DOC: update the pandas.Index.duplicated and pandas.Series.duplicated docstring

pandas/core/base.py

@@ -1197,24 +1197,6 @@ def drop_duplicates(self, keep='first', inplace=False):
         else:
             return result
 
-    _shared_docs['duplicated'] = (
-        """Return boolean %(duplicated)s denoting duplicate values
-
-        Parameters
-        ----------
-        keep : {'first', 'last', False}, default 'first'
-            - ``first`` : Mark duplicates as ``True`` except for the first
-              occurrence.
-            - ``last`` : Mark duplicates as ``True`` except for the last
-              occurrence.
-            - False : Mark all duplicates as ``True``.
-
-        Returns
-        -------
-        duplicated : %(duplicated)s
-        """)
-
-    @Appender(_shared_docs['duplicated'] % _indexops_doc_kwargs)
     def duplicated(self, keep='first'):
         from pandas.core.algorithms import duplicated
         if isinstance(self, ABCIndexClass):

pandas/core/indexes/base.py

@@ -4325,8 +4325,60 @@ def drop_duplicates(self, keep='first'):
         """
         return super(Index, self).drop_duplicates(keep=keep)
 
-    @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs)
     def duplicated(self, keep='first'):
+        """
+        Indicate duplicate index values.
+
+        Duplicated values are indicated as ``True`` values in the resulting
+        array. Either all duplicates, all except the first, or all except the
+        last occurrence of duplicates can be indicated.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            The value or values in a set of duplicates to mark as missing.
+
+            - 'first' : Mark duplicates as ``True`` except for the first
+              occurrence.
+            - 'last' : Mark duplicates as ``True`` except for the last
+              occurrence.
+            - ``False`` : Mark all duplicates as ``True``.
+
+        Examples
+        --------
+        By default, for each set of duplicated values, the first occurrence is
+        set to False and all others to True:
+
+        >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama'])
+        >>> idx.duplicated()
+        array([False, False,  True, False,  True])
+
+        which is equivalent to
+
+        >>> idx.duplicated(keep='first')
+        array([False, False,  True, False,  True])
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set on False and all others on True:
+
+        >>> idx.duplicated(keep='last')
+        array([ True, False,  True, False, False])
+
+        By setting keep on ``False``, all duplicates are True:
+
+        >>> idx.duplicated(keep=False)
+        array([ True, False,  True, False,  True])
+
+        Returns
+        -------
+        numpy.ndarray
+
+        See Also
+        --------
+        pandas.Series.duplicated : Equivalent method on pandas.Series
+        pandas.DataFrame.duplicated : Equivalent method on pandas.DataFrame
+        pandas.Index.drop_duplicates : Remove duplicate values from Index
+        """
         return super(Index, self).duplicated(keep=keep)
 
     _index_shared_docs['fillna'] = """

pandas/core/indexes/category.py

@@ -399,7 +399,7 @@ def unique(self, level=None):
         return self._shallow_copy(result, categories=result.categories,
                                   ordered=result.ordered)
 
-    @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs)
+    @Appender(Index.duplicated.__doc__)
     def duplicated(self, keep='first'):
         from pandas._libs.hashtable import duplicated_int64
         codes = self.codes.astype('i8')

pandas/core/indexes/multi.py

@@ -916,7 +916,7 @@ def f(k, stringify):
                      for k, stringify in zip(key, self._have_mixed_levels)])
         return hash_tuple(key)
 
-    @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs)
+    @Appender(Index.duplicated.__doc__)
     def duplicated(self, keep='first'):
         from pandas.core.sorting import get_group_index
         from pandas._libs.hashtable import duplicated_int64

pandas/core/series.py

@@ -1458,8 +1458,78 @@ def drop_duplicates(self, keep='first', inplace=False):
         """
         return super(Series, self).drop_duplicates(keep=keep, inplace=inplace)
 
-    @Appender(base._shared_docs['duplicated'] % _shared_doc_kwargs)
     def duplicated(self, keep='first'):
+        """
+        Indicate duplicate Series values.
+
+        Duplicated values are indicated as ``True`` values in the resulting
+        Series. Either all duplicates, all except the first or all except the
+        last occurrence of duplicates can be indicated.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            - 'first' : Mark duplicates as ``True`` except for the first
+              occurrence.
+            - 'last' : Mark duplicates as ``True`` except for the last
+              occurrence.
+            - ``False`` : Mark all duplicates as ``True``.
+
+        Examples
+        --------
+        By default, for each set of duplicated values, the first occurrence is
+        set on False and all others on True:
+
+        >>> animals = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama'])
+        >>> animals.duplicated()
+        0    False
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+
+        which is equivalent to
+
+        >>> animals.duplicated(keep='first')
+        0    False
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set on False and all others on True:
+
+        >>> animals.duplicated(keep='last')
+        0     True
+        1    False
+        2     True
+        3    False
+        4    False
+        dtype: bool
+
+        By setting keep on ``False``, all duplicates are True:
+
+        >>> animals.duplicated(keep=False)
+        0     True
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+
+        Returns
+        -------
+        pandas.core.series.Series
+
+        See Also
+        --------
+        pandas.Index.duplicated : Equivalent method on pandas.Index
+        pandas.DataFrame.duplicated : Equivalent method on pandas.DataFrame
+        pandas.Series.drop_duplicates : Remove duplicate values from Series
+        """
         return super(Series, self).duplicated(keep=keep)
 
     def idxmin(self, axis=None, skipna=True, *args, **kwargs):