Python series unique values

pandas.Series.unique#

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.

Return Series with duplicate values removed.

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

Return Index with unique values from an Index object.

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

>>> 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() ['2016-01-01 00:00:00'] Length: 1, dtype: datetime64[ns] 

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

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.

Return Series with duplicate values removed.

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

Return Index with unique values from an Index object.

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

>>> 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() ['2016-01-01 00:00:00'] Length: 1, dtype: datetime64[ns] 

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

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

• 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.

Return unique values from an Index.

Return unique values of Series object.

>>> 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"), . ] . ) . ) ['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(list("baabc")) 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']

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

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