Pythonic way to apply different aggregate functions to different columns of a pandas dataframe? And to name the columns efficiently?

Question

My issue

In SQL it is very easy to apply different aggregate functions to different columns, e.g. :

select item, sum(a) as [sum of a], avg(b) as [avg of