Numerical Computing with Python

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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', ...