pandas.DataFrame.rename_axis#

DataFrame.rename_axis(mapper=<no_default>, *, index=<no_default>, columns=<no_default>, axis=0, copy=<no_default>, inplace=False)[source]#

Set the name of the axis for the index or columns.

Parameters:
mapperscalar, list-like, optional

Value to set the axis name attribute.

Use either mapper and axis to specify the axis to target with mapper, or index and/or columns.

indexscalar, list-like, dict-like or function, optional

A scalar, list-like, dict-like or functions transformations to apply to that axis’ values.

columnsscalar, list-like, dict-like or function, optional

A scalar, list-like, dict-like or functions transformations to apply to that axis’ values.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The axis to rename.

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

Modifies the object directly, instead of creating a new Series or DataFrame.

Returns:
DataFrame, or None

The same type as the caller or None if inplace=True.

See also

Series.rename

Alter Series index labels or name.

DataFrame.rename

Alter DataFrame index labels or name.

Index.rename

Set new names on index.

Notes

DataFrame.rename_axis supports two calling conventions

  • (index=index_mapper, columns=columns_mapper, ...)

  • (mapper, axis={'index', 'columns'}, ...)

The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter copy is ignored.

The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.

We highly recommend using keyword arguments to clarify your intent.

Examples

DataFrame

>>> df = pd.DataFrame(
...     {"num_legs": [4, 4, 2], "num_arms": [0, 0, 2]}, ["dog", "cat", "monkey"]
... )
>>> df
        num_legs  num_arms
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("animal")
>>> df
        num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs   num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2

MultiIndex

>>> df.index = pd.MultiIndex.from_product(
...     [["mammal"], ["dog", "cat", "monkey"]], names=["type", "name"]
... )
>>> df
limbs          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2

>>> df.rename_axis(index={"type": "class"})
limbs          num_legs  num_arms
class  name
mammal dog            4         0
       cat            4         0
       monkey         2         2

>>> df.rename_axis(columns=str.upper)
LIMBS          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2
On this page

This Page