pandas.core.groupby.SeriesGroupBy.unique

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 Categorical

The unique values returned as a NumPy array. In case of categorical data type, returned as a Categorical.

See also

pandas.unique

top-level unique method for any 1-d array-like object.

Index.unique

return Index with unique values from an Index object.

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()
array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')],
      dtype=object)

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]