roupby(level="Type").mean()
        Type
        Captive    210.0
        Wild       185.0
        Name: Max Speed, dtype: float64

        We can also choose to include `NA` in group keys or not by defining
        `dropna` parameter, the default setting is `True`.

        >>> ser = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan])
        >>> ser.groupby(level=0).sum()
        a    3
        b    3
        dtype: int64

        >>> ser.groupby(level=0, dropna=False).sum()
        a    3
        b    3
        NaN  3
        dtype: int64

        >>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot']
        >>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed")
        >>> ser.groupby(["a", "b", "a", np.nan]).mean()
        a    210.0
        b    350.0
        Name: Max Speed, dtype: float64

        >>> ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()
        a    210.0
        b    350.0
        NaN   20.0
        Name: Max Speed, dtype: float64
        Ú