DOC: docstring to series.unique (#20474)

Author: minggli

pandas/core/base.py

@@ -1021,30 +1021,6 @@ 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 +1429,51 @@ 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
+ -------
+ ndarray or Categorical
+ The unique values returned as a NumPy array. In case of categorical
+ data type, returned as a Categorical.
+
+ See Also
+ --------
+ pandas.unique : top-level unique method for any 1-d array-like object.
+ 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([pd.Timestamp('2016-01-01') for _ in range(3)]).unique()
+ array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
+
+ >>> pd.Series([pd.Timestamp('2016-01-01', 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):