xorbits.pandas.Series.unique#

Series.unique(method='tree')#

Return unique values of Series object.

Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort.

Returns

The unique values returned as a NumPy array. See Notes.

Return type

ndarray or ExtensionArray

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']

values : 1d array-like method : ‘shuffle’ or ‘tree’, ‘tree’ method provide a better performance, ‘shuffle’

This docstring was copied from pandas.core.series.Series.