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Classe « DataFrame »

Méthode pandas.DataFrame.eval

Signature de la méthode eval

def eval(self, expr: 'str', *, inplace: 'bool' = False, **kwargs) -> 'Any | None' 

Description

help(DataFrame.eval)

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

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

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