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

pandas/core/base.py

@@ -1184,24 +1184,6 @@ def searchsorted(self, value, side='left', sorter=None):
         # needs coercion on the key (DatetimeIndex does already)
         return self.values.searchsorted(value, side=side, sorter=sorter)
 
-    _shared_docs['drop_duplicates'] = (
-        """Return %(klass)s with duplicate values removed
-
-        Parameters
-        ----------
-
-        keep : {'first', 'last', False}, default 'first'
-            - ``first`` : Drop duplicates except for the first occurrence.
-            - ``last`` : Drop duplicates except for the last occurrence.
-            - False : Drop all duplicates.
-        %(inplace)s
-
-        Returns
-        -------
-        deduplicated : %(klass)s
-        """)
-
-    @Appender(_shared_docs['drop_duplicates'] % _indexops_doc_kwargs)
     def drop_duplicates(self, keep='first', inplace=False):
         inplace = validate_bool_kwarg(inplace, 'inplace')
         if isinstance(self, ABCIndexClass):

pandas/core/indexes/base.py

@@ -4017,8 +4017,52 @@ def unique(self, level=None):
         result = super(Index, self).unique()
         return self._shallow_copy(result)
 
-    @Appender(base._shared_docs['drop_duplicates'] % _index_doc_kwargs)
     def drop_duplicates(self, keep='first'):
+        """
+        Return Index with duplicate values removed.
+
+        The drop_duplicates method can remove occurences or whole sets
+        of duplicated entries in a pandas.Index object.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', ``False``}, default 'first'
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+
+        Returns
+        -------
+        deduplicated : Index
+
+        See Also
+        --------
+        pandas.Series.drop_duplicates : equivalent method on pandas.Series
+
+        Examples
+        --------
+        Generate an pandas.Index with duplicate values.
+
+        >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])
+
+        With the 'keep' parameter, the selection behaviour of duplicated values
+        can be changed. The value 'first' keeps the first occurrence for each
+        set of duplicated entries. The default value of keep is 'first'.
+
+        >>> idx.drop_duplicates(keep='first')
+        Index(['lama', 'cow', 'beetle', 'hippo'], dtype='object')
+
+        The value 'last' keeps the last occurrence for each set of duplicated
+        entries.
+
+        >>> idx.drop_duplicates(keep='last')
+        Index(['cow', 'beetle', 'lama', 'hippo'], dtype='object')
+
+        The value ``False`` discards all sets of duplicated entries.
+
+        >>> idx.drop_duplicates(keep=False)
+        Index(['cow', 'beetle', 'hippo'], dtype='object')
+        """
         return super(Index, self).drop_duplicates(keep=keep)
 
     @Appender(base._shared_docs['duplicated'] % _index_doc_kwargs)

pandas/core/series.py

@@ -1316,8 +1316,77 @@ def unique(self):
 
         return result
 
-    @Appender(base._shared_docs['drop_duplicates'] % _shared_doc_kwargs)
     def drop_duplicates(self, keep='first', inplace=False):
+        """
+        Return Series with duplicate values removed.
+
+        The drop_duplicates method can remove occurences or whole sets
+        of duplicated entries in a pandas.Series object.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', ``False``}, default 'first'
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+        inplace : boolean, default ``False``
+            If ``True``, performs operation inplace and returns None.
+
+        Returns
+        -------
+        deduplicated : Series
+
+        See Also
+        --------
+        pandas.Index.drop_duplicates : equivalent method on pandas.Index
+
+        Examples
+        --------
+        Generate an Series with duplicated entries.
+
+        >>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'],
+        ...               name='animal')
+        >>> s
+        0      lama
+        1       cow
+        2      lama
+        3    beetle
+        4      lama
+        5     hippo
+        Name: animal, dtype: object
+
+        With the 'keep' parameter, the selection behaviour of duplicated values
+        can be changed. The value 'first' keeps the first occurrence for each
+        set of duplicated entries. The default value of keep is 'first'.
+
+        >>> s.drop_duplicates()
+        0      lama
+        1       cow
+        3    beetle
+        5     hippo
+        Name: animal, dtype: object
+
+        The value 'last' for parameter 'keep' keeps the last occurrence for
+        each set of duplicated entries.
+
+        >>> s.drop_duplicates(keep='last')
+        1       cow
+        3    beetle
+        4      lama
+        5     hippo
+        Name: animal, dtype: object
+
+        The value ``False`` for parameter 'keep' discards all sets of
+        duplicated entries. Setting the value of 'inplace' to ``True`` performs
+        the operation inplace and returns ``None``.
+
+        >>> s.drop_duplicates(keep=False, inplace=True)
+        >>> s
+        1       cow
+        3    beetle
+        5     hippo
+        Name: animal, dtype: object
+        """
         return super(Series, self).drop_duplicates(keep=keep, inplace=inplace)
 
     @Appender(base._shared_docs['duplicated'] % _shared_doc_kwargs)