1. Read in the flights dataset, and answer the first query by defining the grouping columns (AIRLINE, WEEKDAY), the aggregating column (CANCELLED), and the aggregating function (sum):

>>> flights.groupby(['AIRLINE', 'WEEKDAY'])['CANCELLED'] 
           .agg('sum').head(7)
AIRLINE  WEEKDAY
AA       1          41
         2           9
         3          16
         4          20
         5          18
         6          21
         7          29
Name: CANCELLED, dtype: int64

2. Answer the second query by using a list for each pair of grouping and aggregating columns. Also, use a list for the aggregating functions:

>>> flights.groupby(['AIRLINE', 'WEEKDAY']) 
            ['CANCELLED', 'DIVERTED'].agg(['sum', 'mean']).head(7)

3. Answer the third query using a dictionary in the agg method to map specific aggregating columns to specific aggregating functions:

>>> group_cols = ['ORG_AIR', 'DEST_AIR']
>>> agg_dict = {'CANCELLED':['sum', 'mean', 'size'], 
                'AIR_TIME':['mean', 'var']}
>>> flights.groupby(group_cols).agg(agg_dict).head()