pandas.unique |
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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 Returns: numpy.ndarray or ExtensionArrayThe 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.uniqueReturn unique values from an Index. Series.uniqueReturn 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"), ... ] ... ) ... ) ['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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