518 lines
20 KiB
Python
518 lines
20 KiB
Python
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import operator
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import numpy as np
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import pytest
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from pandas.compat.numpy import _np_version_under1p20
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import pandas as pd
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import pandas._testing as tm
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from pandas.core import ops
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from pandas.core.arrays.sparse import SparseArray, SparseDtype
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@pytest.fixture(params=["integer", "block"])
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def kind(request):
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"""kind kwarg to pass to SparseArray/SparseSeries"""
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return request.param
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@pytest.fixture(params=[True, False])
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def mix(request):
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# whether to operate op(sparse, dense) instead of op(sparse, sparse)
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return request.param
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class TestSparseArrayArithmetics:
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_base = np.array
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_klass = SparseArray
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def _assert(self, a, b):
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tm.assert_numpy_array_equal(a, b)
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def _check_numeric_ops(self, a, b, a_dense, b_dense, mix, op):
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with np.errstate(invalid="ignore", divide="ignore"):
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if mix:
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result = op(a, b_dense).to_dense()
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else:
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result = op(a, b).to_dense()
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if op in [operator.truediv, ops.rtruediv]:
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# pandas uses future division
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expected = op(a_dense * 1.0, b_dense)
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else:
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expected = op(a_dense, b_dense)
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if op in [operator.floordiv, ops.rfloordiv]:
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# Series sets 1//0 to np.inf, which SparseArray does not do (yet)
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mask = np.isinf(expected)
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if mask.any():
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expected[mask] = np.nan
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self._assert(result, expected)
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def _check_bool_result(self, res):
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assert isinstance(res, self._klass)
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assert isinstance(res.dtype, SparseDtype)
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assert res.dtype.subtype == np.bool_
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assert isinstance(res.fill_value, bool)
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def _check_comparison_ops(self, a, b, a_dense, b_dense):
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with np.errstate(invalid="ignore"):
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# Unfortunately, trying to wrap the computation of each expected
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# value is with np.errstate() is too tedious.
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#
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# sparse & sparse
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self._check_bool_result(a == b)
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self._assert((a == b).to_dense(), a_dense == b_dense)
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self._check_bool_result(a != b)
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self._assert((a != b).to_dense(), a_dense != b_dense)
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self._check_bool_result(a >= b)
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self._assert((a >= b).to_dense(), a_dense >= b_dense)
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self._check_bool_result(a <= b)
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self._assert((a <= b).to_dense(), a_dense <= b_dense)
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self._check_bool_result(a > b)
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self._assert((a > b).to_dense(), a_dense > b_dense)
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self._check_bool_result(a < b)
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self._assert((a < b).to_dense(), a_dense < b_dense)
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# sparse & dense
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self._check_bool_result(a == b_dense)
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self._assert((a == b_dense).to_dense(), a_dense == b_dense)
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self._check_bool_result(a != b_dense)
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self._assert((a != b_dense).to_dense(), a_dense != b_dense)
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self._check_bool_result(a >= b_dense)
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self._assert((a >= b_dense).to_dense(), a_dense >= b_dense)
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self._check_bool_result(a <= b_dense)
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self._assert((a <= b_dense).to_dense(), a_dense <= b_dense)
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self._check_bool_result(a > b_dense)
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self._assert((a > b_dense).to_dense(), a_dense > b_dense)
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self._check_bool_result(a < b_dense)
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self._assert((a < b_dense).to_dense(), a_dense < b_dense)
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def _check_logical_ops(self, a, b, a_dense, b_dense):
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# sparse & sparse
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self._check_bool_result(a & b)
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self._assert((a & b).to_dense(), a_dense & b_dense)
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self._check_bool_result(a | b)
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self._assert((a | b).to_dense(), a_dense | b_dense)
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# sparse & dense
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self._check_bool_result(a & b_dense)
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self._assert((a & b_dense).to_dense(), a_dense & b_dense)
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self._check_bool_result(a | b_dense)
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self._assert((a | b_dense).to_dense(), a_dense | b_dense)
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@pytest.mark.parametrize("scalar", [0, 1, 3])
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@pytest.mark.parametrize("fill_value", [None, 0, 2])
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def test_float_scalar(
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self, kind, mix, all_arithmetic_functions, fill_value, scalar, request
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):
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op = all_arithmetic_functions
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if not _np_version_under1p20:
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if op in [operator.floordiv, ops.rfloordiv]:
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mark = pytest.mark.xfail(strict=False, reason="GH#38172")
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request.node.add_marker(mark)
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values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
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a = self._klass(values, kind=kind, fill_value=fill_value)
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self._check_numeric_ops(a, scalar, values, scalar, mix, op)
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def test_float_scalar_comparison(self, kind):
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values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
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a = self._klass(values, kind=kind)
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self._check_comparison_ops(a, 1, values, 1)
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self._check_comparison_ops(a, 0, values, 0)
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self._check_comparison_ops(a, 3, values, 3)
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a = self._klass(values, kind=kind, fill_value=0)
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self._check_comparison_ops(a, 1, values, 1)
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self._check_comparison_ops(a, 0, values, 0)
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self._check_comparison_ops(a, 3, values, 3)
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a = self._klass(values, kind=kind, fill_value=2)
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self._check_comparison_ops(a, 1, values, 1)
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self._check_comparison_ops(a, 0, values, 0)
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self._check_comparison_ops(a, 3, values, 3)
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def test_float_same_index_without_nans(
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self, kind, mix, all_arithmetic_functions, request
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):
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# when sp_index are the same
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op = all_arithmetic_functions
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values = self._base([0.0, 1.0, 2.0, 6.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0])
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rvalues = self._base([0.0, 2.0, 3.0, 4.0, 0.0, 0.0, 1.0, 3.0, 2.0, 0.0])
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a = self._klass(values, kind=kind, fill_value=0)
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b = self._klass(rvalues, kind=kind, fill_value=0)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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def test_float_same_index_with_nans(
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self, kind, mix, all_arithmetic_functions, request
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):
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# when sp_index are the same
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op = all_arithmetic_functions
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if not _np_version_under1p20:
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if op in [operator.floordiv, ops.rfloordiv]:
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mark = pytest.mark.xfail(strict=False, reason="GH#38172")
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request.node.add_marker(mark)
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values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
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rvalues = self._base([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan])
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a = self._klass(values, kind=kind)
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b = self._klass(rvalues, kind=kind)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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def test_float_same_index_comparison(self, kind):
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# when sp_index are the same
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values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
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rvalues = self._base([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan])
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a = self._klass(values, kind=kind)
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b = self._klass(rvalues, kind=kind)
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self._check_comparison_ops(a, b, values, rvalues)
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values = self._base([0.0, 1.0, 2.0, 6.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0])
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rvalues = self._base([0.0, 2.0, 3.0, 4.0, 0.0, 0.0, 1.0, 3.0, 2.0, 0.0])
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a = self._klass(values, kind=kind, fill_value=0)
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b = self._klass(rvalues, kind=kind, fill_value=0)
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self._check_comparison_ops(a, b, values, rvalues)
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def test_float_array(self, kind, mix, all_arithmetic_functions):
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op = all_arithmetic_functions
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values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
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rvalues = self._base([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan])
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a = self._klass(values, kind=kind)
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b = self._klass(rvalues, kind=kind)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op)
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a = self._klass(values, kind=kind, fill_value=0)
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b = self._klass(rvalues, kind=kind)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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a = self._klass(values, kind=kind, fill_value=0)
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b = self._klass(rvalues, kind=kind, fill_value=0)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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a = self._klass(values, kind=kind, fill_value=1)
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b = self._klass(rvalues, kind=kind, fill_value=2)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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def test_float_array_different_kind(self, mix, all_arithmetic_functions):
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op = all_arithmetic_functions
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values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
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rvalues = self._base([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan])
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a = self._klass(values, kind="integer")
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b = self._klass(rvalues, kind="block")
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op)
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a = self._klass(values, kind="integer", fill_value=0)
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b = self._klass(rvalues, kind="block")
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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a = self._klass(values, kind="integer", fill_value=0)
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b = self._klass(rvalues, kind="block", fill_value=0)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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a = self._klass(values, kind="integer", fill_value=1)
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b = self._klass(rvalues, kind="block", fill_value=2)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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def test_float_array_comparison(self, kind):
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values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
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rvalues = self._base([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan])
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a = self._klass(values, kind=kind)
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b = self._klass(rvalues, kind=kind)
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self._check_comparison_ops(a, b, values, rvalues)
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self._check_comparison_ops(a, b * 0, values, rvalues * 0)
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a = self._klass(values, kind=kind, fill_value=0)
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b = self._klass(rvalues, kind=kind)
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self._check_comparison_ops(a, b, values, rvalues)
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a = self._klass(values, kind=kind, fill_value=0)
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b = self._klass(rvalues, kind=kind, fill_value=0)
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self._check_comparison_ops(a, b, values, rvalues)
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a = self._klass(values, kind=kind, fill_value=1)
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b = self._klass(rvalues, kind=kind, fill_value=2)
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self._check_comparison_ops(a, b, values, rvalues)
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def test_int_array(self, kind, mix, all_arithmetic_functions):
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op = all_arithmetic_functions
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# have to specify dtype explicitly until fixing GH 667
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dtype = np.int64
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values = self._base([0, 1, 2, 0, 0, 0, 1, 2, 1, 0], dtype=dtype)
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rvalues = self._base([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=dtype)
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a = self._klass(values, dtype=dtype, kind=kind)
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assert a.dtype == SparseDtype(dtype)
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b = self._klass(rvalues, dtype=dtype, kind=kind)
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assert b.dtype == SparseDtype(dtype)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op)
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a = self._klass(values, fill_value=0, dtype=dtype, kind=kind)
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assert a.dtype == SparseDtype(dtype)
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b = self._klass(rvalues, dtype=dtype, kind=kind)
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assert b.dtype == SparseDtype(dtype)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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a = self._klass(values, fill_value=0, dtype=dtype, kind=kind)
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assert a.dtype == SparseDtype(dtype)
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b = self._klass(rvalues, fill_value=0, dtype=dtype, kind=kind)
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assert b.dtype == SparseDtype(dtype)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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a = self._klass(values, fill_value=1, dtype=dtype, kind=kind)
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assert a.dtype == SparseDtype(dtype, fill_value=1)
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b = self._klass(rvalues, fill_value=2, dtype=dtype, kind=kind)
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assert b.dtype == SparseDtype(dtype, fill_value=2)
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self._check_numeric_ops(a, b, values, rvalues, mix, op)
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def test_int_array_comparison(self, kind):
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dtype = "int64"
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# int32 NI ATM
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values = self._base([0, 1, 2, 0, 0, 0, 1, 2, 1, 0], dtype=dtype)
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rvalues = self._base([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=dtype)
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a = self._klass(values, dtype=dtype, kind=kind)
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b = self._klass(rvalues, dtype=dtype, kind=kind)
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self._check_comparison_ops(a, b, values, rvalues)
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self._check_comparison_ops(a, b * 0, values, rvalues * 0)
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a = self._klass(values, dtype=dtype, kind=kind, fill_value=0)
|
||
|
|
b = self._klass(rvalues, dtype=dtype, kind=kind)
|
||
|
|
self._check_comparison_ops(a, b, values, rvalues)
|
||
|
|
|
||
|
|
a = self._klass(values, dtype=dtype, kind=kind, fill_value=0)
|
||
|
|
b = self._klass(rvalues, dtype=dtype, kind=kind, fill_value=0)
|
||
|
|
self._check_comparison_ops(a, b, values, rvalues)
|
||
|
|
|
||
|
|
a = self._klass(values, dtype=dtype, kind=kind, fill_value=1)
|
||
|
|
b = self._klass(rvalues, dtype=dtype, kind=kind, fill_value=2)
|
||
|
|
self._check_comparison_ops(a, b, values, rvalues)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("fill_value", [True, False, np.nan])
|
||
|
|
def test_bool_same_index(self, kind, fill_value):
|
||
|
|
# GH 14000
|
||
|
|
# when sp_index are the same
|
||
|
|
values = self._base([True, False, True, True], dtype=np.bool_)
|
||
|
|
rvalues = self._base([True, False, True, True], dtype=np.bool_)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind, dtype=np.bool_, fill_value=fill_value)
|
||
|
|
b = self._klass(rvalues, kind=kind, dtype=np.bool_, fill_value=fill_value)
|
||
|
|
self._check_logical_ops(a, b, values, rvalues)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("fill_value", [True, False, np.nan])
|
||
|
|
def test_bool_array_logical(self, kind, fill_value):
|
||
|
|
# GH 14000
|
||
|
|
# when sp_index are the same
|
||
|
|
values = self._base([True, False, True, False, True, True], dtype=np.bool_)
|
||
|
|
rvalues = self._base([True, False, False, True, False, True], dtype=np.bool_)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind, dtype=np.bool_, fill_value=fill_value)
|
||
|
|
b = self._klass(rvalues, kind=kind, dtype=np.bool_, fill_value=fill_value)
|
||
|
|
self._check_logical_ops(a, b, values, rvalues)
|
||
|
|
|
||
|
|
def test_mixed_array_float_int(self, kind, mix, all_arithmetic_functions, request):
|
||
|
|
op = all_arithmetic_functions
|
||
|
|
|
||
|
|
if not _np_version_under1p20:
|
||
|
|
if op in [operator.floordiv, ops.rfloordiv] and mix:
|
||
|
|
mark = pytest.mark.xfail(strict=True, reason="GH#38172")
|
||
|
|
request.node.add_marker(mark)
|
||
|
|
|
||
|
|
rdtype = "int64"
|
||
|
|
|
||
|
|
values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
|
||
|
|
rvalues = self._base([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=rdtype)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind)
|
||
|
|
b = self._klass(rvalues, kind=kind)
|
||
|
|
assert b.dtype == SparseDtype(rdtype)
|
||
|
|
|
||
|
|
self._check_numeric_ops(a, b, values, rvalues, mix, op)
|
||
|
|
self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind, fill_value=0)
|
||
|
|
b = self._klass(rvalues, kind=kind)
|
||
|
|
assert b.dtype == SparseDtype(rdtype)
|
||
|
|
self._check_numeric_ops(a, b, values, rvalues, mix, op)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind, fill_value=0)
|
||
|
|
b = self._klass(rvalues, kind=kind, fill_value=0)
|
||
|
|
assert b.dtype == SparseDtype(rdtype)
|
||
|
|
self._check_numeric_ops(a, b, values, rvalues, mix, op)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind, fill_value=1)
|
||
|
|
b = self._klass(rvalues, kind=kind, fill_value=2)
|
||
|
|
assert b.dtype == SparseDtype(rdtype, fill_value=2)
|
||
|
|
self._check_numeric_ops(a, b, values, rvalues, mix, op)
|
||
|
|
|
||
|
|
def test_mixed_array_comparison(self, kind):
|
||
|
|
rdtype = "int64"
|
||
|
|
# int32 NI ATM
|
||
|
|
|
||
|
|
values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])
|
||
|
|
rvalues = self._base([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=rdtype)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind)
|
||
|
|
b = self._klass(rvalues, kind=kind)
|
||
|
|
assert b.dtype == SparseDtype(rdtype)
|
||
|
|
|
||
|
|
self._check_comparison_ops(a, b, values, rvalues)
|
||
|
|
self._check_comparison_ops(a, b * 0, values, rvalues * 0)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind, fill_value=0)
|
||
|
|
b = self._klass(rvalues, kind=kind)
|
||
|
|
assert b.dtype == SparseDtype(rdtype)
|
||
|
|
self._check_comparison_ops(a, b, values, rvalues)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind, fill_value=0)
|
||
|
|
b = self._klass(rvalues, kind=kind, fill_value=0)
|
||
|
|
assert b.dtype == SparseDtype(rdtype)
|
||
|
|
self._check_comparison_ops(a, b, values, rvalues)
|
||
|
|
|
||
|
|
a = self._klass(values, kind=kind, fill_value=1)
|
||
|
|
b = self._klass(rvalues, kind=kind, fill_value=2)
|
||
|
|
assert b.dtype == SparseDtype(rdtype, fill_value=2)
|
||
|
|
self._check_comparison_ops(a, b, values, rvalues)
|
||
|
|
|
||
|
|
def test_xor(self):
|
||
|
|
s = SparseArray([True, True, False, False])
|
||
|
|
t = SparseArray([True, False, True, False])
|
||
|
|
result = s ^ t
|
||
|
|
sp_index = pd.core.arrays.sparse.IntIndex(4, np.array([0, 1, 2], dtype="int32"))
|
||
|
|
expected = SparseArray([False, True, True], sparse_index=sp_index)
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("op", [operator.eq, operator.add])
|
||
|
|
def test_with_list(op):
|
||
|
|
arr = SparseArray([0, 1], fill_value=0)
|
||
|
|
result = op(arr, [0, 1])
|
||
|
|
expected = op(arr, SparseArray([0, 1]))
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_with_dataframe():
|
||
|
|
# GH#27910
|
||
|
|
arr = SparseArray([0, 1], fill_value=0)
|
||
|
|
df = pd.DataFrame([[1, 2], [3, 4]])
|
||
|
|
result = arr.__add__(df)
|
||
|
|
assert result is NotImplemented
|
||
|
|
|
||
|
|
|
||
|
|
def test_with_zerodim_ndarray():
|
||
|
|
# GH#27910
|
||
|
|
arr = SparseArray([0, 1], fill_value=0)
|
||
|
|
|
||
|
|
result = arr * np.array(2)
|
||
|
|
expected = arr * 2
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("ufunc", [np.abs, np.exp])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"arr", [SparseArray([0, 0, -1, 1]), SparseArray([None, None, -1, 1])]
|
||
|
|
)
|
||
|
|
def test_ufuncs(ufunc, arr):
|
||
|
|
result = ufunc(arr)
|
||
|
|
fill_value = ufunc(arr.fill_value)
|
||
|
|
expected = SparseArray(ufunc(np.asarray(arr)), fill_value=fill_value)
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"a, b",
|
||
|
|
[
|
||
|
|
(SparseArray([0, 0, 0]), np.array([0, 1, 2])),
|
||
|
|
(SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),
|
||
|
|
(SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),
|
||
|
|
(SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),
|
||
|
|
(SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("ufunc", [np.add, np.greater])
|
||
|
|
def test_binary_ufuncs(ufunc, a, b):
|
||
|
|
# can't say anything about fill value here.
|
||
|
|
result = ufunc(a, b)
|
||
|
|
expected = ufunc(np.asarray(a), np.asarray(b))
|
||
|
|
assert isinstance(result, SparseArray)
|
||
|
|
tm.assert_numpy_array_equal(np.asarray(result), expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ndarray_inplace():
|
||
|
|
sparray = SparseArray([0, 2, 0, 0])
|
||
|
|
ndarray = np.array([0, 1, 2, 3])
|
||
|
|
ndarray += sparray
|
||
|
|
expected = np.array([0, 3, 2, 3])
|
||
|
|
tm.assert_numpy_array_equal(ndarray, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_sparray_inplace():
|
||
|
|
sparray = SparseArray([0, 2, 0, 0])
|
||
|
|
ndarray = np.array([0, 1, 2, 3])
|
||
|
|
sparray += ndarray
|
||
|
|
expected = SparseArray([0, 3, 2, 3], fill_value=0)
|
||
|
|
tm.assert_sp_array_equal(sparray, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("fill_value", [True, False])
|
||
|
|
def test_invert(fill_value):
|
||
|
|
arr = np.array([True, False, False, True])
|
||
|
|
sparray = SparseArray(arr, fill_value=fill_value)
|
||
|
|
result = ~sparray
|
||
|
|
expected = SparseArray(~arr, fill_value=not fill_value)
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|
||
|
|
|
||
|
|
result = ~pd.Series(sparray)
|
||
|
|
expected = pd.Series(expected)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = ~pd.DataFrame({"A": sparray})
|
||
|
|
expected = pd.DataFrame({"A": expected})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("fill_value", [0, np.nan])
|
||
|
|
@pytest.mark.parametrize("op", [operator.pos, operator.neg])
|
||
|
|
def test_unary_op(op, fill_value):
|
||
|
|
arr = np.array([0, 1, np.nan, 2])
|
||
|
|
sparray = SparseArray(arr, fill_value=fill_value)
|
||
|
|
result = op(sparray)
|
||
|
|
expected = SparseArray(op(arr), fill_value=op(fill_value))
|
||
|
|
tm.assert_sp_array_equal(result, expected)
|