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Series.unique() ⚬｜Documentation｜1st｜20251021154722-00-⌔

pandas.Series.unique — pandas 2.3.3 documentation#pandas.Series.unique

Series.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:
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[us]
>>> 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[us, US/Eastern]

A 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, str): ['a', 'b', 'c']
>>> pd.Series(
...     pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
... ).unique()
['b', 'a', 'c']
Categories (3, str): ['a' < 'b' < 'c']

Printed 2026-06-28.

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