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Classe « Series »

Méthode pandas.Series.rename

Signature de la méthode rename

def rename(self, index: 'Renamer | Hashable | None' = None, *, axis: 'Axis | None' = None, copy: 'bool | None' = None, inplace: 'bool' = False, level: 'Level | None' = None, errors: 'IgnoreRaise' = 'ignore') -> 'Series | None' 

Description

help(Series.rename)

Alter Series index labels or name.

Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra labels listed don't throw an
error.

Alternatively, change ``Series.name`` with a scalar value.

See the :ref:`user guide <basics.rename>` for more.

Parameters
----------
index : scalar, hashable sequence, dict-like or function optional
    Functions or dict-like are transformations to apply to
    the index.
    Scalar or hashable sequence-like will alter the ``Series.name``
    attribute.
axis : {0 or 'index'}
    Unused. Parameter needed for compatibility with DataFrame.
copy : bool, default True
    Also copy underlying data.

    .. note::
        The `copy` keyword will change behavior in pandas 3.0.
        `Copy-on-Write
        <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
        will be enabled by default, which means that all methods with a
        `copy` keyword will use a lazy copy mechanism to defer the copy and
        ignore the `copy` keyword. The `copy` keyword will be removed in a
        future version of pandas.

        You can already get the future behavior and improvements through
        enabling copy on write ``pd.options.mode.copy_on_write = True``
inplace : bool, default False
    Whether to return a new Series. If True the value of copy is ignored.
level : int or level name, default None
    In case of MultiIndex, only rename labels in the specified level.
errors : {'ignore', 'raise'}, default 'ignore'
    If 'raise', raise `KeyError` when a `dict-like mapper` or
    `index` contains labels that are not present in the index being transformed.
    If 'ignore', existing keys will be renamed and extra keys will be ignored.

Returns
-------
Series or None
    Series with index labels or name altered or None if ``inplace=True``.

See Also
--------
DataFrame.rename : Corresponding DataFrame method.
Series.rename_axis : Set the name of the axis.

Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: int64
>>> s.rename("my_name")  # scalar, changes Series.name
0    1
1    2
2    3
Name: my_name, dtype: int64
>>> s.rename(lambda x: x ** 2)  # function, changes labels
0    1
1    2
4    3
dtype: int64
>>> s.rename({1: 3, 2: 5})  # mapping, changes labels
0    1
3    2
5    3
dtype: int64

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