pandas.Series.unique  
Series.unique(self) [source]
 
Return unique values of Series object. Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort.    
Returns:

 ndarray or ExtensionArray 
The unique values returned as a NumPy array. See Notes.       See also  
unique
 Top-level unique method for any 1-d array-like object. 
Index.unique
 Return Index with unique values from an Index object.   Notes Returns the unique values as a NumPy array. In case of an extension-array backed Series, a new ExtensionArray of that type with just the unique values is returned. This includes  Categorical Period Datetime with Timezone Interval Sparse IntegerNA  See Examples section. 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()
<DatetimeArray>
['2016-01-01 00:00:00-05:00']
Length: 1, dtype: datetime64[ns, US/Eastern]
 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]
 


    
{'path': 'public/docs/pandas~0.25/reference/api/pandas.series.unique.html', 'source': 'DevDocs.IO'}