Update readme.md

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LeetCode SQL 50 Solution/1321. Restaurant Growth/readme.md

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+Here's a well-structured `README.md` for **LeetCode 321 - Restaurant Growth**, formatted for a GitHub repository:  
+
+```md
+# 🍽️ Restaurant Growth - LeetCode 321
+
+## 📌 Problem Statement
+You are given a table **Customer**, which records daily customer transactions in a restaurant.  
+The restaurant owner wants to analyze a **7-day moving average** of customer spending.  
+
+### 📊 Customer Table
+| Column Name | Type    |
+| ----------- | ------- |
+| customer_id | int     |
+| name        | varchar |
+| visited_on  | date    |
+| amount      | int     |
+- **(customer_id, visited_on) is the primary key**.
+- `visited_on` represents the date a customer visited the restaurant.
+- `amount` represents the total amount paid by a customer on that day.
+
+---
+
+## 🔢 Goal:
+Compute the **7-day moving average** of customer spending.  
+- The window consists of **current day + 6 days before**.
+- `average_amount` should be **rounded to 2 decimal places**.
+- The result should be **ordered by `visited_on` in ascending order**.
+
+---
+
+## 📊 Example 1:
+### **Input:**
+#### **Customer Table**
+| customer_id | name    | visited_on | amount |
+| ----------- | ------- | ---------- | ------ |
+| 1           | Jhon    | 2019-01-01 | 100    |
+| 2           | Daniel  | 2019-01-02 | 110    |
+| 3           | Jade    | 2019-01-03 | 120    |
+| 4           | Khaled  | 2019-01-04 | 130    |
+| 5           | Winston | 2019-01-05 | 110    |
+| 6           | Elvis   | 2019-01-06 | 140    |
+| 7           | Anna    | 2019-01-07 | 150    |
+| 8           | Maria   | 2019-01-08 | 80     |
+| 9           | Jaze    | 2019-01-09 | 110    |
+| 1           | Jhon    | 2019-01-10 | 130    |
+| 3           | Jade    | 2019-01-10 | 150    |
+
+### **Output:**
+| visited_on | amount | average_amount |
+| ---------- | ------ | -------------- |
+| 2019-01-07 | 860    | 122.86         |
+| 2019-01-08 | 840    | 120            |
+| 2019-01-09 | 840    | 120            |
+| 2019-01-10 | 1000   | 142.86         |
+
+### **Explanation:**
+1. **First moving average (2019-01-01 to 2019-01-07)**  
+   \[
+   (100 + 110 + 120 + 130 + 110 + 140 + 150) / 7 = 122.86
+   \]
+2. **Second moving average (2019-01-02 to 2019-01-08)**  
+   \[
+   (110 + 120 + 130 + 110 + 140 + 150 + 80) / 7 = 120
+   \]
+3. **Third moving average (2019-01-03 to 2019-01-09)**  
+   \[
+   (120 + 130 + 110 + 140 + 150 + 80 + 110) / 7 = 120
+   \]
+4. **Fourth moving average (2019-01-04 to 2019-01-10)**  
+   \[
+   (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150) / 7 = 142.86
+   \]
+
+---
+
+## 🖥 SQL Solutions
+
+### 1️⃣ **Using `WINDOW FUNCTION` (`SUM() OVER` + `RANK() OVER`)**
+#### **Explanation:**
+- First, **group transactions per day** using `SUM(amount)`.
+- Then, use `SUM() OVER (ROWS 6 PRECEDING)` to calculate **moving sum** over 7 days.
+- Use `RANK()` to track row numbers and filter rows with `rk > 6`.
+- Finally, compute `ROUND(amount / 7, 2)`.
+
+```sql
+WITH t AS (
+    SELECT 
+        visited_on, 
+        SUM(amount) OVER (
+            ORDER BY visited_on 
+            ROWS 6 PRECEDING
+        ) AS amount,
+        RANK() OVER (
+            ORDER BY visited_on 
+            ROWS 6 PRECEDING
+        ) AS rk
+    FROM (
+        SELECT visited_on, SUM(amount) AS amount
+        FROM Customer
+        GROUP BY visited_on
+    ) AS tt
+)
+SELECT visited_on, amount, ROUND(amount / 7, 2) AS average_amount
+FROM t
+WHERE rk > 6;
+```
+
+---
+
+### 2️⃣ **Using `JOIN` + `DATEDIFF()`**
+#### **Explanation:**
+- Use a **self-join** to find transactions **within a 7-day range**.
+- Sum the `amount` for each window and calculate the moving average.
+- Use `DATEDIFF(a.visited_on, b.visited_on) BETWEEN 0 AND 6` to filter records.
+- Ensure only complete 7-day windows are included.
+
+```sql
+SELECT 
+    a.visited_on,
+    SUM(b.amount) AS amount,
+    ROUND(SUM(b.amount) / 7, 2) AS average_amount
+FROM 
+    (SELECT DISTINCT visited_on FROM customer) AS a
+    JOIN customer AS b 
+    ON DATEDIFF(a.visited_on, b.visited_on) BETWEEN 0 AND 6
+WHERE 
+    a.visited_on >= (SELECT MIN(visited_on) FROM customer) + 6
+GROUP BY a.visited_on
+ORDER BY a.visited_on;
+```
+
+---
+
+## 🐍 Pandas Solution (Python)
+#### **Explanation:**
+- **Group by `visited_on`** and sum `amount` per day.
+- **Use `.rolling(7).sum()`** to compute the moving sum over 7 days.
+- **Drop NaN values** to exclude incomplete windows.
+- **Round the average to 2 decimal places**.
+
+```python
+import pandas as pd
+
+def restaurant_growth(customers: pd.DataFrame) -> pd.DataFrame:
+    # Aggregate daily amounts
+    daily_amount = customers.groupby("visited_on")["amount"].sum().reset_index()
+
+    # Compute rolling 7-day sum and moving average
+    daily_amount["amount"] = daily_amount["amount"].rolling(7).sum()
+    daily_amount["average_amount"] = (daily_amount["amount"] / 7).round(2)
+
+    # Drop incomplete windows
+    daily_amount = daily_amount.dropna().reset_index(drop=True)
+
+    return daily_amount
+```
+
+---
+
+## 📁 File Structure
+```
+📂 Restaurant-Growth
+│── 📜 README.md
+│── 📜 solution.sql
+│── 📜 solution_pandas.py
+│── 📜 test_cases.sql
+```
+
+---
+
+## 🔗 Useful Links
+- 📖 [LeetCode Problem](https://leetcode.com/problems/restaurant-growth/)
+- 📚 [SQL `WINDOW FUNCTIONS` Documentation](https://www.w3schools.com/sql/sql_window.asp)
+- 🐍 [Pandas Rolling Window](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.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? 🚀