pandas.core.groupby.SeriesGroupBy.unique#

property SeriesGroupBy.unique#

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 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']

pandas.core.groupby.SeriesGroupBy.nunique

pandas.core.groupby.SeriesGroupBy.value_counts