from __future__ import annotations
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import decimal
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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_gt2
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import pandas as pd
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import pandas._testing as tm
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from pandas.tests.extension import base
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from pandas.tests.extension.decimal.array import (
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DecimalArray,
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DecimalDtype,
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make_data,
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to_decimal,
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)
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@pytest.fixture
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def dtype():
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return DecimalDtype()
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@pytest.fixture
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def data():
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return DecimalArray(make_data())
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@pytest.fixture
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def data_for_twos():
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return DecimalArray([decimal.Decimal(2) for _ in range(100)])
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@pytest.fixture
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def data_missing():
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return DecimalArray([decimal.Decimal("NaN"), decimal.Decimal(1)])
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@pytest.fixture
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def data_for_sorting():
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return DecimalArray(
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[decimal.Decimal("1"), decimal.Decimal("2"), decimal.Decimal("0")]
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)
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@pytest.fixture
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def data_missing_for_sorting():
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return DecimalArray(
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[decimal.Decimal("1"), decimal.Decimal("NaN"), decimal.Decimal("0")]
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)
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@pytest.fixture
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def na_cmp():
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return lambda x, y: x.is_nan() and y.is_nan()
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@pytest.fixture
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def data_for_grouping():
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b = decimal.Decimal("1.0")
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a = decimal.Decimal("0.0")
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c = decimal.Decimal("2.0")
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na = decimal.Decimal("NaN")
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return DecimalArray([b, b, na, na, a, a, b, c])
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class TestDecimalArray(base.ExtensionTests):
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def _get_expected_exception(
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self, op_name: str, obj, other
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) -> type[Exception] | tuple[type[Exception], ...] | None:
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return None
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def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
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return True
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def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
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if op_name == "count":
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return super().check_reduce(ser, op_name, skipna)
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else:
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result = getattr(ser, op_name)(skipna=skipna)
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expected = getattr(np.asarray(ser), op_name)()
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tm.assert_almost_equal(result, expected)
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def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna, request):
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if all_numeric_reductions in ["kurt", "skew", "sem", "median"]:
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mark = pytest.mark.xfail(raises=NotImplementedError)
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request.applymarker(mark)
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super().test_reduce_series_numeric(data, all_numeric_reductions, skipna)
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def test_reduce_frame(self, data, all_numeric_reductions, skipna, request):
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op_name = all_numeric_reductions
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if op_name in ["skew", "median"]:
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mark = pytest.mark.xfail(raises=NotImplementedError)
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request.applymarker(mark)
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return super().test_reduce_frame(data, all_numeric_reductions, skipna)
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def test_compare_scalar(self, data, comparison_op):
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ser = pd.Series(data)
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self._compare_other(ser, data, comparison_op, 0.5)
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def test_compare_array(self, data, comparison_op):
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ser = pd.Series(data)
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alter = np.random.default_rng(2).choice([-1, 0, 1], len(data))
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# Randomly double, halve or keep same value
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other = pd.Series(data) * [decimal.Decimal(pow(2.0, i)) for i in alter]
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self._compare_other(ser, data, comparison_op, other)
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def test_arith_series_with_array(self, data, all_arithmetic_operators):
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op_name = all_arithmetic_operators
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ser = pd.Series(data)
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context = decimal.getcontext()
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divbyzerotrap = context.traps[decimal.DivisionByZero]
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invalidoptrap = context.traps[decimal.InvalidOperation]
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context.traps[decimal.DivisionByZero] = 0
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context.traps[decimal.InvalidOperation] = 0
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# Decimal supports ops with int, but not float
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other = pd.Series([int(d * 100) for d in data])
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self.check_opname(ser, op_name, other)
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if "mod" not in op_name:
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self.check_opname(ser, op_name, ser * 2)
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self.check_opname(ser, op_name, 0)
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self.check_opname(ser, op_name, 5)
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context.traps[decimal.DivisionByZero] = divbyzerotrap
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context.traps[decimal.InvalidOperation] = invalidoptrap
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def test_fillna_frame(self, data_missing):
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msg = "ExtensionArray.fillna added a 'copy' keyword"
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with tm.assert_produces_warning(
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DeprecationWarning, match=msg, check_stacklevel=False
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):
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super().test_fillna_frame(data_missing)
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def test_fillna_limit_pad(self, data_missing):
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msg = "ExtensionArray.fillna 'method' keyword is deprecated"
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with tm.assert_produces_warning(
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DeprecationWarning,
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match=msg,
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check_stacklevel=False,
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raise_on_extra_warnings=False,
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):
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super().test_fillna_limit_pad(data_missing)
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msg = "The 'method' keyword in DecimalArray.fillna is deprecated"
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with tm.assert_produces_warning(
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FutureWarning,
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match=msg,
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check_stacklevel=False,
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raise_on_extra_warnings=False,
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):
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super().test_fillna_limit_pad(data_missing)
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@pytest.mark.parametrize(
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"limit_area, input_ilocs, expected_ilocs",
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[
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("outside", [1, 0, 0, 0, 1], [1, 0, 0, 0, 1]),
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("outside", [1, 0, 1, 0, 1], [1, 0, 1, 0, 1]),
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("outside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 1]),
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("outside", [0, 1, 0, 1, 0], [0, 1, 0, 1, 1]),
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("inside", [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),
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("inside", [1, 0, 1, 0, 1], [1, 1, 1, 1, 1]),
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("inside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 0]),
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("inside", [0, 1, 0, 1, 0], [0, 1, 1, 1, 0]),
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],
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)
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def test_ffill_limit_area(
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self, data_missing, limit_area, input_ilocs, expected_ilocs
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):
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# GH#56616
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msg = "ExtensionArray.fillna 'method' keyword is deprecated"
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with tm.assert_produces_warning(
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DeprecationWarning,
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match=msg,
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check_stacklevel=False,
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raise_on_extra_warnings=False,
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):
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msg = "DecimalArray does not implement limit_area"
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with pytest.raises(NotImplementedError, match=msg):
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super().test_ffill_limit_area(
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data_missing, limit_area, input_ilocs, expected_ilocs
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)
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def test_fillna_limit_backfill(self, data_missing):
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msg = "Series.fillna with 'method' is deprecated"
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with tm.assert_produces_warning(
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FutureWarning,
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match=msg,
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check_stacklevel=False,
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raise_on_extra_warnings=False,
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):
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super().test_fillna_limit_backfill(data_missing)
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msg = "ExtensionArray.fillna 'method' keyword is deprecated"
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with tm.assert_produces_warning(
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DeprecationWarning,
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match=msg,
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check_stacklevel=False,
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raise_on_extra_warnings=False,
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):
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super().test_fillna_limit_backfill(data_missing)
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msg = "The 'method' keyword in DecimalArray.fillna is deprecated"
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with tm.assert_produces_warning(
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FutureWarning,
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match=msg,
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check_stacklevel=False,
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raise_on_extra_warnings=False,
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):
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super().test_fillna_limit_backfill(data_missing)
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def test_fillna_no_op_returns_copy(self, data):
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msg = "|".join(
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[
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"ExtensionArray.fillna 'method' keyword is deprecated",
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"The 'method' keyword in DecimalArray.fillna is deprecated",
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]
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)
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with tm.assert_produces_warning(
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(FutureWarning, DeprecationWarning), match=msg, check_stacklevel=False
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):
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super().test_fillna_no_op_returns_copy(data)
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def test_fillna_series(self, data_missing):
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msg = "ExtensionArray.fillna added a 'copy' keyword"
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with tm.assert_produces_warning(
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DeprecationWarning, match=msg, check_stacklevel=False
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):
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super().test_fillna_series(data_missing)
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def test_fillna_series_method(self, data_missing, fillna_method):
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msg = "|".join(
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[
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"ExtensionArray.fillna 'method' keyword is deprecated",
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"The 'method' keyword in DecimalArray.fillna is deprecated",
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]
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)
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with tm.assert_produces_warning(
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(FutureWarning, DeprecationWarning), match=msg, check_stacklevel=False
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):
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super().test_fillna_series_method(data_missing, fillna_method)
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def test_fillna_copy_frame(self, data_missing, using_copy_on_write):
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warn = DeprecationWarning if not using_copy_on_write else None
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msg = "ExtensionArray.fillna added a 'copy' keyword"
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with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
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super().test_fillna_copy_frame(data_missing)
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def test_fillna_copy_series(self, data_missing, using_copy_on_write):
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warn = DeprecationWarning if not using_copy_on_write else None
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msg = "ExtensionArray.fillna added a 'copy' keyword"
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with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
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super().test_fillna_copy_series(data_missing)
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@pytest.mark.parametrize("dropna", [True, False])
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def test_value_counts(self, all_data, dropna, request):
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all_data = all_data[:10]
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if dropna:
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other = np.array(all_data[~all_data.isna()])
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else:
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other = all_data
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vcs = pd.Series(all_data).value_counts(dropna=dropna)
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vcs_ex = pd.Series(other).value_counts(dropna=dropna)
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with decimal.localcontext() as ctx:
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# avoid raising when comparing Decimal("NAN") < Decimal(2)
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ctx.traps[decimal.InvalidOperation] = False
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result = vcs.sort_index()
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expected = vcs_ex.sort_index()
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tm.assert_series_equal(result, expected)
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def test_series_repr(self, data):
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# Overriding this base test to explicitly test that
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# the custom _formatter is used
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ser = pd.Series(data)
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assert data.dtype.name in repr(ser)
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assert "Decimal: " in repr(ser)
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@pytest.mark.xfail(reason="Inconsistent array-vs-scalar behavior")
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@pytest.mark.parametrize("ufunc", [np.positive, np.negative, np.abs])
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def test_unary_ufunc_dunder_equivalence(self, data, ufunc):
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super().test_unary_ufunc_dunder_equivalence(data, ufunc)
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def test_array_interface_copy(self, data):
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result_copy1 = np.array(data, copy=True)
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result_copy2 = np.array(data, copy=True)
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assert not np.may_share_memory(result_copy1, result_copy2)
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if not np_version_gt2:
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# copy=False semantics are only supported in NumPy>=2.
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return
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try:
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result_nocopy1 = np.array(data, copy=False)
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except ValueError:
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# An error is always acceptable for `copy=False`
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return
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result_nocopy2 = np.array(data, copy=False)
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# If copy=False was given and did not raise, these must share the same data
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assert np.may_share_memory(result_nocopy1, result_nocopy2)
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|
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def test_take_na_value_other_decimal():
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arr = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
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result = arr.take([0, -1], allow_fill=True, fill_value=decimal.Decimal("-1.0"))
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expected = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("-1.0")])
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tm.assert_extension_array_equal(result, expected)
|
|
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def test_series_constructor_coerce_data_to_extension_dtype():
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dtype = DecimalDtype()
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ser = pd.Series([0, 1, 2], dtype=dtype)
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arr = DecimalArray(
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[decimal.Decimal(0), decimal.Decimal(1), decimal.Decimal(2)],
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dtype=dtype,
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)
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exp = pd.Series(arr)
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tm.assert_series_equal(ser, exp)
|
|
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def test_series_constructor_with_dtype():
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arr = DecimalArray([decimal.Decimal("10.0")])
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result = pd.Series(arr, dtype=DecimalDtype())
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expected = pd.Series(arr)
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tm.assert_series_equal(result, expected)
|
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result = pd.Series(arr, dtype="int64")
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expected = pd.Series([10])
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tm.assert_series_equal(result, expected)
|
|
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def test_dataframe_constructor_with_dtype():
|
arr = DecimalArray([decimal.Decimal("10.0")])
|
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result = pd.DataFrame({"A": arr}, dtype=DecimalDtype())
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expected = pd.DataFrame({"A": arr})
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tm.assert_frame_equal(result, expected)
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arr = DecimalArray([decimal.Decimal("10.0")])
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result = pd.DataFrame({"A": arr}, dtype="int64")
|
expected = pd.DataFrame({"A": [10]})
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tm.assert_frame_equal(result, expected)
|
|
|
@pytest.mark.parametrize("frame", [True, False])
|
def test_astype_dispatches(frame):
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# This is a dtype-specific test that ensures Series[decimal].astype
|
# gets all the way through to ExtensionArray.astype
|
# Designing a reliable smoke test that works for arbitrary data types
|
# is difficult.
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data = pd.Series(DecimalArray([decimal.Decimal(2)]), name="a")
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ctx = decimal.Context()
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ctx.prec = 5
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if frame:
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data = data.to_frame()
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result = data.astype(DecimalDtype(ctx))
|
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if frame:
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result = result["a"]
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assert result.dtype.context.prec == ctx.prec
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|
|
class DecimalArrayWithoutFromSequence(DecimalArray):
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"""Helper class for testing error handling in _from_sequence."""
|
|
@classmethod
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def _from_sequence(cls, scalars, *, dtype=None, copy=False):
|
raise KeyError("For the test")
|
|
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class DecimalArrayWithoutCoercion(DecimalArrayWithoutFromSequence):
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@classmethod
|
def _create_arithmetic_method(cls, op):
|
return cls._create_method(op, coerce_to_dtype=False)
|
|
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DecimalArrayWithoutCoercion._add_arithmetic_ops()
|
|
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def test_combine_from_sequence_raises(monkeypatch):
|
# https://github.com/pandas-dev/pandas/issues/22850
|
cls = DecimalArrayWithoutFromSequence
|
|
@classmethod
|
def construct_array_type(cls):
|
return DecimalArrayWithoutFromSequence
|
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monkeypatch.setattr(DecimalDtype, "construct_array_type", construct_array_type)
|
|
arr = cls([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
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ser = pd.Series(arr)
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result = ser.combine(ser, operator.add)
|
|
# note: object dtype
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expected = pd.Series(
|
[decimal.Decimal("2.0"), decimal.Decimal("4.0")], dtype="object"
|
)
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tm.assert_series_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"class_", [DecimalArrayWithoutFromSequence, DecimalArrayWithoutCoercion]
|
)
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def test_scalar_ops_from_sequence_raises(class_):
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# op(EA, EA) should return an EA, or an ndarray if it's not possible
|
# to return an EA with the return values.
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arr = class_([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
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result = arr + arr
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expected = np.array(
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[decimal.Decimal("2.0"), decimal.Decimal("4.0")], dtype="object"
|
)
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tm.assert_numpy_array_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"reverse, expected_div, expected_mod",
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[(False, [0, 1, 1, 2], [1, 0, 1, 0]), (True, [2, 1, 0, 0], [0, 0, 2, 2])],
|
)
|
def test_divmod_array(reverse, expected_div, expected_mod):
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# https://github.com/pandas-dev/pandas/issues/22930
|
arr = to_decimal([1, 2, 3, 4])
|
if reverse:
|
div, mod = divmod(2, arr)
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else:
|
div, mod = divmod(arr, 2)
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expected_div = to_decimal(expected_div)
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expected_mod = to_decimal(expected_mod)
|
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tm.assert_extension_array_equal(div, expected_div)
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tm.assert_extension_array_equal(mod, expected_mod)
|
|
|
def test_ufunc_fallback(data):
|
a = data[:5]
|
s = pd.Series(a, index=range(3, 8))
|
result = np.abs(s)
|
expected = pd.Series(np.abs(a), index=range(3, 8))
|
tm.assert_series_equal(result, expected)
|
|
|
def test_array_ufunc():
|
a = to_decimal([1, 2, 3])
|
result = np.exp(a)
|
expected = to_decimal(np.exp(a._data))
|
tm.assert_extension_array_equal(result, expected)
|
|
|
def test_array_ufunc_series():
|
a = to_decimal([1, 2, 3])
|
s = pd.Series(a)
|
result = np.exp(s)
|
expected = pd.Series(to_decimal(np.exp(a._data)))
|
tm.assert_series_equal(result, expected)
|
|
|
def test_array_ufunc_series_scalar_other():
|
# check _HANDLED_TYPES
|
a = to_decimal([1, 2, 3])
|
s = pd.Series(a)
|
result = np.add(s, decimal.Decimal(1))
|
expected = pd.Series(np.add(a, decimal.Decimal(1)))
|
tm.assert_series_equal(result, expected)
|
|
|
def test_array_ufunc_series_defer():
|
a = to_decimal([1, 2, 3])
|
s = pd.Series(a)
|
|
expected = pd.Series(to_decimal([2, 4, 6]))
|
r1 = np.add(s, a)
|
r2 = np.add(a, s)
|
|
tm.assert_series_equal(r1, expected)
|
tm.assert_series_equal(r2, expected)
|
|
|
def test_groupby_agg():
|
# Ensure that the result of agg is inferred to be decimal dtype
|
# https://github.com/pandas-dev/pandas/issues/29141
|
|
data = make_data()[:5]
|
df = pd.DataFrame(
|
{"id1": [0, 0, 0, 1, 1], "id2": [0, 1, 0, 1, 1], "decimals": DecimalArray(data)}
|
)
|
|
# single key, selected column
|
expected = pd.Series(to_decimal([data[0], data[3]]))
|
result = df.groupby("id1")["decimals"].agg(lambda x: x.iloc[0])
|
tm.assert_series_equal(result, expected, check_names=False)
|
result = df["decimals"].groupby(df["id1"]).agg(lambda x: x.iloc[0])
|
tm.assert_series_equal(result, expected, check_names=False)
|
|
# multiple keys, selected column
|
expected = pd.Series(
|
to_decimal([data[0], data[1], data[3]]),
|
index=pd.MultiIndex.from_tuples([(0, 0), (0, 1), (1, 1)]),
|
)
|
result = df.groupby(["id1", "id2"])["decimals"].agg(lambda x: x.iloc[0])
|
tm.assert_series_equal(result, expected, check_names=False)
|
result = df["decimals"].groupby([df["id1"], df["id2"]]).agg(lambda x: x.iloc[0])
|
tm.assert_series_equal(result, expected, check_names=False)
|
|
# multiple columns
|
expected = pd.DataFrame({"id2": [0, 1], "decimals": to_decimal([data[0], data[3]])})
|
result = df.groupby("id1").agg(lambda x: x.iloc[0])
|
tm.assert_frame_equal(result, expected, check_names=False)
|
|
|
def test_groupby_agg_ea_method(monkeypatch):
|
# Ensure that the result of agg is inferred to be decimal dtype
|
# https://github.com/pandas-dev/pandas/issues/29141
|
|
def DecimalArray__my_sum(self):
|
return np.sum(np.array(self))
|
|
monkeypatch.setattr(DecimalArray, "my_sum", DecimalArray__my_sum, raising=False)
|
|
data = make_data()[:5]
|
df = pd.DataFrame({"id": [0, 0, 0, 1, 1], "decimals": DecimalArray(data)})
|
expected = pd.Series(to_decimal([data[0] + data[1] + data[2], data[3] + data[4]]))
|
|
result = df.groupby("id")["decimals"].agg(lambda x: x.values.my_sum())
|
tm.assert_series_equal(result, expected, check_names=False)
|
s = pd.Series(DecimalArray(data))
|
grouper = np.array([0, 0, 0, 1, 1], dtype=np.int64)
|
result = s.groupby(grouper).agg(lambda x: x.values.my_sum())
|
tm.assert_series_equal(result, expected, check_names=False)
|
|
|
def test_indexing_no_materialize(monkeypatch):
|
# See https://github.com/pandas-dev/pandas/issues/29708
|
# Ensure that indexing operations do not materialize (convert to a numpy
|
# array) the ExtensionArray unnecessary
|
|
def DecimalArray__array__(self, dtype=None):
|
raise Exception("tried to convert a DecimalArray to a numpy array")
|
|
monkeypatch.setattr(DecimalArray, "__array__", DecimalArray__array__, raising=False)
|
|
data = make_data()
|
s = pd.Series(DecimalArray(data))
|
df = pd.DataFrame({"a": s, "b": range(len(s))})
|
|
# ensure the following operations do not raise an error
|
s[s > 0.5]
|
df[s > 0.5]
|
s.at[0]
|
df.at[0, "a"]
|
|
|
def test_to_numpy_keyword():
|
# test the extra keyword
|
values = [decimal.Decimal("1.1111"), decimal.Decimal("2.2222")]
|
expected = np.array(
|
[decimal.Decimal("1.11"), decimal.Decimal("2.22")], dtype="object"
|
)
|
a = pd.array(values, dtype="decimal")
|
result = a.to_numpy(decimals=2)
|
tm.assert_numpy_array_equal(result, expected)
|
|
result = pd.Series(a).to_numpy(decimals=2)
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
def test_array_copy_on_write(using_copy_on_write):
|
df = pd.DataFrame({"a": [decimal.Decimal(2), decimal.Decimal(3)]}, dtype="object")
|
df2 = df.astype(DecimalDtype())
|
df.iloc[0, 0] = 0
|
if using_copy_on_write:
|
expected = pd.DataFrame(
|
{"a": [decimal.Decimal(2), decimal.Decimal(3)]}, dtype=DecimalDtype()
|
)
|
tm.assert_equal(df2.values, expected.values)
|
