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

Another solid variation of the solution is to do what @MaxU did with this solution to a similar question and wrap the individual functions in a Pandas series, thus only requiring a reset_index() to return a dataframe.

First, define the functions for transformations:

def ed(group):
    return group.elapsed_time * group.num_cores).sum() / 86400

def rd(group):
    return group.running_time * group.num_cores).sum() / 86400

Wrap them up in a Series using get_stats:

def get_stats(group):
    return pd.Series({'elapsed_days': ed(group),
                      'running_days':rd(group)})

Finally:

lasts.groupby('user').apply(get_stats).reset_index()