DOC: update the pandas.Index.drop_duplicates and pandas.Series.drop_d…

…uplicates docstring (pandas-dev#20114)

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/groupby.py

@@ -1475,7 +1475,8 @@ def describe(self, **kwargs):
     @Substitution(name='groupby')
     @Appender(_doc_template)
     def resample(self, rule, *args, **kwargs):
-        """Provide resampling when using a TimeGrouper.
+        """
+        Provide resampling when using a TimeGrouper.
 
         Given a grouper the function resamples it according to a string and an 
         optional list and dictionary of parameters. Returns a new grouper with
@@ -1501,7 +1502,6 @@ def resample(self, rule, *args, **kwargs):
 
         Examples
         --------
-
         Start by creating a DataFrame with 9 one minute timestamps.
 
         >>> idx = pd.date_range('1/1/2000', periods=9, freq='T')