Classe « DataFrame »

Méthode pandas.DataFrame.eval

Signature de la méthode eval

def eval(self, expr, inplace=False, **kwargs) 

Description

eval.__doc__

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

        Returns
        -------
        ndarray, scalar, pandas object, or None
            The result of the evaluation or None if ``inplace=True``.

        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

        Multiple columns can be assigned to using multi-line expressions:

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