l': ['Falcon', 'Falcon',
        ...                               'Parrot', 'Parrot'],
        ...                    'Max Speed': [380., 370., 24., 26.]})
        >>> df
           Animal  Max Speed
        0  Falcon      380.0
        1  Falcon      370.0
        2  Parrot       24.0
        3  Parrot       26.0
        >>> df.groupby(['Animal']).mean()
                Max Speed
        Animal
        Falcon      375.0
        Parrot       25.0

        **Hierarchical Indexes**

        We can groupby different levels of a hierarchical index
        using the `level` parameter:

        >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
        ...           ['Captive', 'Wild', 'Captive', 'Wild']]
        >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
        >>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
        ...                   index=index)
        >>> df
                        Max Speed
        Animal Type
        Falcon Captive      390.0
               Wild         350.0
        Parrot Captive       30.0
               Wild          20.0
        >>> df.groupby(level=0).mean()
                Max Speed
        Animal
        Falcon      370.0
        Parrot       25.0
        >>> df.groupby(level="Type").mean()
                 Max Speed
        Type
        Captive      210.0
        Wild         185.0

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

        >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
        >>> df = pd.DataFrame(l, columns=["a", "b", "c"])

        >>> df.groupby(by=["b"]).sum()
            a   c
        b
        1.0 2   3
        2.0 2   5

        >>> df.groupby(by=["b"], dropna=False).sum()
            a   c
        b
        1.0 2   3
        2.0 2   5
        NaN 1   4

        >>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
        >>> df = pd.DataFrame(l, columns=["a", "b", "c"])

        >>> df.groupby(by="a").sum()
            b     c
        a
        a   13.0   13.0
        b   12.3  123.0

        >>> df.groupby(by="a", dropna=False).sum()
            b     c
        a
        a   13.0   13.0
        b   12.3  123.0
        NaN 12.3   33.0

        When using ``.apply()``, use ``group_keys`` to include or exclude the
        group keys. The ``group_keys`` argument defaults to ``True`` (include).

        >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
        ...                               'Parrot', 'Parrot'],
        ...                    'Max Speed': [380., 370., 24., 26.]})
        >>> df.groupby("Animal", group_keys=True).apply(lambda x: x)
                  Animal  Max Speed
        Animal
        Falcon 0  Falcon      380.0
               1  Falcon      370.0
        Parrot 2  Parrot       24.0
               3  Parrot       26.0

        >>> df.groupby("Animal", group_keys=False).apply(lambda x: x)
           Animal  Max Speed
        0  Falcon      380.0
        1  Falcon      370.0
        2  Parrot       24.0
        3  Parrot       26.0
        r”