I am a long time SAS/SQL user and have always defaulted to using SQL for my groupbys

for example to do

select region
,case when age < 5 then 'Low'
when age >= 5 and age <= 10 then 'Middle'
else 'High' as duration
,sum(1) as total
,sum(profit) as profit
,sum(profit)/sum(1)  as avg_profit
,max(revenue) as max revenue
from table
where region not in ('A')
group by 
region,(case when age < 5 then 'Low'
when age >= 5 and age <= 10 then 'Middle'
else 'High)

I am trying to recreate the above in Pandas but I don’t know how to write it in as little code as possible as the above
Can anyone suggest an efficient way to write this in Pandas that doesn’t involve 5 merges and creating new columns beforehand?