pandas.DataFrame.rename#

DataFrame.rename(mapper=None, *, index=None, columns=None, axis=None, copy=<no_default>, inplace=False, level=None, errors='ignore')[source]#

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 user guide for more.

Parameters:

mapperdict-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.
indexdict-like or function: Alternative to specifying axis (mapper, axis=0 is equivalent to index=mapper).
columnsdict-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’.
copybool, default False: This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
inplacebool, default False: Whether to modify the DataFrame rather than creating a new one. If True then value of copy is ignored.
levelint 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