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Module « pandas »

Fonction unique - module pandas

Signature de la fonction unique

def unique(values) 

Description

help(pandas.unique)

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
----------
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(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)

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