DataFrame.rename(self, mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore')[source]

Alter axes 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 functions 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).

axisint or str

Axis to target with mapper. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’.

copybool, default True

Also copy underlying data.

inplacebool, default False

Whether to return a new DataFrame. 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 with the renamed axis labels.


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”})


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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