Immediate Food Delivery III
<p>Table: <code>Delivery</code></p>

<pre>
+-----------------------------+---------+
| Column Name                 | Type    |
+-----------------------------+---------+
| delivery_id                 | int     |
| customer_id                 | int     |
| order_date                  | date    |
| customer_pref_delivery_date | date    |
+-----------------------------+---------+
delivery_id is the column with unique values of this table.
Each row contains information about food delivery to a customer that makes an order at some date and specifies a preferred delivery date (on the order date or after it).
</pre>

<p>If the customer&#39;s preferred delivery date is the same as the order date, then the order is called <strong>immediate,</strong>&nbsp;otherwise, it is <strong>scheduled</strong>.</p>

<p>Write a solution&nbsp;to find the percentage of immediate orders on each unique&nbsp;<code>order_date</code>, <strong>rounded to 2 decimal places</strong>.&nbsp;</p>

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

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

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

<pre>
<strong>Input:</strong> 
Delivery table:
+-------------+-------------+------------+-----------------------------+
| delivery_id | customer_id | order_date | customer_pref_delivery_date |
+-------------+-------------+------------+-----------------------------+
| 1           | 1           | 2019-08-01 | 2019-08-02                  |
| 2           | 2           | 2019-08-01 | 2019-08-01                  |
| 3           | 1           | 2019-08-01 | 2019-08-01                  |
| 4           | 3           | 2019-08-02 | 2019-08-13                  |
| 5           | 3           | 2019-08-02 | 2019-08-02                  |
| 6           | 2           | 2019-08-02 | 2019-08-02                  |
| 7           | 4           | 2019-08-03 | 2019-08-03                  |
| 8           | 1           | 2019-08-03 | 2019-08-03                  |
| 9           | 5           | 2019-08-04 | 2019-08-08                  |
| 10          | 2           | 2019-08-04 | 2019-08-18                  |
+-------------+-------------+------------+-----------------------------+
<strong>Output:</strong> 
+------------+----------------------+
| order_date | immediate_percentage |
+------------+----------------------+
| 2019-08-01 | 66.67                |
| 2019-08-02 | 66.67                |
| 2019-08-03 | 100.00               |
| 2019-08-04 | 0.00                 |
+------------+----------------------+
<strong>Explanation:</strong> 
- On 2019-08-01 there were three orders, out of those, two were immediate and one was scheduled. So, immediate percentage for that date was 66.67.
- On 2019-08-02 there were three orders, out of those, two were immediate and one was scheduled. So, immediate percentage for that date was 66.67.
- On 2019-08-03 there were two orders, both were immediate. So, the immediate percentage for that date was 100.00.
- On 2019-08-04 there were two orders, both were scheduled. So, the immediate percentage for that date was 0.00.
order_date is sorted in ascending order.
</pre>
Database
# Write your MySQL query statement below
SELECT
    order_date,
    ROUND(
        100 * SUM(IF(customer_pref_delivery_date = order_date, 1, 0)) / COUNT(*),
        2
    ) AS immediate_percentage
FROM Delivery
GROUP BY order_date
ORDER BY order_date;
