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Classe « DataFrame »

Méthode pandas.DataFrame.rename

Signature de la méthode rename

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

Description

help(DataFrame.rename)

Rename columns or index labels.

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.

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

Parameters
----------
mapper : dict-like or function
    Dict-like or function transformations to apply to
    that axis' values. Use either ``mapper`` and ``axis`` to
    specify the axis to target with ``mapper``, or ``index`` and
    ``columns``.
index : dict-like or function
    Alternative to specifying axis (``mapper, axis=0``
    is equivalent to ``index=mapper``).
columns : dict-like or function
    Alternative to specifying axis (``mapper, axis=1``
    is equivalent to ``columns=mapper``).
axis : {0 or 'index', 1 or 'columns'}, default 0
    Axis to target with ``mapper``. Can be either the axis name
    ('index', 'columns') or number (0, 1). The default is 'index'.
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 modify the DataFrame rather than creating a new one.
    If True then value of copy is ignored.
level : int or level name, default None
    In case of a MultiIndex, only rename labels in the specified
    level.
errors : {'ignore', 'raise'}, default 'ignore'
    If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
    or `columns` 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
-------
DataFrame or None
    DataFrame with the renamed axis labels or None if ``inplace=True``.

Raises
------
KeyError
    If any of the labels is not found in the selected axis and
    "errors='raise'".

See Also
--------
DataFrame.rename_axis : Set the name of the axis.

Examples
--------
``DataFrame.rename`` supports two calling conventions

* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``

We *highly* recommend using keyword arguments to clarify your
intent.

Rename columns using a mapping:

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

Rename index using a mapping:

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
   A  B
x  1  4
y  2  5
z  3  6

Cast index labels to a different type:

>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')

>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis

Using axis-style parameters:

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6

>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6

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