309 lines
9.5 KiB
Python
309 lines
9.5 KiB
Python
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import numpy as np
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import pytest
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from pandas import (
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NaT,
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Timestamp,
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isna,
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)
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from pandas.core.arrays.sparse import (
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SparseArray,
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SparseDtype,
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)
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class TestReductions:
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@pytest.mark.parametrize(
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"data,pos,neg",
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[
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([True, True, True], True, False),
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([1, 2, 1], 1, 0),
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([1.0, 2.0, 1.0], 1.0, 0.0),
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],
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)
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def test_all(self, data, pos, neg):
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# GH#17570
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out = SparseArray(data).all()
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assert out
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out = SparseArray(data, fill_value=pos).all()
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assert out
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data[1] = neg
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out = SparseArray(data).all()
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assert not out
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out = SparseArray(data, fill_value=pos).all()
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assert not out
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@pytest.mark.parametrize(
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"data,pos,neg",
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[
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([True, True, True], True, False),
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([1, 2, 1], 1, 0),
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([1.0, 2.0, 1.0], 1.0, 0.0),
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],
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)
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def test_numpy_all(self, data, pos, neg):
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# GH#17570
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out = np.all(SparseArray(data))
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assert out
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out = np.all(SparseArray(data, fill_value=pos))
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assert out
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data[1] = neg
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out = np.all(SparseArray(data))
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assert not out
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out = np.all(SparseArray(data, fill_value=pos))
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assert not out
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# raises with a different message on py2.
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msg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.all(SparseArray(data), out=np.array([]))
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@pytest.mark.parametrize(
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"data,pos,neg",
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[
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([False, True, False], True, False),
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([0, 2, 0], 2, 0),
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([0.0, 2.0, 0.0], 2.0, 0.0),
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],
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)
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def test_any(self, data, pos, neg):
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# GH#17570
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out = SparseArray(data).any()
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assert out
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out = SparseArray(data, fill_value=pos).any()
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assert out
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data[1] = neg
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out = SparseArray(data).any()
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assert not out
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out = SparseArray(data, fill_value=pos).any()
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assert not out
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@pytest.mark.parametrize(
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"data,pos,neg",
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[
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([False, True, False], True, False),
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([0, 2, 0], 2, 0),
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([0.0, 2.0, 0.0], 2.0, 0.0),
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],
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)
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def test_numpy_any(self, data, pos, neg):
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# GH#17570
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out = np.any(SparseArray(data))
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assert out
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out = np.any(SparseArray(data, fill_value=pos))
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assert out
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data[1] = neg
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out = np.any(SparseArray(data))
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assert not out
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out = np.any(SparseArray(data, fill_value=pos))
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assert not out
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msg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.any(SparseArray(data), out=out)
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def test_sum(self):
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data = np.arange(10).astype(float)
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out = SparseArray(data).sum()
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assert out == 45.0
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data[5] = np.nan
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out = SparseArray(data, fill_value=2).sum()
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assert out == 40.0
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out = SparseArray(data, fill_value=np.nan).sum()
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assert out == 40.0
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@pytest.mark.parametrize(
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"arr",
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[np.array([0, 1, np.nan, 1]), np.array([0, 1, 1])],
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)
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@pytest.mark.parametrize("fill_value", [0, 1, np.nan])
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@pytest.mark.parametrize("min_count, expected", [(3, 2), (4, np.nan)])
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def test_sum_min_count(self, arr, fill_value, min_count, expected):
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# GH#25777
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sparray = SparseArray(arr, fill_value=fill_value)
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result = sparray.sum(min_count=min_count)
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if np.isnan(expected):
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assert np.isnan(result)
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else:
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assert result == expected
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def test_bool_sum_min_count(self):
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spar_bool = SparseArray([False, True] * 5, dtype=np.bool8, fill_value=True)
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res = spar_bool.sum(min_count=1)
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assert res == 5
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res = spar_bool.sum(min_count=11)
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assert isna(res)
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def test_numpy_sum(self):
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data = np.arange(10).astype(float)
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out = np.sum(SparseArray(data))
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assert out == 45.0
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data[5] = np.nan
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out = np.sum(SparseArray(data, fill_value=2))
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assert out == 40.0
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out = np.sum(SparseArray(data, fill_value=np.nan))
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assert out == 40.0
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msg = "the 'dtype' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.sum(SparseArray(data), dtype=np.int64)
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msg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.sum(SparseArray(data), out=out)
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def test_mean(self):
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data = np.arange(10).astype(float)
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out = SparseArray(data).mean()
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assert out == 4.5
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data[5] = np.nan
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out = SparseArray(data).mean()
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assert out == 40.0 / 9
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def test_numpy_mean(self):
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data = np.arange(10).astype(float)
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out = np.mean(SparseArray(data))
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assert out == 4.5
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data[5] = np.nan
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out = np.mean(SparseArray(data))
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assert out == 40.0 / 9
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msg = "the 'dtype' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.mean(SparseArray(data), dtype=np.int64)
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msg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.mean(SparseArray(data), out=out)
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class TestMinMax:
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@pytest.mark.parametrize(
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"raw_data,max_expected,min_expected",
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[
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(np.arange(5.0), [4], [0]),
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(-np.arange(5.0), [0], [-4]),
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(np.array([0, 1, 2, np.nan, 4]), [4], [0]),
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(np.array([np.nan] * 5), [np.nan], [np.nan]),
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(np.array([]), [np.nan], [np.nan]),
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],
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)
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def test_nan_fill_value(self, raw_data, max_expected, min_expected):
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arr = SparseArray(raw_data)
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max_result = arr.max()
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min_result = arr.min()
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assert max_result in max_expected
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assert min_result in min_expected
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max_result = arr.max(skipna=False)
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min_result = arr.min(skipna=False)
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if np.isnan(raw_data).any():
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assert np.isnan(max_result)
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assert np.isnan(min_result)
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else:
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assert max_result in max_expected
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assert min_result in min_expected
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@pytest.mark.parametrize(
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"fill_value,max_expected,min_expected",
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[
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(100, 100, 0),
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(-100, 1, -100),
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],
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)
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def test_fill_value(self, fill_value, max_expected, min_expected):
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arr = SparseArray(
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np.array([fill_value, 0, 1]), dtype=SparseDtype("int", fill_value)
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)
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max_result = arr.max()
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assert max_result == max_expected
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min_result = arr.min()
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assert min_result == min_expected
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def test_only_fill_value(self):
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fv = 100
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arr = SparseArray(np.array([fv, fv, fv]), dtype=SparseDtype("int", fv))
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assert len(arr._valid_sp_values) == 0
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assert arr.max() == fv
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assert arr.min() == fv
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assert arr.max(skipna=False) == fv
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assert arr.min(skipna=False) == fv
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@pytest.mark.parametrize("func", ["min", "max"])
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@pytest.mark.parametrize("data", [np.array([]), np.array([np.nan, np.nan])])
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@pytest.mark.parametrize(
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"dtype,expected",
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[
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(SparseDtype(np.float64, np.nan), np.nan),
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(SparseDtype(np.float64, 5.0), np.nan),
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(SparseDtype("datetime64[ns]", NaT), NaT),
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(SparseDtype("datetime64[ns]", Timestamp("2018-05-05")), NaT),
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],
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)
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def test_na_value_if_no_valid_values(self, func, data, dtype, expected):
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arr = SparseArray(data, dtype=dtype)
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result = getattr(arr, func)()
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if expected is NaT:
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# TODO: pin down whether we wrap datetime64("NaT")
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assert result is NaT or np.isnat(result)
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else:
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assert np.isnan(result)
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class TestArgmaxArgmin:
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@pytest.mark.parametrize(
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"arr,argmax_expected,argmin_expected",
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[
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(SparseArray([1, 2, 0, 1, 2]), 1, 2),
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(SparseArray([-1, -2, 0, -1, -2]), 2, 1),
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(SparseArray([np.nan, 1, 0, 0, np.nan, -1]), 1, 5),
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(SparseArray([np.nan, 1, 0, 0, np.nan, 2]), 5, 2),
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(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=-1), 5, 2),
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(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=0), 5, 2),
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(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=1), 5, 2),
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(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=2), 5, 2),
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(SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=3), 5, 2),
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(SparseArray([0] * 10 + [-1], fill_value=0), 0, 10),
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(SparseArray([0] * 10 + [-1], fill_value=-1), 0, 10),
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(SparseArray([0] * 10 + [-1], fill_value=1), 0, 10),
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(SparseArray([-1] + [0] * 10, fill_value=0), 1, 0),
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(SparseArray([1] + [0] * 10, fill_value=0), 0, 1),
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(SparseArray([-1] + [0] * 10, fill_value=-1), 1, 0),
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(SparseArray([1] + [0] * 10, fill_value=1), 0, 1),
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],
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)
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def test_argmax_argmin(self, arr, argmax_expected, argmin_expected):
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argmax_result = arr.argmax()
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argmin_result = arr.argmin()
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assert argmax_result == argmax_expected
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assert argmin_result == argmin_expected
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@pytest.mark.parametrize(
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"arr,method",
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[(SparseArray([]), "argmax"), (SparseArray([]), "argmin")],
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)
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def test_empty_array(self, arr, method):
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msg = f"attempt to get {method} of an empty sequence"
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with pytest.raises(ValueError, match=msg):
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arr.argmax() if method == "argmax" else arr.argmin()
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