不同国家的天气类型

题目描述：

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。

 

解题思路：

if用法，注意条件

select c.country_name , if(sum(w.weather_state) / count(w.weather_state) <= 15 , 'Cold' , if(sum(w.weather_state) / count(w.weather_state) < 25, 'Hot' , 'Warm')) weather_tupe
from weather w join countries c
on w.country_id = c.country_id
where w.day between '2019-11-01' and '2019-11-31'
group by c.country_id;

 

表结构：

create table countries(
    country_id int,
    country_name varchar(150),
    primary key (country_id)
);

create table weather(
    country_id int,
    weather_state varchar(150),
    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,'2019-11-09');
insert into weather(country_id, weather_state, day) VALUES (5,18,'2019-11-23');
insert into weather(country_id, weather_state, day) VALUES (5,21,'2019-11-28');
insert into weather(country_id, weather_state, day) VALUES (7,25,'2019-12-01');
insert into weather(country_id, weather_state, day) VALUES (7,22,'2019-11-05');
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');