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- import numpy as np
- from pandas import (
- Categorical,
- IntervalIndex,
- Series,
- date_range,
- )
- import pandas._testing as tm
- class TestUnique:
- def test_unique_uint64(self):
- ser = Series([1, 2, 2**63, 2**63], dtype=np.uint64)
- res = ser.unique()
- exp = np.array([1, 2, 2**63], dtype=np.uint64)
- tm.assert_numpy_array_equal(res, exp)
- def test_unique_data_ownership(self):
- # it works! GH#1807
- Series(Series(["a", "c", "b"]).unique()).sort_values()
- def test_unique(self):
- # GH#714 also, dtype=float
- ser = Series([1.2345] * 100)
- ser[::2] = np.nan
- result = ser.unique()
- assert len(result) == 2
- # explicit f4 dtype
- ser = Series([1.2345] * 100, dtype="f4")
- ser[::2] = np.nan
- result = ser.unique()
- assert len(result) == 2
- def test_unique_nan_object_dtype(self):
- # NAs in object arrays GH#714
- ser = Series(["foo"] * 100, dtype="O")
- ser[::2] = np.nan
- result = ser.unique()
- assert len(result) == 2
- def test_unique_none(self):
- # decision about None
- ser = Series([1, 2, 3, None, None, None], dtype=object)
- result = ser.unique()
- expected = np.array([1, 2, 3, None], dtype=object)
- tm.assert_numpy_array_equal(result, expected)
- def test_unique_categorical(self):
- # GH#18051
- cat = Categorical([])
- ser = Series(cat)
- result = ser.unique()
- tm.assert_categorical_equal(result, cat)
- cat = Categorical([np.nan])
- ser = Series(cat)
- result = ser.unique()
- tm.assert_categorical_equal(result, cat)
- def test_tz_unique(self):
- # GH 46128
- dti1 = date_range("2016-01-01", periods=3)
- ii1 = IntervalIndex.from_breaks(dti1)
- ser1 = Series(ii1)
- uni1 = ser1.unique()
- tm.assert_interval_array_equal(ser1.array, uni1)
- dti2 = date_range("2016-01-01", periods=3, tz="US/Eastern")
- ii2 = IntervalIndex.from_breaks(dti2)
- ser2 = Series(ii2)
- uni2 = ser2.unique()
- tm.assert_interval_array_equal(ser2.array, uni2)
- assert uni1.dtype != uni2.dtype
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