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pandas.Series.rename_axis — pandas 2.3.1 documentation

Series.rename_axis(mapper=<no_default>, *, index=<no_default>, axis=0, copy=True, 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.

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

A scalar, list-like, dict-like or functions transformations to apply to that axis’ values. Note that the columns parameter is not allowed if the object is a Series. This parameter only apply for DataFrame type objects.

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

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

The axis to rename. For Series this parameter is unused and defaults to 0.

copybool, default None

Also copy underlying data.

Note

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

inplacebool, default False

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

Returns:

Series, DataFrame, or None

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

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

Series

>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0       dog
1       cat
2    monkey
dtype: object
>>> s.rename_axis("animal")
animal
0    dog
1    cat
2    monkey
dtype: object

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

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