no yes with dataframeyou have more options as you can specify list of functions to apply to all of the columns or different functions per column to startsuppose we wanted to compute the same three statistics for the tip_pct and total_bill columnsin [ ]functions ['count''mean''max'in [ ]result grouped['tip_pct''total_bill'agg(functionsin [ ]result out[ ]sex smoker female no yes male no yes tip_pct count mean max total_bill count mean max as you can seethe resulting dataframe has hierarchical columnsthe same as you would get aggregating each column separately and using concat to glue the results together using the column names as the keys argumentin [ ]result['tip_pct'out[ ]count mean sex smoker female no yes male no yes max as abovea list of tuples with custom names can be passedin [ ]ftuples [('durchschnitt''mean')('abweichung'np var)in [ ]grouped['tip_pct''total_bill'agg(ftuplesout[ ]tip_pct total_bill durchschnitt abweichung durchschnitt abweichung sex smoker female no yes male no yes nowsuppose you wanted to apply potentially different functions to one or more of the columns the trick is to pass dict to agg that contains mapping of column names to any of the function specifications listed so farin [ ]grouped agg({'tipnp max'size'sum'}out[ ]size tip sex smoker data aggregation www it-ebooks info