1. Read in the flights dataset, and define the grouping columns (AIRLINE), aggregating columns (ARR_DELAY), and aggregating functions (mean):

>>> flights = pd.read_csv('data/flights.csv')
>>> flights.head()

2. Place the grouping column in the groupby method and then call the agg method with a dictionary pairing the aggregating column with its aggregating function:

>>> flights.groupby('AIRLINE').agg({'ARR_DELAY':'mean'}).head()

3. Alternatively, you may place the aggregating column in the indexing operator and then pass the aggregating function as a string to agg:

>>> flights.groupby('AIRLINE')['ARR_DELAY'].agg('mean').head()
AIRLINE
AA     5.542661
AS    -0.833333
B6     8.692593
DL     0.339691
EV     7.034580
Name: ARR_DELAY, dtype: float64

4. The string names used in the previous step are a convenience pandas offers you to refer to a particular aggregation function. You can pass any aggregating function directly to the agg method such as the NumPy mean function. The output is the same as the previous step:

>>> flights.groupby('AIRLINE')['ARR_DELAY'].agg(np.mean).head()

5. It's possible to skip the agg method altogether in this case and use the mean method directly. This output is also the same as step 3:

>>> flights.groupby('AIRLINE')['ARR_DELAY'].mean().head()