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