pandas.Series.unique#

Series.unique()[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

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 anextension-array backed Series, a newExtensionArray of that type with justthe 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_inrange(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_inrange(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 ofappearance 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']