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]