How to drop extra copy of duplicate index of Pandas Series?

Question

I have a Series s with duplicate index :

>>> s
STK_ID  RPT_Date
600809  20061231    demo_str
        20070331    demo_str
        20070630    demo_str
        20070930    demo_str
        20071231    demo_str
        20060331    demo_str
        20060630    demo_str
        20060930    demo_str
        20061231    demo_str
        20070331    demo_str
        20070630    demo_str
Name: STK_Name, Length: 11

And I just want to keep the unique rows and only one copy of the duplicate rows by:

s[s.index.unique()]

Pandas 0.10.1.dev-f7f7e13 give the below error msg

>>> s[s.index.unique()]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "d:\Python27\lib\site-packages\pandas\core\series.py", line 515, in __getitem__
    return self._get_with(key)
  File "d:\Python27\lib\site-packages\pandas\core\series.py", line 558, in _get_with
    return self.reindex(key)
  File "d:\Python27\lib\site-packages\pandas\core\series.py", line 2361, in reindex
    level=level, limit=limit)
  File "d:\Python27\lib\site-packages\pandas\core\index.py", line 2063, in reindex
    limit=limit)
  File "d:\Python27\lib\site-packages\pandas\core\index.py", line 2021, in get_indexer
    raise Exception('Reindexing only valid with uniquely valued Index '
Exception: Reindexing only valid with uniquely valued Index objects
>>> 

So how to drop extra duplicate rows of series, keep the unique rows and only one copy of the duplicate rows in an efficient way ? (better in one line)

Answer 1

You can groupby the index and apply a function that returns one value per index group. Here, I take the first value:

In [1]: s = Series(range(10), index=[1,2,2,2,5,6,7,7,7,8])

In [2]: s
Out[2]:
1    0
2    1
2    2
2    3
5    4
6    5
7    6
7    7
7    8
8    9

In [3]: s.groupby(s.index).first()
Out[3]:
1    0
2    1
5    4
6    5
7    6
8    9

UPDATE

Addressing BigBug's comment about crashing when passing a MultiIndex to Series.groupby():

In [1]: s
Out[1]:
STK_ID  RPT_Date
600809  20061231    demo
        20070331    demo
        20070630    demo
        20070331    demo

In [2]: s.reset_index().groupby(s.index.names).first()
Out[2]:
                    0
STK_ID RPT_Date
600809 20061231  demo
       20070331  demo
       20070630  demo

Answer 2

You could subset your data with duplicated (which keeps first value by default) for index. With @Zelazny7 example:

s = pd.Series(range(10), index=[1,2,2,2,5,6,7,7,7,8])

In [130]: s[~s.index.duplicated()]
Out[130]: 
1    0
2    1
5    4
6    5
7    6
8    9
dtype: int64