..                          "one", "one", "two", "two"],
        ...                    "C": ["small", "large", "large", "small",
        ...                          "small", "large", "small", "small",
        ...                          "large"],
        ...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
        ...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
        >>> df
             A    B      C  D  E
        0  foo  one  small  1  2
        1  foo  one  large  2  4
        2  foo  one  large  2  5
        3  foo  two  small  3  5
        4  foo  two  small  3  6
        5  bar  one  large  4  6
        6  bar  one  small  5  8
        7  bar  two  small  6  9
        8  bar  two  large  7  9

        This first example aggregates values by taking the sum.

        >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
        ...                        columns=['C'], aggfunc="sum")
        >>> table
        C        large  small
        A   B
        bar one    4.0    5.0
            two    7.0    6.0
        foo one    4.0    1.0
            two    NaN    6.0

        We can also fill missing values using the `fill_value` parameter.

        >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
        ...                        columns=['C'], aggfunc="sum", fill_value=0)
        >>> table
        C        large  small
        A   B
        bar one      4      5
            two      7      6
        foo one      4      1
            two      0      6

        The next example aggregates by taking the mean across multiple columns.

        >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
        ...                        aggfunc={'D': "mean", 'E': "mean"})
        >>> table
                        D         E
        A   C
        bar large  5.500000  7.500000
            small  5.500000  8.500000
        foo large  2.000000  4.500000
            small  2.333333  4.333333

        We can also calculate multiple types of aggregations for any given
        value column.

        >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
        ...                        aggfunc={'D': "mean",
        ...                                 'E': ["min", "max", "mean"]})
        >>> table
                          D   E
                       mean max      mean  min
        A   C
        bar large  5.500000   9  7.500000    6
            small  5.500000   9  8.500000    8
        foo large  2.000000   5  4.500000    4
            small  2.333333   6  4.333333    2
        