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Module « pandas »

Fonction eval - module pandas

Signature de la fonction eval

def eval(expr: 'str | BinOp', parser: 'str' = 'pandas', engine: 'str | None' = None, local_dict=None, global_dict=None, resolvers=(), level: 'int' = 0, target=None, inplace: 'bool' = False) 

Description

help(pandas.eval)

Evaluate a Python expression as a string using various backends.

The following arithmetic operations are supported: ``+``, ``-``, ``*``,
``/``, ``**``, ``%``, ``//`` (python engine only) along with the following
boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not).
Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`,
:keyword:`or`, and :keyword:`not` with the same semantics as the
corresponding bitwise operators.  :class:`~pandas.Series` and
:class:`~pandas.DataFrame` objects are supported and behave as they would
with plain ol' Python evaluation.

Parameters
----------
expr : str
    The expression to evaluate. This string cannot contain any Python
    `statements
    <https://docs.python.org/3/reference/simple_stmts.html#simple-statements>`__,
    only Python `expressions
    <https://docs.python.org/3/reference/simple_stmts.html#expression-statements>`__.
parser : {'pandas', 'python'}, default 'pandas'
    The parser to use to construct the syntax tree from the expression. The
    default of ``'pandas'`` parses code slightly different than standard
    Python. Alternatively, you can parse an expression using the
    ``'python'`` parser to retain strict Python semantics.  See the
    :ref:`enhancing performance <enhancingperf.eval>` documentation for
    more details.
engine : {'python', 'numexpr'}, default 'numexpr'

    The engine used to evaluate the expression. Supported engines are

    - None : tries to use ``numexpr``, falls back to ``python``
    - ``'numexpr'`` : This default engine evaluates pandas objects using
      numexpr for large speed ups in complex expressions with large frames.
    - ``'python'`` : Performs operations as if you had ``eval``'d in top
      level python. This engine is generally not that useful.

    More backends may be available in the future.
local_dict : dict or None, optional
    A dictionary of local variables, taken from locals() by default.
global_dict : dict or None, optional
    A dictionary of global variables, taken from globals() by default.
resolvers : list of dict-like or None, optional
    A list of objects implementing the ``__getitem__`` special method that
    you can use to inject an additional collection of namespaces to use for
    variable lookup. For example, this is used in the
    :meth:`~DataFrame.query` method to inject the
    ``DataFrame.index`` and ``DataFrame.columns``
    variables that refer to their respective :class:`~pandas.DataFrame`
    instance attributes.
level : int, optional
    The number of prior stack frames to traverse and add to the current
    scope. Most users will **not** need to change this parameter.
target : object, optional, default None
    This is the target object for assignment. It is used when there is
    variable assignment in the expression. If so, then `target` must
    support item assignment with string keys, and if a copy is being
    returned, it must also support `.copy()`.
inplace : bool, default False
    If `target` is provided, and the expression mutates `target`, whether
    to modify `target` inplace. Otherwise, return a copy of `target` with
    the mutation.

Returns
-------
ndarray, numeric scalar, DataFrame, Series, or None
    The completion value of evaluating the given code or None if ``inplace=True``.

Raises
------
ValueError
    There are many instances where such an error can be raised:

    - `target=None`, but the expression is multiline.
    - The expression is multiline, but not all them have item assignment.
      An example of such an arrangement is this:

      a = b + 1
      a + 2

      Here, there are expressions on different lines, making it multiline,
      but the last line has no variable assigned to the output of `a + 2`.
    - `inplace=True`, but the expression is missing item assignment.
    - Item assignment is provided, but the `target` does not support
      string item assignment.
    - Item assignment is provided and `inplace=False`, but the `target`
      does not support the `.copy()` method

See Also
--------
DataFrame.query : Evaluates a boolean expression to query the columns
        of a frame.
DataFrame.eval : Evaluate a string describing operations on
        DataFrame columns.

Notes
-----
The ``dtype`` of any objects involved in an arithmetic ``%`` operation are
recursively cast to ``float64``.

See the :ref:`enhancing performance <enhancingperf.eval>` documentation for
more details.

Examples
--------
>>> df = pd.DataFrame({"animal": ["dog", "pig"], "age": [10, 20]})
>>> df
  animal  age
0    dog   10
1    pig   20

We can add a new column using ``pd.eval``:

>>> pd.eval("double_age = df.age * 2", target=df)
  animal  age  double_age
0    dog   10          20
1    pig   20          40

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