Formatted question description: https://leetcode.ca/all/1321.html

1321. Restaurant Growth

Level

Medium

Description

Table: Customer

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| customer_id   | int     |
| name          | varchar |
| visited_on    | date    |
| amount        | int     |
+---------------+---------+
(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) have visited the restaurant.
amount is the total paid by a customer.

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

Write an SQL query to compute moving average of how much customer paid in a 7 days window (current day + 6 days before) .

Return result table ordered by visited_on.

average_amount should be rounded to 2 decimal places, all dates are in the format (‘YYYY-MM-DD’).

The query result format is in the following example:

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         | 
+-------------+--------------+--------------+-------------+

Result table:
+--------------+--------------+----------------+
| 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         |
+--------------+--------------+----------------+

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

Solution

First, only select the values of visited_on at least greater than the minimum value of visited_on by at least 6. For example, if the minimum value of visited_on is ‘2019-01-01’, then only select the values of visited_on that are at least ‘2019-01-07’.

Then, for each date in selected visited_on, obtain all the entries in the 7-day window, and calculate the sum and the average. Use round to round the average to 2 decimal placed.

# Write your MySQL query statement below
select visits.visited_on as visited_on, sum(c.amount) as amount, round(sum(c.amount) / 7.0, 2) as average_amount
    from (
        select distinct visited_on from Customer
            where datediff(visited_on, (select min(visited_on) from Customer)) >= 6
    ) visits left join Customer c
    on datediff(visits.visited_on, c.visited_on) between 0 and 6
    group by visits.visited_on
    order by visited_on;

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