pandas.unique#

pandas.unique(values)[source]#

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:
Index, ExtensionArray or numpy.ndarray

The return type depends on the type of the input:

  • Index : when the input is an Index, the result is an Index (or a subclass such as DatetimeIndex).

  • ExtensionArray : when the input has an ExtensionDtype (for example Categorical, tz-aware datetime64, IntervalArray, a masked integer/boolean/float array, an Arrow-backed array, or NumpyExtensionArray), the result is an ExtensionArray of the same dtype.

  • numpy.ndarray : for any other input (a NumPy-dtype Series, a numpy.ndarray, or any other 1-D array-like), the result is a numpy.ndarray.

See also

Index.unique

Return unique values from an Index.

Series.unique

Return unique values of Series object.

Notes

When working with object-dtype arrays, boolean and integer values may not be distinguished since True == 1 and False == 0 in Python.

>>> pd.unique(np.array([True, 1, False, 0], dtype=object))
array([True, False], dtype=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(['2016-01-01T00:00:00.000000'], dtype='datetime64[us]')

>>> 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, str): ['a', 'b', 'c']

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

A NumpyExtensionArray of complex

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