ctions over the rows.

    >>> df.agg(['sum', 'min'])
            A     B     C
    sum  12.0  15.0  18.0
    min   1.0   2.0   3.0

    Different aggregations per column.

    >>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
            A    B
    sum  12.0  NaN
    min   1.0  2.0
    max   NaN  8.0

    Aggregate different functions over the columns and rename the index of the resulting
    DataFrame.

    >>> df.agg(x=('A', 'max'), y=('B', 'min'), z=('C', 'mean'))
         A    B    C
    x  7.0  NaN  NaN
    y  NaN  2.0  NaN
    z  NaN  NaN  6.0

    Aggregate over the columns.

    >>> df.agg("mean", axis="columns")
    0    2.0
    1    5.0
    2    8.0
    3    NaN
    dtype: float64
    Ú	aggregate)