pandas.Series.rename

Series.rename(index=None, *, axis=None, copy=None, inplace=False, level=None, errors='ignore')

Alter Series index labels or name.

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.

Alternatively, change Series.name with a scalar value.

See the user guide for more.

Parameters:

indexscalar, hashable sequence, dict-like or function optional

Functions or dict-like are transformations to apply to the index. Scalar or hashable sequence-like will alter the Series.name attribute.

axis{0 or ‘index’}

Unused. Parameter needed for compatibility with DataFrame.

copybool, default True

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

Whether to return a new Series. If True the value of copy is ignored.

levelint or level name, default None

In case of MultiIndex, only rename labels in the specified level.

errors{‘ignore’, ‘raise’}, default ‘ignore’

If ‘raise’, raise KeyError when a dict-like mapper or index 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:

Series or None

Series with index labels or name altered or None if inplace=True.

See also

DataFrame.rename

Corresponding DataFrame method.

Series.rename_axis

Set the name of the axis.

Examples

>>> s = pd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: int64
>>> s.rename("my_name")  # scalar, changes Series.name
0    1
1    2
2    3
Name: my_name, dtype: int64
>>> s.rename(lambda x: x ** 2)  # function, changes labels
0    1
1    2
4    3
dtype: int64
>>> s.rename({1: 3, 2: 5})  # mapping, changes labels
0    1
3    2
5    3
dtype: int64