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.

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

Also copy underlying data.


The copy keyword will change behavior in pandas 3.0. Copy-on-Write 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

Deprecated since version 3.0.0.

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.

DataFrame or None

DataFrame with the renamed axis labels or None if inplace=True.


If any of the labels is not found in the selected axis and “errors=’raise’”.

See also


Set the name of the axis.


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