# pandas.unique

pandas.unique(values)

  Hash table-based unique. Uniques are returned in order of appearance. This does NOT sort.

  Significantly faster than numpy.unique for long enough sequences. Includes NA values.

  Parameters
    values 1d array-like
  Returns
    numpy.ndarray or ExtensionArray

      The return can be:

      - Index : when the input is an Index

      - Categorical : when the input is a Categorical dtype

      - ndarray : when the input is a Series/ndarray

      Return numpy.ndarray or ExtensionArray.

  See also

  `Index.unique`

    Return unique values from an Index.

  `Series.unique`

    Return unique values of Series object.

  Examples

      >>> pd.unique(pd.Series([2, 1, 3, 3]))
      array([2, 1, 3])

      >>> pd.unique(pd.Series([2] + [1] * 5))
      array([2, 1])

      >>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
      array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

      >>> pd.unique(
      ...     pd.Series(
      ...         [
      ...             pd.Timestamp("20160101", tz="US/Eastern"),
      ...             pd.Timestamp("20160101", tz="US/Eastern"),
      ...         ]
      ...     )
      ... )
      <DatetimeArray>
      ['2016-01-01 00:00:00-05:00']
      Length: 1, dtype: datetime64[ns, US/Eastern]

      >>> pd.unique(
      ...     pd.Index(
      ...         [
      ...             pd.Timestamp("20160101", tz="US/Eastern"),
      ...             pd.Timestamp("20160101", tz="US/Eastern"),
      ...         ]
      ...     )
      ... )
      DatetimeIndex(['2016-01-01 00:00:00-05:00'],
              dtype='datetime64[ns, US/Eastern]',
              freq=None)

      >>> pd.unique(list("baabc"))
      array(['b', 'a', 'c'], dtype=object)

  An unordered Categorical will return categories in the order of appearance.

      >>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
      ['b', 'a', 'c']
      Categories (3, object): ['a', 'b', 'c']

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

  An ordered Categorical preserves the category ordering.

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

  An array of tuples

      >>> pd.unique([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")])
      array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)