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# 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']