Return unique values of Series object.
Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort.
The unique values returned as a NumPy array. See Notes.
See also
Series.drop_duplicates
Return Series with duplicate values removed.
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
Datetime without Timezone
Timedelta
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()
<DatetimeArray>
['2016-01-01 00:00:00']
Length: 1, 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 Categorical will return categories in the order of appearance and with the same dtype.
>>> pd.Series(pd.Categorical(list('baabc'))).unique()
['b', 'a', 'c']
Categories (3, object): ['a', 'b', 'c']
>>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'),
... ordered=True)).unique()
['b', 'a', 'c']
Categories (3, object): ['a' < 'b' < 'c']
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