pandas.Series.rename — pandas 3.0.0rc0+33.g1fd184de2a documentation (original) (raw)

Series.rename(index=None, *, axis=None, copy=<no_default>, 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.nameattribute.

axis{0 or ‘index’}

Unused. Parameter needed for compatibility with DataFrame.

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-Writefor more details.

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 orindex 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

A shallow copy with index labels or name altered, or the same object if inplace=True and index is not a dict or callable else None.

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