ne', 'one', 'two', 'three',
    ...                           'two', 'two'],
    ...                    'C' : [1, 5, 5, 2, 5, 5],
    ...                    'D' : [2.0, 5., 8., 1., 2., 9.]})
    >>> grouped = df.groupby('A')[['C', 'D']]
    >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
            C         D
    0 -1.154701 -0.577350
    1  0.577350  0.000000
    2  0.577350  1.154701
    3 -1.154701 -1.000000
    4  0.577350 -0.577350
    5  0.577350  1.000000

    Broadcast result of the transformation

    >>> grouped.transform(lambda x: x.max() - x.min())
        C    D
    0  4.0  6.0
    1  3.0  8.0
    2  4.0  6.0
    3  3.0  8.0
    4  4.0  6.0
    5  3.0  8.0

    >>> grouped.transform("mean")
        C    D
    0  3.666667  4.0
    1  4.000000  5.0
    2  3.666667  4.0
    3  4.000000  5.0
    4  3.666667  4.0
    5  4.000000  5.0

    .. versionchanged:: 1.3.0

    The resulting dtype will reflect the return value of the passed ``func``,
    for example:

    >>> grouped.transform(lambda x: x.astype(int).max())
    C  D
    0  5  8
    1  5  9
    2  5  8
    3  5  9
    4  5  8
    5  5  9
    ră