单挑力扣（LeetCode）SQL笔试题：1294. 不同国家的天气类型（难度：简单）

国家表：Countries
+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| country_id    | int     |
| country_name  | varchar |
+---------------+---------+
country_id 是这张表的主键。
该表的每行有 country_id 和 country_name 两列。
天气表：Weather
+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| country_id    | int     |
| weather_state | varchar |
| day           | date    |
+---------------+---------+
(country_id, day) 是该表的复合主键。
该表的每一行记录了某个国家某一天的天气情况。


写一段 SQL 来找到表中每个国家在 2019 年 11 月的天气类型。
天气类型的定义如下：当 weather_state 的平均值小于或等于15返回 Cold，当 weather_state 的平均值大于或等于 25 返回 Hot，否则返回Warm。
你可以以任意顺序返回你的查询结果。
查询结果格式如下所示：
Countries table:
+------------+--------------+
| country_id | country_name |
+------------+--------------+
| 2          | USA          |
| 3          | Australia    |
| 7          | Peru         |
| 5          | China        |
| 8          | Morocco      |
| 9          | Spain        |
+------------+--------------+
Weather table:
+------------+---------------+------------+
| country_id | weather_state | day        |
+------------+---------------+------------+
| 2          | 15            | 2019-11-01 |
| 2          | 12            | 2019-10-28 |
| 2          | 12            | 2019-10-27 |
| 3          | -2            | 2019-11-10 |
| 3          | 0             | 2019-11-11 |
| 3          | 3             | 2019-11-12 |
| 5          | 16            | 2019-11-07 |
| 5          | 18            | 2019-11-09 |
| 5          | 21            | 2019-11-23 |
| 7          | 25            | 2019-11-28 |
| 7          | 22            | 2019-12-01 |
| 7          | 20            | 2019-12-02 |
| 8          | 25            | 2019-11-05 |
| 8          | 27            | 2019-11-15 |
| 8          | 31            | 2019-11-25 |
| 9          | 7             | 2019-10-23 |
| 9          | 3             | 2019-12-23 |
+------------+---------------+------------+
Result table:
+--------------+--------------+
| country_name | weather_type |
+--------------+--------------+
| USA          | Cold         |
| Austraila    | Cold         |
| Peru         | Hot          |
| China        | Warm         |
| Morocco      | Hot          |
+--------------+--------------+
USA 11 月的平均 weather_state 为 (15)  1 = 15 所以天气类型为 Cold。
Australia 11 月的平均 weather_state 为 (-2 + 0 + 3)  3 = 0.333 所以天气类型为 Cold。
Peru 11 月的平均 weather_state 为 (25)  1 = 25 所以天气类型为 Hot。
China 11 月的平均 weather_state 为 (16 + 18 + 21)  3 = 18.333 所以天气类型为 Warm。
Morocco 11 月的平均 weather_state 为 (25 + 27 + 31)  3 = 27.667 所以天气类型为 Hot。
我们并不知道 Spain 在 11 月的 weather_state 情况所以无需将他包含在结果中。


来源：力扣（LeetCode）
链接：https://leetcode.cn/problems/weather-type-in-each-country

#测试数据
Create table If Not Exists Countries (country_id int, country_name varchar(20));
Create table If Not Exists Weather (country_id int, weather_state int, day date);


insert into Countries (country_id, country_name) values ('2', 'USA');
insert into Countries (country_id, country_name) values ('3', 'Australia');
insert into Countries (country_id, country_name) values ('7', 'Peru');
insert into Countries (country_id, country_name) values ('5', 'China');
insert into Countries (country_id, country_name) values ('8', 'Morocco');
insert into Countries (country_id, country_name) values ('9', 'Spain');


insert into Weather (country_id, weather_state, day) values ('2', '15', '2019-11-01');
insert into Weather (country_id, weather_state, day) values ('2', '12', '2019-10-28');
insert into Weather (country_id, weather_state, day) values ('2', '12', '2019-10-27');
insert into Weather (country_id, weather_state, day) values ('3', '-2', '2019-11-10');
insert into Weather (country_id, weather_state, day) values ('3', '0', '2019-11-11');
insert into Weather (country_id, weather_state, day) values ('3', '3', '2019-11-12');
insert into Weather (country_id, weather_state, day) values ('5', '16', '2019-11-07');
insert into Weather (country_id, weather_state, day) values ('5', '18', '2019-11-09');
insert into Weather (country_id, weather_state, day) values ('5', '21', '2019-11-23');
insert into Weather (country_id, weather_state, day) values ('7', '25', '2019-11-28');
insert into Weather (country_id, weather_state, day) values ('7', '22', '2019-12-01');
insert into Weather (country_id, weather_state, day) values ('7', '20', '2019-12-02');
insert into Weather (country_id, weather_state, day) values ('8', '25', '2019-11-05');
insert into Weather (country_id, weather_state, day) values ('8', '27', '2019-11-15');
insert into Weather (country_id, weather_state, day) values ('8', '31', '2019-11-25');
insert into Weather (country_id, weather_state, day) values ('9', '7', '2019-10-23');
insert into Weather (country_id, weather_state, day) values ('9', '3', '2019-12-23');

select
    max(b.country_name) country_name,
    case when avg(a.weather_state) <= 15 then 'Cold'
         when avg(a.weather_state) >= 25 then 'Hot'
         else 'Warm'
    end weather_type
from Weather a
left join Countries b
on a.country_id = b.country_id
where a.day between '2019-11-01' and '2019-11-30'
group by a.country_id;