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提交日期 题目名称 提交代码
2026-06-12 招建银行信用卡中心客户挽留-电商平台分类  
select 
mch_nm as merchant_name,
case when mch_nm like "%拼多多%" or mch_nm like "%上海寻梦信息科技%"then '拼多多'
		when mch_nm like "%京东%"then '京东'
when mch_nm like "%淘宝%"then '淘宝'
when mch_nm like "%抖音%"then '抖音'
when mch_nm like "%小红书%"then '小红书'
else '其它'
end platform
from ccb_trx_rcd;
2026-06-12 招建银行信用卡中心客户挽留-电商平台分类  
select 
mch_nm as merchant_name,
case when mch_nm like "拼多多" or mch_nm like "上海寻梦信息科技"then '拼多多'
		when mch_nm like "京东"then '京东'
when mch_nm like "淘宝"then '淘宝'
when mch_nm like "抖音"then '抖音'
when mch_nm like "小红书"then '小红书'
else '其它'
end platform
from ccb_trx_rcd;
2026-06-12 登录天数分布  
with t1 as(
select 
usr_id,
count(distinctDATE(login_time)) cnt
from user_login_log
where login_time >= DATE_SUB(current_date() , INTERVAL 180 DAY)
group by usr_id
order by cnt
)
select 
sum(if(0 < cnt and cnt <6,1,0)) as days_1_to_5,
sum(if(5 < cnt and cnt <11,1,0)) as days_1days_6_to_10_to_5,
sum(if(10 < cnt and cnt <21,1,0)) as days_11_to_20,
sum(if(20 < cnt,1,0)) as days_over_20
from t1
2026-06-12 登录天数分布  
with t1 as(
select 
usr_id,
count(*) cnt
from user_login_log
where login_time >= DATE_SUB(current_date() , INTERVAL 180 DAY)
group by usr_id
order by cnt
)
select 
sum(if(0 < cnt and cnt <6,1,0)) as days_1_to_5,
sum(if(4 < cnt and cnt <11,1,0)) as days_1days_6_to_10_to_5,
sum(if(9 < cnt and cnt <21,1,0)) as days_11_to_20,
sum(if(20 < cnt,1,0)) as days_over_20
from t1
2026-06-12 滴滴出行订单分析（二）用户打车次数排名  
with t1 as (
select 
cust_uid,
count(*) cnt
from didi_sht_rcd 
group by cust_uid 
 ),
t2 as (select * ,
rank()over(order by cnt desc,cust_uid asc) as row_cnt
from t1
)
select * from t2
where row_cnt <= 10
2026-06-12 一线城市历年平均气温  
select year(dt),
cast(avg(if(city = 'beijing',tmp_h,null)) as decimal(4,2)) as '北京',
cast(avg(if(city = 'shanghai',tmp_h,null)) as decimal(4,2)) as '上海',
cast(avg(if(city = 'shenzhen',tmp_h,null)) as decimal(4,2)) as '深圳',
cast(avg(if(city = 'guangzhou',tmp_h,null)) as decimal(4,2)) as '广州'
from weather_rcd_china 
group by year(dt)
2026-06-11 一线城市历年平均气温  
select year(dt) as Y
    ,cast(avg(case when city='beijing' then tmp_h else null end) as decimal(4,2)) as '北京'
    ,cast(avg(case when city='shanghai' then tmp_h else null end) as decimal(4,2)) as 上海
    ,cast(avg(case when city='shenzhen' then tmp_h else null end) as decimal(4,2)) as 深圳
    ,cast(avg(case when city='guangzhou' then tmp_h else null end) as decimal(4,2)) as 广州
from
    weather_rcd_china
where 
    year(dt) between 2011 and 2022
group by 
    year(dt)