pandas.CategoricalIndex.map

CategoricalIndex.map(self, 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

mapperfunction, 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')

pandas.CategoricalIndex.as_unordered pandas.CategoricalIndex.equals