average-selling-price

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Database

Table: <code>Prices</code>

<pre>
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| start_date | date |
| end_date | date |
| price | int |
+---------------+---------+
(product_id, start_date, end_date) is the primary key (combination of columns with unique values) for this table.
Each row of this table indicates the price of the product_id in the period from start_date to end_date.
For each product_id there will be no two overlapping periods. That means there will be no two intersecting periods for the same product_id.
</pre>

&nbsp;

Table: <code>UnitsSold</code>

<pre>
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| purchase_date | date |
| units | int |
+---------------+---------+
This table may contain duplicate rows.
Each row of this table indicates the date, units, and product_id of each product sold. 
</pre>

&nbsp;

Write a solution to find the average selling price for each product. <code>average_price</code> should be rounded to 2 decimal places. If a product does not have any sold units, its average selling price is assumed to be 0.

Return the result table in any order.

The&nbsp;result format is in the following example.

&nbsp;
Example 1:

<pre>
Input: 
Prices table:
+------------+------------+------------+--------+
| product_id | start_date | end_date | price |
+------------+------------+------------+--------+
| 1 | 2019-02-17 | 2019-02-28 | 5 |
| 1 | 2019-03-01 | 2019-03-22 | 20 |
| 2 | 2019-02-01 | 2019-02-20 | 15 |
| 2 | 2019-02-21 | 2019-03-31 | 30 |
+------------+------------+------------+--------+
UnitsSold table:
+------------+---------------+-------+
| product_id | purchase_date | units |
+------------+---------------+-------+
| 1 | 2019-02-25 | 100 |
| 1 | 2019-03-01 | 15 |
| 2 | 2019-02-10 | 200 |
| 2 | 2019-03-22 | 30 |
+------------+---------------+-------+
Output: 
+------------+---------------+
| product_id | average_price |
+------------+---------------+
| 1 | 6.96 |
| 2 | 16.96 |
+------------+---------------+
Explanation: 
Average selling price = Total Price of Product / Number of products sold.
Average selling price for product 1 = ((100 * 5) + (15 * 20)) / 115 = 6.96
Average selling price for product 2 = ((200 * 15) + (30 * 30)) / 230 = 16.96
</pre>

average_selling_price

Average Selling Price

表：<code>Prices</code>

<pre>
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| start_date | date |
| end_date | date |
| price | int |
+---------------+---------+
(product_id，start_date，end_date) 是 <code>prices</code> 表的主键（具有唯一值的列的组合）。
<code>prices</code> 表的每一行表示的是某个产品在一段时期内的价格。
每个产品的对应时间段是不会重叠的，这也意味着同一个产品的价格时段不会出现交叉。</pre>

&nbsp;

表：<code>UnitsSold</code>

<pre>
+---------------+---------+
| Column Name | Type |
+---------------+---------+
| product_id | int |
| purchase_date | date |
| units | int |
+---------------+---------+
该表可能包含重复数据。
该表的每一行表示的是每种产品的出售日期，单位和产品 id。</pre>

&nbsp;

编写解决方案以查找每种产品的平均售价。<code>average_price</code> 应该 四舍五入到小数点后两位。如果产品没有任何售出，则假设其平均售价为 0。

返回结果表 无顺序要求 。

结果格式如下例所示。

&nbsp;

示例 1：

<pre>
输入：
Prices table:
+------------+------------+------------+--------+
| product_id | start_date | end_date | price |
+------------+------------+------------+--------+
| 1 | 2019-02-17 | 2019-02-28 | 5 |
| 1 | 2019-03-01 | 2019-03-22 | 20 |
| 2 | 2019-02-01 | 2019-02-20 | 15 |
| 2 | 2019-02-21 | 2019-03-31 | 30 |
+------------+------------+------------+--------+
UnitsSold table:
+------------+---------------+-------+
| product_id | purchase_date | units |
+------------+---------------+-------+
| 1 | 2019-02-25 | 100 |
| 1 | 2019-03-01 | 15 |
| 2 | 2019-02-10 | 200 |
| 2 | 2019-03-22 | 30 |
+------------+---------------+-------+
输出：
+------------+---------------+
| product_id | average_price |
+------------+---------------+
| 1 | 6.96 |
| 2 | 16.96 |
+------------+---------------+
解释：
平均售价 = 产品总价 / 销售的产品数量。
产品 1 的平均售价 = ((100 * 5)+(15 * 20) )/ 115 = 6.96
产品 2 的平均售价 = ((200 * 15)+(30 * 30) )/ 230 = 16.96</pre>