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# 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