pandas.Series.rename#

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

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.
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