```
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---
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### 📝 Step-by-Step Breakdown:
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1️⃣ **Join Tables Based on Matching Date Ranges**
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```sql
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LEFT JOIN UnitsSold u
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ON p.product_id = u.product_id
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AND u.purchase_date BETWEEN p.start_date AND p.end_date
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```
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- Ensures we only match **valid** sales based on pricing periods.
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2️⃣ **Calculate Revenue Per Product**
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```sql
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SUM(p.price * u.units)
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```
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- Computes total revenue for each product.
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3️⃣ **Compute Total Sold Units Per Product**
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```sql
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SUM(u.units)
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```
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- Sums up all sold units.
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4️⃣ **Calculate Average Price and Handle Edge Cases**
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```sql
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ROUND(SUM(p.price * u.units) / SUM(u.units), 2)
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```
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- Ensures precision with 2 decimal places.
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5️⃣ **Handle Products with No Sales**
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```sql
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IFNULL(..., 0)
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```
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- If `SUM(u.units)` is `NULL`, return `0`.
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---
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### 2️⃣ Alternative MySQL Query (Using `COALESCE`)
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```sql
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SELECT p.product_id,
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ROUND(SUM(COALESCE(p.price, 0) * COALESCE(u.units, 0)) / SUM(COALESCE(u.units, 0)), 2) AS average_price
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FROM Prices p
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LEFT JOIN UnitsSold u
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ON p.product_id = u.product_id
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AND u.purchase_date BETWEEN p.start_date AND p.end_date
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GROUP BY p.product_id;
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```
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- Uses **`COALESCE(value, 0)`** instead of `IFNULL()` for robustness.
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---
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## 🐍 Pandas Solution (Python)
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#### **Explanation:**
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- **Merge DataFrames on `product_id`** where `purchase_date` falls in the price range.
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- **Compute total price & units**.
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- **Handle cases where no units were sold**.
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```python
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import pandas as pd
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def average_selling_price(prices: pd.DataFrame, units_sold: pd.DataFrame) -> pd.DataFrame:
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# Merge on product_id where purchase_date is within the valid price period
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merged = prices.merge(units_sold, on="product_id")
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merged = merged[(merged["purchase_date"] >= merged["start_date"]) &
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(merged["purchase_date"] <= merged["end_date"])]
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# Calculate total revenue and total units per product
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merged["total_price"] = merged["price"] * merged["units"]
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result = merged.groupby("product_id").agg(
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average_price=("total_price", lambda x: round(x.sum() / merged.loc[x.index, "units"].sum(), 2))
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).reset_index()
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# Handle products with no sales
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result["average_price"] = result["average_price"].fillna(0)
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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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📂 Average-Selling-Price
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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/average-selling-price/)
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- 📚 [SQL `LEFT JOIN` Documentation](https://www.w3schools.com/sql/sql_join_left.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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### Features of this `README.md`:
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✅ **Clear problem statement with table structure**
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✅ **Examples with detailed calculations**
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✅ **SQL and Pandas solutions with explanations**
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✅ **Alternative SQL query for flexibility**
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✅ **File structure for GitHub organization**
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✅ **Useful reference links**
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Would you like any refinements? 🚀

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