pandas.unique

pandas.unique(values)

Return unique values based on a hash table.

Uniques are returned in order of appearance. This does NOT sort.

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

Parameters:

values1d array-like

The input array-like object containing values from which to extract unique values.

Returns:

numpy.ndarray, ExtensionArray or NumpyExtensionArray

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, ExtensionArray or NumpyExtensionArray.

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'], dtype='datetime64[s]')

>>> pd.unique(
...     pd.Series(
...         [
...             pd.Timestamp("20160101", tz="US/Eastern"),
...             pd.Timestamp("20160101", tz="US/Eastern"),
...         ],
...         dtype="M8[ns, 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"),
...         ],
...         dtype="M8[ns, US/Eastern]",
...     )
... )
DatetimeIndex(['2016-01-01 00:00:00-05:00'],
        dtype='datetime64[ns, US/Eastern]',
        freq=None)

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

An NumpyExtensionArray of complex

>>> pd.unique(pd.array([1 + 1j, 2, 3]))
<NumpyExtensionArray>
[(1+1j), (2+0j), (3+0j)]
Length: 3, dtype: complex128