# pandas.CategoricalIndex.map

`CategoricalIndex.``map`(mapper)

  Map values using input correspondence (a dict, Series, or function).

  Maps the values (their categories, not the codes) of the index to new categories. If the mapping correspondence is one-to-one the result is a `CategoricalIndex` which has the same order property as the original, otherwise an `Index` is returned.

  If a dict or `Series` is used any unmapped category is mapped to NaN. Note that if this happens an `Index` will be returned.

  Parameters:

  mapper : function, dict, or Series

  > Mapping correspondence.

  Returns:

  pandas.CategoricalIndex or pandas.Index

  > Mapped index.

  See also

  `Index.map`
    Apply a mapping correspondence on an `Index`.
  `Series.map`
    Apply a mapping correspondence on a `Series`.
  `Series.apply`
    Apply more complex functions on a `Series`.

  Examples

      >>> idx = pd.CategoricalIndex(['a', 'b', 'c'])
      >>> idx
      CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
                       ordered=False, dtype='category')
      >>> idx.map(lambda x: x.upper())
      CategoricalIndex(['A', 'B', 'C'], categories=['A', 'B', 'C'],
                       ordered=False, dtype='category')
      >>> idx.map({'a': 'first', 'b': 'second', 'c': 'third'})
      CategoricalIndex(['first', 'second', 'third'], categories=['first',
                       'second', 'third'], ordered=False, dtype='category')

  If the mapping is one-to-one the ordering of the categories is preserved:

      >>> idx = pd.CategoricalIndex(['a', 'b', 'c'], ordered=True)
      >>> idx
      CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],
                       ordered=True, dtype='category')
      >>> idx.map({'a': 3, 'b': 2, 'c': 1})
      CategoricalIndex([3, 2, 1], categories=[3, 2, 1], ordered=True,
                       dtype='category')

  If the mapping is not one-to-one an `Index` is returned:

      >>> idx.map({'a': 'first', 'b': 'second', 'c': 'first'})
      Index(['first', 'second', 'first'], dtype='object')

  If a dict is used, all unmapped categories are mapped to NaN and the result is an `Index`:

      >>> idx.map({'a': 'first', 'b': 'second'})
      Index(['first', 'second', nan], dtype='object')

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