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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 ExtensionArray

The unique values returned 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 also

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()
<DatetimeArray>
['2016-01-01 00:00:00-05:00']
Length: 1, dtype: datetime64[ns, US/Eastern]

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]
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