pandas, apply multiple functions of multiple columns to groupby object

Question

I want to apply multiple functions of multiple columns to a groupby object which results in a new pandas.DataFrame.

I know how to do it in seperate steps:

Answer 1

In response to the bounty, we can make it more general, by using partial application, from the standard libraries functools.partial function.

import functools
import pandas as pd

#same data as other answer:
lasts = pd.DataFrame({'user':['a','s','d','d'],
                   'elapsed_time':[40000,50000,60000,90000],
                   'running_time':[30000,20000,30000,15000],
                   'num_cores':[7,8,9,4]})

#define the desired lambda as a function:
def myfunc(column, df, cores):
    return (column * df.ix[column.index][cores]).sum()/86400

#use the partial to define the function with a given column and df:
mynewfunc = functools.partial(myfunc, df = lasts, cores = 'num_cores')

#agg by the partial function
lasts.groupby('user').agg({'elapsed_time':mynewfunc, 'running_time':mynewfunc})

Which gives us:

    running_time    elapsed_time
user        
a   2.430556    3.240741
d   3.819444    10.416667
s   1.851852    4.629630

This is not super useful for the example given, but may be more useful as a general example.