modin.pandas.Series.rename¶
- Series.rename(index: Renamer | Hashable | None = None, *, axis: Axis | None = None, copy: bool | None = None, inplace: bool = False, level: Level | None = None, errors: IgnoreRaise = 'ignore') Series | None [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.- Parameters:
index (scalar, 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.
copy (bool, default True) – Also copy underlying data. copy has been ignored with Snowflake execution engine.
inplace (bool, default False) – Whether to return a new Series. If True the value of copy is ignored.
level (int 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 with index labels or name altered or None if
inplace=True
.- Return type:
Series or None
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({1: 3, 2: 5}) # mapping, changes labels 0 1 3 2 5 3 dtype: int64