Restaurant Growth
<p>Table: <code>Customer</code></p>

<pre>
+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| customer_id   | int     |
| name          | varchar |
| visited_on    | date    |
| amount        | int     |
+---------------+---------+
In SQL,(customer_id, visited_on) is the primary key for this table.
This table contains data about customer transactions in a restaurant.
visited_on is the date on which the customer with ID (customer_id) has visited the restaurant.
amount is the total paid by a customer.
</pre>

<p>&nbsp;</p>

<p>You are the restaurant owner and you want to analyze a possible expansion (there will be at least one customer every day).</p>

<p>Compute the moving average of how much the customer paid in a seven days window (i.e., current day + 6 days before). <code>average_amount</code> should be <strong>rounded to two decimal places</strong>.</p>

<p>Return the result table ordered by <code>visited_on</code> <strong>in ascending order</strong>.</p>

<p>The result format is in the following example.</p>

<p>&nbsp;</p>
<p><strong class="example">Example 1:</strong></p>

<pre>
<strong>Input:</strong> 
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         | 
+-------------+--------------+--------------+-------------+
<strong>Output:</strong> 
+--------------+--------------+----------------+
| 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         |
+--------------+--------------+----------------+
<strong>Explanation:</strong> 
1st moving average from 2019-01-01 to 2019-01-07 has an average_amount of (100 + 110 + 120 + 130 + 110 + 140 + 150)/7 = 122.86
2nd moving average from 2019-01-02 to 2019-01-08 has an average_amount of (110 + 120 + 130 + 110 + 140 + 150 + 80)/7 = 120
3rd moving average from 2019-01-03 to 2019-01-09 has an average_amount of (120 + 130 + 110 + 140 + 150 + 80 + 110)/7 = 120
4th moving average from 2019-01-04 to 2019-01-10 has an average_amount of (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150)/7 = 142.86
</pre>
Database
# Write your MySQL query statement below
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;
