DOC: Improve the docstrings of CategoricalIndex.map

pandas/core/arrays/categorical.py

@@ -1080,20 +1080,63 @@ def remove_unused_categories(self, inplace=False):
             return cat
 
     def map(self, mapper):
-        """Apply mapper function to its categories (not codes).
+        """
+        Map categories using input correspondence (dict, Series, or function).
+
+        Maps the categories to new categories. If the mapping correspondence is
+        a bijection (maps each original category to a different new category)
+        the result is a :class:`~pandas.Categorical` which has the same order
+        property as the original, otherwise a :class:`~pandas.Index` is
+        returned.
+
+        If a `dict` or :class:`~pandas.Series` is used any unmapped category is
+        mapped to NaN. Note that if this happens an :class:`~pandas.Index` will
+        be returned.
 
         Parameters
         ----------
-        mapper : callable
-            Function to be applied. When all categories are mapped
-            to different categories, the result will be Categorical which has
-            the same order property as the original. Otherwise, the result will
-            be np.ndarray.
+        mapper : function, dict, or Series
+            Mapping correspondence.
 
         Returns
         -------
-        applied : Categorical or Index.
+        pandas.Categorical or pandas.Index
+            Mapped categorical.
+
+        See Also
+        --------
+        CategoricalIndex.map : Apply a mapping correspondence on a
+            :class:`~pandas.CategoricalIndex`.
+        Index.map : Apply a mapping correspondence on an
+            :class:`~pandas.Index`.
+        Series.map : Apply a mapping correspondence on a
+            :class:`~pandas.Series`.
+        Series.apply : Apply more complex functions on a
+            :class:`~pandas.Series`.
+
+        Examples
+        --------
+        >>> cat = pd.Categorical(['a', 'b', 'c'])
+        >>> cat
+        [a, b, c]
+        Categories (3, object): [a, b, c]
+        >>> cat.map(lambda x: x.upper())
+        [A, B, C]
+        Categories (3, object): [A, B, C]
+        >>> cat.map({'a': 'first', 'b': 'second', 'c': 'third'})
+        [first, second, third]
+        Categories (3, object): [first, second, third]
+
+        If the mapping is not bijective an :class:`~pandas.Index` is returned:
+
+        >>> cat.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 :class:`~pandas.Index`:
 
+        >>> cat.map({'a': 'first', 'b': 'second'})
+        Index(['first', 'second', nan], dtype='object')
         """
         new_categories = self.categories.map(mapper)
         try:

pandas/core/indexes/base.py

@@ -3352,22 +3352,23 @@ def groupby(self, values):
         return result
 
     def map(self, mapper, na_action=None):
-        """Map values of Series using input correspondence
+        """
+        Map values using input correspondence (a dict, Series, or function).
 
         Parameters
         ----------
         mapper : function, dict, or Series
+            Mapping correspondence.
         na_action : {None, 'ignore'}
             If 'ignore', propagate NA values, without passing them to the
-            mapping function
+            mapping correspondence.
 
         Returns
         -------
         applied : Union[Index, MultiIndex], inferred
             The output of the mapping function applied to the index.
             If the function returns a tuple with more than one element
             a MultiIndex will be returned.
-
         """
 
         from .multi import MultiIndex

pandas/core/indexes/category.py

@@ -660,20 +660,61 @@ def is_dtype_equal(self, other):
     take_nd = take
 
     def map(self, mapper):
-        """Apply mapper function to its categories (not codes).
+        """
+        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 a bijection (maps each
+        original category to a different new category) the result is a
+        :class:`~pandas.CategoricalIndex` which has the same order property as
+        the original, otherwise an :class:`~pandas.Index` is returned.
+
+        If a `dict` or :class:`~pandas.Series` is used any unmapped category is
+        mapped to NaN. Note that if this happens an :class:`~pandas.Index` will
+        be returned.
 
         Parameters
         ----------
-        mapper : callable
-            Function to be applied. When all categories are mapped
-            to different categories, the result will be a CategoricalIndex
-            which has the same order property as the original. Otherwise,
-            the result will be a Index.
+        mapper : function, dict, or Series
+            Mapping correspondence.
 
         Returns
         -------
-        applied : CategoricalIndex or Index
+        pandas.CategoricalIndex or pandas.Index
+            Mapped index.
+
+        See Also
+        --------
+        Index.map : Apply a mapping correspondence on an
+            :class:`~pandas.Index`.
+        Series.map : Apply a mapping correspondence on a
+            :class:`~pandas.Series`.
+        Series.apply : Apply more complex functions on a
+            :class:`~pandas.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 not bijective an :class:`~pandas.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 :class:`~pandas.Index`:
+
+        >>> idx.map({'a': 'first', 'b': 'second'})
+        Index(['first', 'second', nan], dtype='object')
         """
         return self._shallow_copy_with_infer(self.values.map(mapper))
 

pandas/core/series.py

@@ -2831,25 +2831,26 @@ def unstack(self, level=-1, fill_value=None):
 
     def map(self, arg, na_action=None):
         """
-        Map values of Series using input correspondence (which can be
-        a dict, Series, or function)
+        Map values of Series using input correspondence (a dict, Series, or
+        function).
 
         Parameters
         ----------
         arg : function, dict, or Series
+            Mapping correspondence.
         na_action : {None, 'ignore'}
             If 'ignore', propagate NA values, without passing them to the
-            mapping function
+            mapping correspondence.
 
         Returns
         -------
         y : Series
-            same index as caller
+            Same index as caller.
 
         Examples
         --------
 
-        Map inputs to outputs (both of type `Series`)
+        Map inputs to outputs (both of type `Series`):
 
         >>> x = pd.Series([1,2,3], index=['one', 'two', 'three'])
         >>> x
@@ -2900,9 +2901,9 @@ def map(self, arg, na_action=None):
 
         See Also
         --------
-        Series.apply: For applying more complex functions on a Series
-        DataFrame.apply: Apply a function row-/column-wise
-        DataFrame.applymap: Apply a function elementwise on a whole DataFrame
+        Series.apply : For applying more complex functions on a Series.
+        DataFrame.apply : Apply a function row-/column-wise.
+        DataFrame.applymap : Apply a function elementwise on a whole DataFrame.
 
         Notes
         -----