pandas.Series.unique#

Series.unique()[source]#

Return unique values of Series object.

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

Returns:
ndarray or ExtensionArray

The unique values returned as a NumPy array. See Notes.

See also

Series.drop_duplicates

Return Series with duplicate values removed.

unique

Top-level unique method for any 1-d array-like object.

Index.unique

Return Index with unique values from an Index object.

Notes

Returns the unique values as a NumPy array. In case of an extension-array backed Series, a new ExtensionArray of that type with just the unique values is returned. This includes

  • Categorical

  • Period

  • Datetime with Timezone

  • Datetime without Timezone

  • Timedelta

  • Interval

  • Sparse

  • IntegerNA

See Examples section.

Examples

>>> pd.Series([2, 1, 3, 3], name="A").unique()
array([2, 1, 3])

>>> pd.Series([pd.Timestamp("2016-01-01") for _ in range(3)]).unique()
<DatetimeArray>
['2016-01-01 00:00:00']
Length: 1, dtype: datetime64[s]

>>> pd.Series(
...     [pd.Timestamp("2016-01-01", tz="US/Eastern") for _ in range(3)]
... ).unique()
<DatetimeArray>
['2016-01-01 00:00:00-05:00']
Length: 1, dtype: datetime64[s, US/Eastern]

An Categorical will return categories in the order of appearance and with the same dtype.

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