Evaluate a string describing operations on DataFrame columns.

        Operates on columns only, not specific rows or elements.  This allows
        `eval` to run arbitrary code, which can make you vulnerable to code
        injection if you pass user input to this function.

        Parameters
        ----------
        expr : str
            The expression string to evaluate.
        inplace : bool, default False
            If the expression contains an assignment, whether to perform the
            operation inplace and mutate the existing DataFrame. Otherwise,
            a new DataFrame is returned.

            .. versionadded:: 0.18.0.
        kwargs : dict
            See the documentation for :func:`eval` for complete details
            on the keyword arguments accepted by
            :meth:`~pandas.DataFrame.query`.

        Returns
        -------
        ndarray, scalar, or pandas object
            The result of the evaluation.

        See Also
        --------
        DataFrame.query : Evaluates a boolean expression to query the columns
            of a frame.
        DataFrame.assign : Can evaluate an expression or function to create new
            values for a column.
        eval : Evaluate a Python expression as a string using various
            backends.

        Notes
        -----
        For more details see the API documentation for :func:`~eval`.
        For detailed examples see :ref:`enhancing performance with eval
        <enhancingperf.eval>`.

        Examples
        --------
        >>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
        >>> df
           A   B
        0  1  10
        1  2   8
        2  3   6
        3  4   4
        4  5   2
        >>> df.eval('A + B')
        0    11
        1    10
        2     9
        3     8
        4     7
        dtype: int64

        Assignment is allowed though by default the original DataFrame is not
        modified.

        >>> df.eval('C = A + B')
           A   B   C
        0  1  10  11
        1  2   8  10
        2  3   6   9
        3  4   4   8
        4  5   2   7
        >>> df
           A   B
        0  1  10
        1  2   8
        2  3   6
        3  4   4
        4  5   2

        Use ``inplace=True`` to modify the original DataFrame.

        >>> df.eval('C = A + B', inplace=True)
        >>> df
           A   B   C
        0  1  10  11
        1  2   8  10
        2  3   6   9
        3  4   4   8
        4  5   2   7

def eval(self, expr, inplace=False, **kwargs):
        """
        Evaluate a string describing operations on DataFrame columns.

        Operates on columns only, not specific rows or elements.  This allows
        `eval` to run arbitrary code, which can make you vulnerable to code
        injection if you pass user input to this function.

        Parameters
        ----------
        expr : str
            The expression string to evaluate.
        inplace : bool, default False
            If the expression contains an assignment, whether to perform the
            operation inplace and mutate the existing DataFrame. Otherwise,
            a new DataFrame is returned.

            .. versionadded:: 0.18.0.
        kwargs : dict
            See the documentation for :func:`eval` for complete details
            on the keyword arguments accepted by
            :meth:`~pandas.DataFrame.query`.

        Returns
        -------
        ndarray, scalar, or pandas object
            The result of the evaluation.

        See Also
        --------
        DataFrame.query : Evaluates a boolean expression to query the columns
            of a frame.
        DataFrame.assign : Can evaluate an expression or function to create new
            values for a column.
        eval : Evaluate a Python expression as a string using various
            backends.

        Notes
        -----
        For more details see the API documentation for :func:`~eval`.
        For detailed examples see :ref:`enhancing performance with eval
        <enhancingperf.eval>`.

        Examples
        --------
        >>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
        >>> df
           A   B
        0  1  10
        1  2   8
        2  3   6
        3  4   4
        4  5   2
        >>> df.eval('A + B')
        0    11
        1    10
        2     9
        3     8
        4     7
        dtype: int64

        Assignment is allowed though by default the original DataFrame is not
        modified.

        >>> df.eval('C = A + B')
           A   B   C
        0  1  10  11
        1  2   8  10
        2  3   6   9
        3  4   4   8
        4  5   2   7
        >>> df
           A   B
        0  1  10
        1  2   8
        2  3   6
        3  4   4
        4  5   2

        Use ``inplace=True`` to modify the original DataFrame.

        >>> df.eval('C = A + B', inplace=True)
        >>> df
           A   B   C
        0  1  10  11
        1  2   8  10
        2  3   6   9
        3  4   4   8
        4  5   2   7
        """
        from pandas.core.computation.eval import eval as _eval

        inplace = validate_bool_kwarg(inplace, 'inplace')
        resolvers = kwargs.pop('resolvers', None)
        kwargs['level'] = kwargs.pop('level', 0) + 1
        if resolvers is None:
            index_resolvers = self._get_index_resolvers()
            column_resolvers = \
                self._get_space_character_free_column_resolvers()
            resolvers = column_resolvers, index_resolvers
        if 'target' not in kwargs:
            kwargs['target'] = self
        kwargs['resolvers'] = kwargs.get('resolvers', ()) + tuple(resolvers)
        return _eval(expr, inplace=inplace, **kwargs)