unique(values)

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:

Return unique values based on a hash table.

See Also

Index.unique

Return unique values from an Index.

Series.unique

Return unique values of Series object.

Examples

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

>>> 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]

This example is valid syntax, but we were not able to check execution

>>> 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)

This example is valid syntax, but we were not able to check execution

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

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

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

>>> 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.

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.arrays.categorical.Categorical.unique

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File: /pandas/core/algorithms.py#336
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