1. Read in the flights dataset, and use the pivot_table method to find the total number of canceled flights per origin airport for each airline:

>>> flights = pd.read_csv('data/flights.csv')
>>> fp = flights.pivot_table(index='AIRLINE', 
                             columns='ORG_AIR', 
                             values='CANCELLED', 
                             aggfunc='sum',
                             fill_value=0).round(2)
>>> fp.head()

2. A groupby aggregation cannot directly replicate this table. The trick is to group by all the columns in the index and columns parameters first:

>>> fg = flights.groupby(['AIRLINE', 'ORG_AIR'])['CANCELLED'].sum()
>>> fg.head()
AIRLINE  ORG_AIR
AA       ATL         3
         DEN         4
         DFW        86
         IAH         3
         LAS         3
Name: CANCELLED, dtype: int64

3. Use the unstack method to pivot the ORG_AIR index level to column names:

>>> fg_unstack = fg.unstack('ORG_AIR', fill_value=0)
>>> fp.equals(fg_unstack)
True