DOC: docstring to series.unique

pandas/core/base.py

@@ -1020,30 +1020,6 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
                               normalize=normalize, bins=bins, dropna=dropna)
         return result
 
-    _shared_docs['unique'] = (
-        """
-        Return unique values in the object. Uniques are returned in order
-        of appearance, this does NOT sort. Hash table-based unique.
-
-        Parameters
-        ----------
-        values : 1d array-like
-
-        Returns
-        -------
-        unique values.
-          - If the input is an Index, the return is an Index
-          - If the input is a Categorical dtype, the return is a Categorical
-          - If the input is a Series/ndarray, the return will be an ndarray
-
-        See Also
-        --------
-        unique
-        Index.unique
-        Series.unique
-        """)
-
-    @Appender(_shared_docs['unique'] % _indexops_doc_kwargs)
     def unique(self):
         values = self._values
 

pandas/core/series.py

@@ -1429,8 +1429,55 @@ def mode(self):
         # TODO: Add option for bins like value_counts()
         return algorithms.mode(self)
 
-    @Appender(base._shared_docs['unique'] % _shared_doc_kwargs)
     def unique(self):
+        """
+        Return unique values of Series object.
+
+        Uniques are returned in order of appearance. Hash table-based unique,
+        therefore does NOT sort.
+
+        Returns
+        -------
+        unique values.
+          - If the input is an Index, the return is an Index
+          - If the input is a Categorical dtype, the return is a Categorical
+          - If the input is a Series/ndarray, the return will be an ndarray
+
+        See Also
+        --------
+        unique : return unique values of 1d array-like objects.
+        Index.unique : return Index with unique values from an Index object.
+
+        Examples
+        --------
+        >>> pd.Series([2, 1, 3, 3], name='A').unique()
+        array([2, 1, 3])
+
+        >>> pd.Series([2] + [1] * 5).unique()
+        array([2, 1])
+
+        >>> pd.Series([pd.Timestamp('20160101') for _ in range(3)]).unique()
+        array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
+
+        >>> pd.Series([pd.Timestamp('20160101', tz='US/Eastern') \
+                       for _ in range(3)]).unique()
+        array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')],
+              dtype=object)
+
+        An unordered Categorical will return categories in the order of
+        appearance.
+
+        >>> pd.Series(pd.Categorical(list('baabc'))).unique()
+        [b, a, c]
+        Categories (3, object): [b, a, c]
+
+        An ordered Categorical preserves the category ordering.
+
+        >>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'), \
+                                     ordered=True)).unique()
+        [b, a, c]
+        Categories (3, object): [a < b < c]
+        """
         result = super(Series, self).unique()
 
         if is_datetime64tz_dtype(self.dtype):