It is possible to replicate much more complex pivot tables with groupby aggregations. For instance, take the following result from pivot_table:

>>> flights.pivot_table(index=['AIRLINE', 'MONTH'],
                        columns=['ORG_AIR', 'CANCELLED'],
                        values=['DEP_DELAY', 'DIST'],
                        aggfunc=[np.sum, np.mean],
                        fill_value=0)

To replicate this with a groupby aggregation, simply follow the same pattern from the recipe and place all the columns from the index and columns parameters into the groupby method and then unstack the columns:

>>> flights.groupby(['AIRLINE', 'MONTH', 'ORG_AIR', 'CANCELLED']) 
           ['DEP_DELAY', 'DIST'] 
           .agg(['mean', 'sum']) 
           .unstack(['ORG_AIR', 'CANCELLED'], fill_value=0) 
           .swaplevel(0, 1, axis='columns')

There are a few differences. The pivot_table method does not accept aggregation functions as strings when passed as a list like the agg groupby method. Instead, you must use NumPy functions. The order of the column levels also differs, with pivot_table putting the aggregation functions at a level preceding the columns in the values parameter. This is equalized with the swaplevel method that, in this instance, switches the order of the top two levels.