test_numeric.py

# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for numeric dtypes
from __future__ import annotations

from collections import abc
from datetime import timedelta
from decimal import Decimal
import operator

import numpy as np
import pytest

import pandas as pd
from pandas import (
    Index,
    RangeIndex,
    Series,
    Timedelta,
    TimedeltaIndex,
    array,
)
import pandas._testing as tm
from pandas.core import ops
from pandas.core.computation import expressions as expr
from pandas.tests.arithmetic.common import (
    assert_invalid_addsub_type,
    assert_invalid_comparison,
)


@pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"])
def switch_numexpr_min_elements(request, monkeypatch):
    with monkeypatch.context() as m:
        m.setattr(expr, "_MIN_ELEMENTS", request.param)
        yield request.param


@pytest.fixture(params=[Index, Series, tm.to_array])
def box_pandas_1d_array(request):
    """
    Fixture to test behavior for Index, Series and tm.to_array classes
    """
    return request.param


def adjust_negative_zero(zero, expected):
    """
    Helper to adjust the expected result if we are dividing by -0.0
    as opposed to 0.0
    """
    if np.signbit(np.array(zero)).any():
        # All entries in the `zero` fixture should be either
        #  all-negative or no-negative.
        assert np.signbit(np.array(zero)).all()

        expected *= -1

    return expected


def compare_op(series, other, op):
    left = np.abs(series) if op in (ops.rpow, operator.pow) else series
    right = np.abs(other) if op in (ops.rpow, operator.pow) else other

    cython_or_numpy = op(left, right)
    python = left.combine(right, op)
    if isinstance(other, Series) and not other.index.equals(series.index):
        python.index = python.index._with_freq(None)
    tm.assert_series_equal(cython_or_numpy, python)


# TODO: remove this kludge once mypy stops giving false positives here
# List comprehension has incompatible type List[PandasObject]; expected List[RangeIndex]
#  See GH#29725
_ldtypes = ["i1", "i2", "i4", "i8", "u1", "u2", "u4", "u8", "f2", "f4", "f8"]
lefts: list[Index | Series] = [RangeIndex(10, 40, 10)]
lefts.extend([Series([10, 20, 30], dtype=dtype) for dtype in _ldtypes])
lefts.extend([Index([10, 20, 30], dtype=dtype) for dtype in _ldtypes if dtype != "f2"])

# ------------------------------------------------------------------
# Comparisons


class TestNumericComparisons:
    def test_operator_series_comparison_zerorank(self):
        # GH#13006
        result = np.float64(0) > Series([1, 2, 3])
        expected = 0.0 > Series([1, 2, 3])
        tm.assert_series_equal(result, expected)
        result = Series([1, 2, 3]) < np.float64(0)
        expected = Series([1, 2, 3]) < 0.0
        tm.assert_series_equal(result, expected)
        result = np.array([0, 1, 2])[0] > Series([0, 1, 2])
        expected = 0.0 > Series([1, 2, 3])
        tm.assert_series_equal(result, expected)

    def test_df_numeric_cmp_dt64_raises(self, box_with_array, fixed_now_ts):
        # GH#8932, GH#22163
        ts = fixed_now_ts
        obj = np.array(range(5))
        obj = tm.box_expected(obj, box_with_array)

        assert_invalid_comparison(obj, ts, box_with_array)

    def test_compare_invalid(self):
        # GH#8058
        # ops testing
        a = Series(np.random.default_rng(2).standard_normal(5), name=0)
        b = Series(np.random.default_rng(2).standard_normal(5))
        b.name = pd.Timestamp("2000-01-01")
        tm.assert_series_equal(a / b, 1 / (b / a))

    def test_numeric_cmp_string_numexpr_path(self, box_with_array, monkeypatch):
        # GH#36377, GH#35700
        box = box_with_array
        xbox = box if box is not Index else np.ndarray

        obj = Series(np.random.default_rng(2).standard_normal(51))
        obj = tm.box_expected(obj, box, transpose=False)
        with monkeypatch.context() as m:
            m.setattr(expr, "_MIN_ELEMENTS", 50)
            result = obj == "a"

        expected = Series(np.zeros(51, dtype=bool))
        expected = tm.box_expected(expected, xbox, transpose=False)
        tm.assert_equal(result, expected)

        with monkeypatch.context() as m:
            m.setattr(expr, "_MIN_ELEMENTS", 50)
            result = obj != "a"
        tm.assert_equal(result, ~expected)

        msg = "Invalid comparison between dtype=float64 and str"
        with pytest.raises(TypeError, match=msg):
            obj < "a"


# ------------------------------------------------------------------
# Numeric dtypes Arithmetic with Datetime/Timedelta Scalar


class TestNumericArraylikeArithmeticWithDatetimeLike:
    @pytest.mark.parametrize("box_cls", [np.array, Index, Series])
    @pytest.mark.parametrize(
        "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype)
    )
    def test_mul_td64arr(self, left, box_cls):
        # GH#22390
        right = np.array([1, 2, 3], dtype="m8[s]")
        right = box_cls(right)

        expected = TimedeltaIndex(["10s", "40s", "90s"], dtype=right.dtype)

        if isinstance(left, Series) or box_cls is Series:
            expected = Series(expected)
        assert expected.dtype == right.dtype

        result = left * right
        tm.assert_equal(result, expected)

        result = right * left
        tm.assert_equal(result, expected)

    @pytest.mark.parametrize("box_cls", [np.array, Index, Series])
    @pytest.mark.parametrize(
        "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype)
    )
    def test_div_td64arr(self, left, box_cls):
        # GH#22390
        right = np.array([10, 40, 90], dtype="m8[s]")
        right = box_cls(right)

        expected = TimedeltaIndex(["1s", "2s", "3s"], dtype=right.dtype)
        if isinstance(left, Series) or box_cls is Series:
            expected = Series(expected)
        assert expected.dtype == right.dtype

        result = right / left
        tm.assert_equal(result, expected)

        result = right // left
        tm.assert_equal(result, expected)

        # (true_) needed for min-versions build 2022-12-26
        msg = "ufunc '(true_)?divide' cannot use operands with types"
        with pytest.raises(TypeError, match=msg):
            left / right

        msg = "ufunc 'floor_divide' cannot use operands with types"
        with pytest.raises(TypeError, match=msg):
            left // right

    # TODO: also test Tick objects;
    #  see test_numeric_arr_rdiv_tdscalar for note on these failing
    @pytest.mark.parametrize(
        "scalar_td",
        [
            Timedelta(days=1),
            Timedelta(days=1).to_timedelta64(),
            Timedelta(days=1).to_pytimedelta(),
            Timedelta(days=1).to_timedelta64().astype("timedelta64[s]"),
            Timedelta(days=1).to_timedelta64().astype("timedelta64[ms]"),
        ],
        ids=lambda x: type(x).__name__,
    )
    def test_numeric_arr_mul_tdscalar(self, scalar_td, numeric_idx, box_with_array):
        # GH#19333
        box = box_with_array
        index = numeric_idx
        expected = TimedeltaIndex([Timedelta(days=n) for n in range(len(index))])
        if isinstance(scalar_td, np.timedelta64):
            dtype = scalar_td.dtype
            expected = expected.astype(dtype)
        elif type(scalar_td) is timedelta:
            expected = expected.astype("m8[us]")

        index = tm.box_expected(index, box)
        expected = tm.box_expected(expected, box)

        result = index * scalar_td
        tm.assert_equal(result, expected)

        commute = scalar_td * index
        tm.assert_equal(commute, expected)

    @pytest.mark.parametrize(
        "scalar_td",
        [
            Timedelta(days=1),
            Timedelta(days=1).to_timedelta64(),
            Timedelta(days=1).to_pytimedelta(),
        ],
        ids=lambda x: type(x).__name__,
    )
    @pytest.mark.parametrize("dtype", [np.int64, np.float64])
    def test_numeric_arr_mul_tdscalar_numexpr_path(
        self, dtype, scalar_td, box_with_array
    ):
        # GH#44772 for the float64 case
        box = box_with_array

        arr_i8 = np.arange(2 * 10**4).astype(np.int64, copy=False)
        arr = arr_i8.astype(dtype, copy=False)
        obj = tm.box_expected(arr, box, transpose=False)

        expected = arr_i8.view("timedelta64[D]").astype("timedelta64[ns]")
        if type(scalar_td) is timedelta:
            expected = expected.astype("timedelta64[us]")

        expected = tm.box_expected(expected, box, transpose=False)

        result = obj * scalar_td
        tm.assert_equal(result, expected)

        result = scalar_td * obj
        tm.assert_equal(result, expected)

    def test_numeric_arr_rdiv_tdscalar(self, three_days, numeric_idx, box_with_array):
        box = box_with_array

        index = numeric_idx[1:3]

        expected = TimedeltaIndex(["3 Days", "36 Hours"])
        if isinstance(three_days, np.timedelta64):
            dtype = three_days.dtype
            if dtype < np.dtype("m8[s]"):
                # i.e. resolution is lower -> use lowest supported resolution
                dtype = np.dtype("m8[s]")
            expected = expected.astype(dtype)
        elif type(three_days) is timedelta:
            expected = expected.astype("m8[us]")

        index = tm.box_expected(index, box)
        expected = tm.box_expected(expected, box)

        result = three_days / index
        tm.assert_equal(result, expected)

        msg = "cannot use operands with types dtype"
        with pytest.raises(TypeError, match=msg):
            index / three_days

    @pytest.mark.parametrize(
        "other",
        [
            Timedelta(hours=31),
            Timedelta(hours=31).to_pytimedelta(),
            Timedelta(hours=31).to_timedelta64(),
            Timedelta(hours=31).to_timedelta64().astype("m8[h]"),
            np.timedelta64("NaT"),
            np.timedelta64("NaT", "D"),
            pd.offsets.Minute(3),
            pd.offsets.Second(0),
            # GH#28080 numeric+datetimelike should raise; Timestamp used
            #  to raise NullFrequencyError but that behavior was removed in 1.0
            pd.Timestamp("2021-01-01", tz="Asia/Tokyo"),
            pd.Timestamp("2021-01-01"),
            pd.Timestamp("2021-01-01").to_pydatetime(),
            pd.Timestamp("2021-01-01", tz="UTC").to_pydatetime(),
            pd.Timestamp("2021-01-01").to_datetime64(),
            np.datetime64("NaT", "ns"),
            pd.NaT,
        ],
        ids=repr,
    )
    def test_add_sub_datetimedeltalike_invalid(
        self, numeric_idx, other, box_with_array
    ):
        box = box_with_array

        left = tm.box_expected(numeric_idx, box)
        msg = "|".join(
            [
                "unsupported operand type",
                "Addition/subtraction of integers and integer-arrays",
                "Instead of adding/subtracting",
                "cannot use operands with types dtype",
                "Concatenation operation is not implemented for NumPy arrays",
                "Cannot (add|subtract) NaT (to|from) ndarray",
                # pd.array vs np.datetime64 case
                r"operand type\(s\) all returned NotImplemented from __array_ufunc__",
                "can only perform ops with numeric values",
                "cannot subtract DatetimeArray from ndarray",
                # pd.Timedelta(1) + Index([0, 1, 2])
                "Cannot add or subtract Timedelta from integers",
            ]
        )
        assert_invalid_addsub_type(left, other, msg)


# ------------------------------------------------------------------
# Arithmetic


class TestDivisionByZero:
    def test_div_zero(self, zero, numeric_idx):
        idx = numeric_idx

        expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
        # We only adjust for Index, because Series does not yet apply
        #  the adjustment correctly.
        expected2 = adjust_negative_zero(zero, expected)

        result = idx / zero
        tm.assert_index_equal(result, expected2)
        ser_compat = Series(idx).astype("i8") / np.array(zero).astype("i8")
        tm.assert_series_equal(ser_compat, Series(expected))

    def test_floordiv_zero(self, zero, numeric_idx):
        idx = numeric_idx

        expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
        # We only adjust for Index, because Series does not yet apply
        #  the adjustment correctly.
        expected2 = adjust_negative_zero(zero, expected)

        result = idx // zero
        tm.assert_index_equal(result, expected2)
        ser_compat = Series(idx).astype("i8") // np.array(zero).astype("i8")
        tm.assert_series_equal(ser_compat, Series(expected))

    def test_mod_zero(self, zero, numeric_idx):
        idx = numeric_idx

        expected = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
        result = idx % zero
        tm.assert_index_equal(result, expected)
        ser_compat = Series(idx).astype("i8") % np.array(zero).astype("i8")
        tm.assert_series_equal(ser_compat, Series(result))

    def test_divmod_zero(self, zero, numeric_idx):
        idx = numeric_idx

        exleft = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
        exright = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
        exleft = adjust_negative_zero(zero, exleft)

        result = divmod(idx, zero)
        tm.assert_index_equal(result[0], exleft)
        tm.assert_index_equal(result[1], exright)

    @pytest.mark.parametrize("op", [operator.truediv, operator.floordiv])
    def test_div_negative_zero(self, zero, numeric_idx, op):
        # Check that -1 / -0.0 returns np.inf, not -np.inf
        if numeric_idx.dtype == np.uint64:
            pytest.skip(f"Not relevant for {numeric_idx.dtype}")
        idx = numeric_idx - 3

        expected = Index([-np.inf, -np.inf, -np.inf, np.nan, np.inf], dtype=np.float64)
        expected = adjust_negative_zero(zero, expected)

        result = op(idx, zero)
        tm.assert_index_equal(result, expected)

    # ------------------------------------------------------------------

    @pytest.mark.parametrize("dtype1", [np.int64, np.float64, np.uint64])
    def test_ser_div_ser(
        self,
        switch_numexpr_min_elements,
        dtype1,
        any_real_numpy_dtype,
    ):
        # no longer do integer div for any ops, but deal with the 0's
        dtype2 = any_real_numpy_dtype

        first = Series([3, 4, 5, 8], name="first").astype(dtype1)
        second = Series([0, 0, 0, 3], name="second").astype(dtype2)

        with np.errstate(all="ignore"):
            expected = Series(
                first.values.astype(np.float64) / second.values,
                dtype="float64",
                name=None,
            )
        expected.iloc[0:3] = np.inf
        if first.dtype == "int64" and second.dtype == "float32":
            # when using numexpr, the casting rules are slightly different
            # and int64/float32 combo results in float32 instead of float64
            if expr.USE_NUMEXPR and switch_numexpr_min_elements == 0:
                expected = expected.astype("float32")

        result = first / second
        tm.assert_series_equal(result, expected)
        assert not result.equals(second / first)

    @pytest.mark.parametrize("dtype1", [np.int64, np.float64, np.uint64])
    def test_ser_divmod_zero(self, dtype1, any_real_numpy_dtype):
        # GH#26987
        dtype2 = any_real_numpy_dtype
        left = Series([1, 1]).astype(dtype1)
        right = Series([0, 2]).astype(dtype2)

        # GH#27321 pandas convention is to set 1 // 0 to np.inf, as opposed
        #  to numpy which sets to np.nan; patch `expected[0]` below
        expected = left // right, left % right
        expected = list(expected)
        expected[0] = expected[0].astype(np.float64)
        expected[0][0] = np.inf
        result = divmod(left, right)

        tm.assert_series_equal(result[0], expected[0])
        tm.assert_series_equal(result[1], expected[1])

        # rdivmod case
        result = divmod(left.values, right)
        tm.assert_series_equal(result[0], expected[0])
        tm.assert_series_equal(result[1], expected[1])

    def test_ser_divmod_inf(self):
        left = Series([np.inf, 1.0])
        right = Series([np.inf, 2.0])

        expected = left // right, left % right
        result = divmod(left, right)

        tm.assert_series_equal(result[0], expected[0])
        tm.assert_series_equal(result[1], expected[1])

        # rdivmod case
        result = divmod(left.values, right)
        tm.assert_series_equal(result[0], expected[0])
        tm.assert_series_equal(result[1], expected[1])

    def test_rdiv_zero_compat(self):
        # GH#8674
        zero_array = np.array([0] * 5)
        data = np.random.default_rng(2).standard_normal(5)
        expected = Series([0.0] * 5)

        result = zero_array / Series(data)
        tm.assert_series_equal(result, expected)

        result = Series(zero_array) / data
        tm.assert_series_equal(result, expected)

        result = Series(zero_array) / Series(data)
        tm.assert_series_equal(result, expected)

    def test_div_zero_inf_signs(self):
        # GH#9144, inf signing
        ser = Series([-1, 0, 1], name="first")
        expected = Series([-np.inf, np.nan, np.inf], name="first")

        result = ser / 0
        tm.assert_series_equal(result, expected)

    def test_rdiv_zero(self):
        # GH#9144
        ser = Series([-1, 0, 1], name="first")
        expected = Series([0.0, np.nan, 0.0], name="first")

        result = 0 / ser
        tm.assert_series_equal(result, expected)

    def test_floordiv_div(self):
        # GH#9144
        ser = Series([-1, 0, 1], name="first")

        result = ser // 0
        expected = Series([-np.inf, np.nan, np.inf], name="first")
        tm.assert_series_equal(result, expected)

    def test_df_div_zero_df(self):
        # integer div, but deal with the 0's (GH#9144)
        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
        result = df / df

        first = Series([1.0, 1.0, 1.0, 1.0])
        second = Series([np.nan, np.nan, np.nan, 1])
        expected = pd.DataFrame({"first": first, "second": second})
        tm.assert_frame_equal(result, expected)

    def test_df_div_zero_array(self):
        # integer div, but deal with the 0's (GH#9144)
        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})

        first = Series([1.0, 1.0, 1.0, 1.0])
        second = Series([np.nan, np.nan, np.nan, 1])
        expected = pd.DataFrame({"first": first, "second": second})

        with np.errstate(all="ignore"):
            arr = df.values.astype("float") / df.values
        result = pd.DataFrame(arr, index=df.index, columns=df.columns)
        tm.assert_frame_equal(result, expected)

    def test_df_div_zero_int(self):
        # integer div, but deal with the 0's (GH#9144)
        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})

        result = df / 0
        expected = pd.DataFrame(np.inf, index=df.index, columns=df.columns)
        expected.iloc[0:3, 1] = np.nan
        tm.assert_frame_equal(result, expected)

        # numpy has a slightly different (wrong) treatment
        with np.errstate(all="ignore"):
            arr = df.values.astype("float64") / 0
        result2 = pd.DataFrame(arr, index=df.index, columns=df.columns)
        tm.assert_frame_equal(result2, expected)

    def test_df_div_zero_series_does_not_commute(self):
        # integer div, but deal with the 0's (GH#9144)
        df = pd.DataFrame(np.random.default_rng(2).standard_normal((10, 5)))
        ser = df[0]
        res = ser / df
        res2 = df / ser
        assert not res.fillna(0).equals(res2.fillna(0))

    # ------------------------------------------------------------------
    # Mod By Zero

    def test_df_mod_zero_df(self, using_array_manager):
        # GH#3590, modulo as ints
        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
        # this is technically wrong, as the integer portion is coerced to float
        first = Series([0, 0, 0, 0])
        if not using_array_manager:
            # INFO(ArrayManager) BlockManager doesn't preserve dtype per column
            # while ArrayManager performs op column-wisedoes and thus preserves
            # dtype if possible
            first = first.astype("float64")
        second = Series([np.nan, np.nan, np.nan, 0])
        expected = pd.DataFrame({"first": first, "second": second})
        result = df % df
        tm.assert_frame_equal(result, expected)

        # GH#38939 If we dont pass copy=False, df is consolidated and
        #  result["first"] is float64 instead of int64
        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}, copy=False)
        first = Series([0, 0, 0, 0], dtype="int64")
        second = Series([np.nan, np.nan, np.nan, 0])
        expected = pd.DataFrame({"first": first, "second": second})
        result = df % df
        tm.assert_frame_equal(result, expected)

    def test_df_mod_zero_array(self):
        # GH#3590, modulo as ints
        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})

        # this is technically wrong, as the integer portion is coerced to float
        # ###
        first = Series([0, 0, 0, 0], dtype="float64")
        second = Series([np.nan, np.nan, np.nan, 0])
        expected = pd.DataFrame({"first": first, "second": second})

        # numpy has a slightly different (wrong) treatment
        with np.errstate(all="ignore"):
            arr = df.values % df.values
        result2 = pd.DataFrame(arr, index=df.index, columns=df.columns, dtype="float64")
        result2.iloc[0:3, 1] = np.nan
        tm.assert_frame_equal(result2, expected)

    def test_df_mod_zero_int(self):
        # GH#3590, modulo as ints
        df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})

        result = df % 0
        expected = pd.DataFrame(np.nan, index=df.index, columns=df.columns)
        tm.assert_frame_equal(result, expected)

        # numpy has a slightly different (wrong) treatment
        with np.errstate(all="ignore"):
            arr = df.values.astype("float64") % 0
        result2 = pd.DataFrame(arr, index=df.index, columns=df.columns)
        tm.assert_frame_equal(result2, expected)

    def test_df_mod_zero_series_does_not_commute(self):
        # GH#3590, modulo as ints
        # not commutative with series
        df = pd.DataFrame(np.random.default_rng(2).standard_normal((10, 5)))
        ser = df[0]
        res = ser % df
        res2 = df % ser
        assert not res.fillna(0).equals(res2.fillna(0))


class TestMultiplicationDivision:
    # __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__
    # for non-timestamp/timedelta/period dtypes

    def test_divide_decimal(self, box_with_array):
        # resolves issue GH#9787
        box = box_with_array
        ser = Series([Decimal(10)])
        expected = Series([Decimal(5)])

        ser = tm.box_expected(ser, box)
        expected = tm.box_expected(expected, box)

        result = ser / Decimal(2)

        tm.assert_equal(result, expected)

        result = ser // Decimal(2)
        tm.assert_equal(result, expected)

    def test_div_equiv_binop(self):
        # Test Series.div as well as Series.__div__
        # float/integer issue
        # GH#7785
        first = Series([1, 0], name="first")
        second = Series([-0.01, -0.02], name="second")
        expected = Series([-0.01, -np.inf])

        result = second.div(first)
        tm.assert_series_equal(result, expected, check_names=False)

        result = second / first
        tm.assert_series_equal(result, expected)

    def test_div_int(self, numeric_idx):
        idx = numeric_idx
        result = idx / 1
        expected = idx.astype("float64")
        tm.assert_index_equal(result, expected)

        result = idx / 2
        expected = Index(idx.values / 2)
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("op", [operator.mul, ops.rmul, operator.floordiv])
    def test_mul_int_identity(self, op, numeric_idx, box_with_array):
        idx = numeric_idx
        idx = tm.box_expected(idx, box_with_array)

        result = op(idx, 1)
        tm.assert_equal(result, idx)

    def test_mul_int_array(self, numeric_idx):
        idx = numeric_idx
        didx = idx * idx

        result = idx * np.array(5, dtype="int64")
        tm.assert_index_equal(result, idx * 5)

        arr_dtype = "uint64" if idx.dtype == np.uint64 else "int64"
        result = idx * np.arange(5, dtype=arr_dtype)
        tm.assert_index_equal(result, didx)

    def test_mul_int_series(self, numeric_idx):
        idx = numeric_idx
        didx = idx * idx

        arr_dtype = "uint64" if idx.dtype == np.uint64 else "int64"
        result = idx * Series(np.arange(5, dtype=arr_dtype))
        tm.assert_series_equal(result, Series(didx))

    def test_mul_float_series(self, numeric_idx):
        idx = numeric_idx
        rng5 = np.arange(5, dtype="float64")

        result = idx * Series(rng5 + 0.1)
        expected = Series(rng5 * (rng5 + 0.1))
        tm.assert_series_equal(result, expected)

    def test_mul_index(self, numeric_idx):
        idx = numeric_idx

        result = idx * idx
        tm.assert_index_equal(result, idx**2)

    def test_mul_datelike_raises(self, numeric_idx):
        idx = numeric_idx
        msg = "cannot perform __rmul__ with this index type"
        with pytest.raises(TypeError, match=msg):
            idx * pd.date_range("20130101", periods=5)

    def test_mul_size_mismatch_raises(self, numeric_idx):
        idx = numeric_idx
        msg = "operands could not be broadcast together"
        with pytest.raises(ValueError, match=msg):
            idx * idx[0:3]
        with pytest.raises(ValueError, match=msg):
            idx * np.array([1, 2])

    @pytest.mark.parametrize("op", [operator.pow, ops.rpow])
    def test_pow_float(self, op, numeric_idx, box_with_array):
        # test power calculations both ways, GH#14973
        box = box_with_array
        idx = numeric_idx
        expected = Index(op(idx.values, 2.0))

        idx = tm.box_expected(idx, box)
        expected = tm.box_expected(expected, box)

        result = op(idx, 2.0)
        tm.assert_equal(result, expected)

    def test_modulo(self, numeric_idx, box_with_array):
        # GH#9244
        box = box_with_array
        idx = numeric_idx
        expected = Index(idx.values % 2)

        idx = tm.box_expected(idx, box)
        expected = tm.box_expected(expected, box)

        result = idx % 2
        tm.assert_equal(result, expected)

    def test_divmod_scalar(self, numeric_idx):
        idx = numeric_idx

        result = divmod(idx, 2)
        with np.errstate(all="ignore"):
            div, mod = divmod(idx.values, 2)

        expected = Index(div), Index(mod)
        for r, e in zip(result, expected):
            tm.assert_index_equal(r, e)

    def test_divmod_ndarray(self, numeric_idx):
        idx = numeric_idx
        other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2

        result = divmod(idx, other)
        with np.errstate(all="ignore"):
            div, mod = divmod(idx.values, other)

        expected = Index(div), Index(mod)
        for r, e in zip(result, expected):
            tm.assert_index_equal(r, e)

    def test_divmod_series(self, numeric_idx):
        idx = numeric_idx
        other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2

        result = divmod(idx, Series(other))
        with np.errstate(all="ignore"):
            div, mod = divmod(idx.values, other)

        expected = Series(div), Series(mod)
        for r, e in zip(result, expected):
            tm.assert_series_equal(r, e)

    @pytest.mark.parametrize("other", [np.nan, 7, -23, 2.718, -3.14, np.inf])
    def test_ops_np_scalar(self, other):
        vals = np.random.default_rng(2).standard_normal((5, 3))
        f = lambda x: pd.DataFrame(
            x, index=list("ABCDE"), columns=["jim", "joe", "jolie"]
        )

        df = f(vals)

        tm.assert_frame_equal(df / np.array(other), f(vals / other))
        tm.assert_frame_equal(np.array(other) * df, f(vals * other))
        tm.assert_frame_equal(df + np.array(other), f(vals + other))
        tm.assert_frame_equal(np.array(other) - df, f(other - vals))

    # TODO: This came from series.test.test_operators, needs cleanup
    def test_operators_frame(self):
        # rpow does not work with DataFrame
        ts = tm.makeTimeSeries()
        ts.name = "ts"

        df = pd.DataFrame({"A": ts})

        tm.assert_series_equal(ts + ts, ts + df["A"], check_names=False)
        tm.assert_series_equal(ts**ts, ts ** df["A"], check_names=False)
        tm.assert_series_equal(ts < ts, ts < df["A"], check_names=False)
        tm.assert_series_equal(ts / ts, ts / df["A"], check_names=False)

    # TODO: this came from tests.series.test_analytics, needs cleanup and
    #  de-duplication with test_modulo above
    def test_modulo2(self):
        with np.errstate(all="ignore"):
            # GH#3590, modulo as ints
            p = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]})
            result = p["first"] % p["second"]
            expected = Series(p["first"].values % p["second"].values, dtype="float64")
            expected.iloc[0:3] = np.nan
            tm.assert_series_equal(result, expected)

            result = p["first"] % 0
            expected = Series(np.nan, index=p.index, name="first")
            tm.assert_series_equal(result, expected)

            p = p.astype("float64")
            result = p["first"] % p["second"]
            expected = Series(p["first"].values % p["second"].values)
            tm.assert_series_equal(result, expected)

            p = p.astype("float64")
            result = p["first"] % p["second"]
            result2 = p["second"] % p["first"]
            assert not result.equals(result2)

    def test_modulo_zero_int(self):
        # GH#9144
        with np.errstate(all="ignore"):
            s = Series([0, 1])

            result = s % 0
            expected = Series([np.nan, np.nan])
            tm.assert_series_equal(result, expected)

            result = 0 % s
            expected = Series([np.nan, 0.0])
            tm.assert_series_equal(result, expected)


class TestAdditionSubtraction:
    # __add__, __sub__, __radd__, __rsub__, __iadd__, __isub__
    # for non-timestamp/timedelta/period dtypes

    @pytest.mark.parametrize(
        "first, second, expected",
        [
            (
                Series([1, 2, 3], index=list("ABC"), name="x"),
                Series([2, 2, 2], index=list("ABD"), name="x"),
                Series([3.0, 4.0, np.nan, np.nan], index=list("ABCD"), name="x"),
            ),
            (
                Series([1, 2, 3], index=list("ABC"), name="x"),
                Series([2, 2, 2, 2], index=list("ABCD"), name="x"),
                Series([3, 4, 5, np.nan], index=list("ABCD"), name="x"),
            ),
        ],
    )
    def test_add_series(self, first, second, expected):
        # GH#1134
        tm.assert_series_equal(first + second, expected)
        tm.assert_series_equal(second + first, expected)

    @pytest.mark.parametrize(
        "first, second, expected",
        [
            (
                pd.DataFrame({"x": [1, 2, 3]}, index=list("ABC")),
                pd.DataFrame({"x": [2, 2, 2]}, index=list("ABD")),
                pd.DataFrame({"x": [3.0, 4.0, np.nan, np.nan]}, index=list("ABCD")),
            ),
            (
                pd.DataFrame({"x": [1, 2, 3]}, index=list("ABC")),
                pd.DataFrame({"x": [2, 2, 2, 2]}, index=list("ABCD")),
                pd.DataFrame({"x": [3, 4, 5, np.nan]}, index=list("ABCD")),
            ),
        ],
    )
    def test_add_frames(self, first, second, expected):
        # GH#1134
        tm.assert_frame_equal(first + second, expected)
        tm.assert_frame_equal(second + first, expected)

    # TODO: This came from series.test.test_operators, needs cleanup
    def test_series_frame_radd_bug(self, fixed_now_ts):
        # GH#353
        vals = Series(tm.makeStringIndex())
        result = "foo_" + vals
        expected = vals.map(lambda x: "foo_" + x)
        tm.assert_series_equal(result, expected)

        frame = pd.DataFrame({"vals": vals})
        result = "foo_" + frame
        expected = pd.DataFrame({"vals": vals.map(lambda x: "foo_" + x)})
        tm.assert_frame_equal(result, expected)

        ts = tm.makeTimeSeries()
        ts.name = "ts"

        # really raise this time
        fix_now = fixed_now_ts.to_pydatetime()
        msg = "|".join(
            [
                "unsupported operand type",
                # wrong error message, see https://github.com/numpy/numpy/issues/18832
                "Concatenation operation",
            ]
        )
        with pytest.raises(TypeError, match=msg):
            fix_now + ts

        with pytest.raises(TypeError, match=msg):
            ts + fix_now

    # TODO: This came from series.test.test_operators, needs cleanup
    def test_datetime64_with_index(self):
        # arithmetic integer ops with an index
        ser = Series(np.random.default_rng(2).standard_normal(5))
        expected = ser - ser.index.to_series()
        result = ser - ser.index
        tm.assert_series_equal(result, expected)

        # GH#4629
        # arithmetic datetime64 ops with an index
        ser = Series(
            pd.date_range("20130101", periods=5),
            index=pd.date_range("20130101", periods=5),
        )
        expected = ser - ser.index.to_series()
        result = ser - ser.index
        tm.assert_series_equal(result, expected)

        msg = "cannot subtract PeriodArray from DatetimeArray"
        with pytest.raises(TypeError, match=msg):
            # GH#18850
            result = ser - ser.index.to_period()

        df = pd.DataFrame(
            np.random.default_rng(2).standard_normal((5, 2)),
            index=pd.date_range("20130101", periods=5),
        )
        df["date"] = pd.Timestamp("20130102")
        df["expected"] = df["date"] - df.index.to_series()
        df["result"] = df["date"] - df.index
        tm.assert_series_equal(df["result"], df["expected"], check_names=False)

    # TODO: taken from tests.frame.test_operators, needs cleanup
    def test_frame_operators(self, float_frame):
        frame = float_frame

        garbage = np.random.default_rng(2).random(4)
        colSeries = Series(garbage, index=np.array(frame.columns))

        idSum = frame + frame
        seriesSum = frame + colSeries

        for col, series in idSum.items():
            for idx, val in series.items():
                origVal = frame[col][idx] * 2
                if not np.isnan(val):
                    assert val == origVal
                else:
                    assert np.isnan(origVal)

        for col, series in seriesSum.items():
            for idx, val in series.items():
                origVal = frame[col][idx] + colSeries[col]
                if not np.isnan(val):
                    assert val == origVal
                else:
                    assert np.isnan(origVal)

    def test_frame_operators_col_align(self, float_frame):
        frame2 = pd.DataFrame(float_frame, columns=["D", "C", "B", "A"])
        added = frame2 + frame2
        expected = frame2 * 2
        tm.assert_frame_equal(added, expected)

    def test_frame_operators_none_to_nan(self):
        df = pd.DataFrame({"a": ["a", None, "b"]})
        tm.assert_frame_equal(df + df, pd.DataFrame({"a": ["aa", np.nan, "bb"]}))

    @pytest.mark.parametrize("dtype", ("float", "int64"))
    def test_frame_operators_empty_like(self, dtype):
        # Test for issue #10181
        frames = [
            pd.DataFrame(dtype=dtype),
            pd.DataFrame(columns=["A"], dtype=dtype),
            pd.DataFrame(index=[0], dtype=dtype),
        ]
        for df in frames:
            assert (df + df).equals(df)
            tm.assert_frame_equal(df + df, df)

    @pytest.mark.parametrize(
        "func",
        [lambda x: x * 2, lambda x: x[::2], lambda x: 5],
        ids=["multiply", "slice", "constant"],
    )
    def test_series_operators_arithmetic(self, all_arithmetic_functions, func):
        op = all_arithmetic_functions
        series = tm.makeTimeSeries().rename("ts")
        other = func(series)
        compare_op(series, other, op)

    @pytest.mark.parametrize(
        "func", [lambda x: x + 1, lambda x: 5], ids=["add", "constant"]
    )
    def test_series_operators_compare(self, comparison_op, func):
        op = comparison_op
        series = tm.makeTimeSeries().rename("ts")
        other = func(series)
        compare_op(series, other, op)

    @pytest.mark.parametrize(
        "func",
        [lambda x: x * 2, lambda x: x[::2], lambda x: 5],
        ids=["multiply", "slice", "constant"],
    )
    def test_divmod(self, func):
        series = tm.makeTimeSeries().rename("ts")
        other = func(series)
        results = divmod(series, other)
        if isinstance(other, abc.Iterable) and len(series) != len(other):
            # if the lengths don't match, this is the test where we use
            # `tser[::2]`. Pad every other value in `other_np` with nan.
            other_np = []
            for n in other:
                other_np.append(n)
                other_np.append(np.nan)
        else:
            other_np = other
        other_np = np.asarray(other_np)
        with np.errstate(all="ignore"):
            expecteds = divmod(series.values, np.asarray(other_np))

        for result, expected in zip(results, expecteds):
            # check the values, name, and index separately
            tm.assert_almost_equal(np.asarray(result), expected)

            assert result.name == series.name
            tm.assert_index_equal(result.index, series.index._with_freq(None))

    def test_series_divmod_zero(self):
        # Check that divmod uses pandas convention for division by zero,
        #  which does not match numpy.
        # pandas convention has
        #  1/0 == np.inf
        #  -1/0 == -np.inf
        #  1/-0.0 == -np.inf
        #  -1/-0.0 == np.inf
        tser = tm.makeTimeSeries().rename("ts")
        other = tser * 0

        result = divmod(tser, other)
        exp1 = Series([np.inf] * len(tser), index=tser.index, name="ts")
        exp2 = Series([np.nan] * len(tser), index=tser.index, name="ts")
        tm.assert_series_equal(result[0], exp1)
        tm.assert_series_equal(result[1], exp2)


class TestUFuncCompat:
    # TODO: add more dtypes
    @pytest.mark.parametrize("holder", [Index, RangeIndex, Series])
    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
    def test_ufunc_compat(self, holder, dtype):
        box = Series if holder is Series else Index

        if holder is RangeIndex:
            if dtype != np.int64:
                pytest.skip(f"dtype {dtype} not relevant for RangeIndex")
            idx = RangeIndex(0, 5, name="foo")
        else:
            idx = holder(np.arange(5, dtype=dtype), name="foo")
        result = np.sin(idx)
        expected = box(np.sin(np.arange(5, dtype=dtype)), name="foo")
        tm.assert_equal(result, expected)

    # TODO: add more dtypes
    @pytest.mark.parametrize("holder", [Index, Series])
    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
    def test_ufunc_coercions(self, holder, dtype):
        idx = holder([1, 2, 3, 4, 5], dtype=dtype, name="x")
        box = Series if holder is Series else Index

        result = np.sqrt(idx)
        assert result.dtype == "f8" and isinstance(result, box)
        exp = Index(np.sqrt(np.array([1, 2, 3, 4, 5], dtype=np.float64)), name="x")
        exp = tm.box_expected(exp, box)
        tm.assert_equal(result, exp)

        result = np.divide(idx, 2.0)
        assert result.dtype == "f8" and isinstance(result, box)
        exp = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=np.float64, name="x")
        exp = tm.box_expected(exp, box)
        tm.assert_equal(result, exp)

        # _evaluate_numeric_binop
        result = idx + 2.0
        assert result.dtype == "f8" and isinstance(result, box)
        exp = Index([3.0, 4.0, 5.0, 6.0, 7.0], dtype=np.float64, name="x")
        exp = tm.box_expected(exp, box)
        tm.assert_equal(result, exp)

        result = idx - 2.0
        assert result.dtype == "f8" and isinstance(result, box)
        exp = Index([-1.0, 0.0, 1.0, 2.0, 3.0], dtype=np.float64, name="x")
        exp = tm.box_expected(exp, box)
        tm.assert_equal(result, exp)

        result = idx * 1.0
        assert result.dtype == "f8" and isinstance(result, box)
        exp = Index([1.0, 2.0, 3.0, 4.0, 5.0], dtype=np.float64, name="x")
        exp = tm.box_expected(exp, box)
        tm.assert_equal(result, exp)

        result = idx / 2.0
        assert result.dtype == "f8" and isinstance(result, box)
        exp = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=np.float64, name="x")
        exp = tm.box_expected(exp, box)
        tm.assert_equal(result, exp)

    # TODO: add more dtypes
    @pytest.mark.parametrize("holder", [Index, Series])
    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
    def test_ufunc_multiple_return_values(self, holder, dtype):
        obj = holder([1, 2, 3], dtype=dtype, name="x")
        box = Series if holder is Series else Index

        result = np.modf(obj)
        assert isinstance(result, tuple)
        exp1 = Index([0.0, 0.0, 0.0], dtype=np.float64, name="x")
        exp2 = Index([1.0, 2.0, 3.0], dtype=np.float64, name="x")
        tm.assert_equal(result[0], tm.box_expected(exp1, box))
        tm.assert_equal(result[1], tm.box_expected(exp2, box))

    def test_ufunc_at(self):
        s = Series([0, 1, 2], index=[1, 2, 3], name="x")
        np.add.at(s, [0, 2], 10)
        expected = Series([10, 1, 12], index=[1, 2, 3], name="x")
        tm.assert_series_equal(s, expected)


class TestObjectDtypeEquivalence:
    # Tests that arithmetic operations match operations executed elementwise

    @pytest.mark.parametrize("dtype", [None, object])
    def test_numarr_with_dtype_add_nan(self, dtype, box_with_array):
        box = box_with_array
        ser = Series([1, 2, 3], dtype=dtype)
        expected = Series([np.nan, np.nan, np.nan], dtype=dtype)

        ser = tm.box_expected(ser, box)
        expected = tm.box_expected(expected, box)

        result = np.nan + ser
        tm.assert_equal(result, expected)

        result = ser + np.nan
        tm.assert_equal(result, expected)

    @pytest.mark.parametrize("dtype", [None, object])
    def test_numarr_with_dtype_add_int(self, dtype, box_with_array):
        box = box_with_array
        ser = Series([1, 2, 3], dtype=dtype)
        expected = Series([2, 3, 4], dtype=dtype)

        ser = tm.box_expected(ser, box)
        expected = tm.box_expected(expected, box)

        result = 1 + ser
        tm.assert_equal(result, expected)

        result = ser + 1
        tm.assert_equal(result, expected)

    # TODO: moved from tests.series.test_operators; needs cleanup
    @pytest.mark.parametrize(
        "op",
        [operator.add, operator.sub, operator.mul, operator.truediv, operator.floordiv],
    )
    def test_operators_reverse_object(self, op):
        # GH#56
        arr = Series(
            np.random.default_rng(2).standard_normal(10),
            index=np.arange(10),
            dtype=object,
        )

        result = op(1.0, arr)
        expected = op(1.0, arr.astype(float))
        tm.assert_series_equal(result.astype(float), expected)


class TestNumericArithmeticUnsorted:
    # Tests in this class have been moved from type-specific test modules
    #  but not yet sorted, parametrized, and de-duplicated
    @pytest.mark.parametrize(
        "op",
        [
            operator.add,
            operator.sub,
            operator.mul,
            operator.floordiv,
            operator.truediv,
        ],
    )
    @pytest.mark.parametrize(
        "idx1",
        [
            RangeIndex(0, 10, 1),
            RangeIndex(0, 20, 2),
            RangeIndex(-10, 10, 2),
            RangeIndex(5, -5, -1),
        ],
    )
    @pytest.mark.parametrize(
        "idx2",
        [
            RangeIndex(0, 10, 1),
            RangeIndex(0, 20, 2),
            RangeIndex(-10, 10, 2),
            RangeIndex(5, -5, -1),
        ],
    )
    def test_binops_index(self, op, idx1, idx2):
        idx1 = idx1._rename("foo")
        idx2 = idx2._rename("bar")
        result = op(idx1, idx2)
        expected = op(Index(idx1.to_numpy()), Index(idx2.to_numpy()))
        tm.assert_index_equal(result, expected, exact="equiv")

    @pytest.mark.parametrize(
        "op",
        [
            operator.add,
            operator.sub,
            operator.mul,
            operator.floordiv,
            operator.truediv,
        ],
    )
    @pytest.mark.parametrize(
        "idx",
        [
            RangeIndex(0, 10, 1),
            RangeIndex(0, 20, 2),
            RangeIndex(-10, 10, 2),
            RangeIndex(5, -5, -1),
        ],
    )
    @pytest.mark.parametrize("scalar", [-1, 1, 2])
    def test_binops_index_scalar(self, op, idx, scalar):
        result = op(idx, scalar)
        expected = op(Index(idx.to_numpy()), scalar)
        tm.assert_index_equal(result, expected, exact="equiv")

    @pytest.mark.parametrize("idx1", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)])
    @pytest.mark.parametrize("idx2", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)])
    def test_binops_index_pow(self, idx1, idx2):
        # numpy does not allow powers of negative integers so test separately
        # https://github.com/numpy/numpy/pull/8127
        idx1 = idx1._rename("foo")
        idx2 = idx2._rename("bar")
        result = pow(idx1, idx2)
        expected = pow(Index(idx1.to_numpy()), Index(idx2.to_numpy()))
        tm.assert_index_equal(result, expected, exact="equiv")

    @pytest.mark.parametrize("idx", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)])
    @pytest.mark.parametrize("scalar", [1, 2])
    def test_binops_index_scalar_pow(self, idx, scalar):
        # numpy does not allow powers of negative integers so test separately
        # https://github.com/numpy/numpy/pull/8127
        result = pow(idx, scalar)
        expected = pow(Index(idx.to_numpy()), scalar)
        tm.assert_index_equal(result, expected, exact="equiv")

    # TODO: divmod?
    @pytest.mark.parametrize(
        "op",
        [
            operator.add,
            operator.sub,
            operator.mul,
            operator.floordiv,
            operator.truediv,
            operator.pow,
            operator.mod,
        ],
    )
    def test_arithmetic_with_frame_or_series(self, op):
        # check that we return NotImplemented when operating with Series
        # or DataFrame
        index = RangeIndex(5)
        other = Series(np.random.default_rng(2).standard_normal(5))

        expected = op(Series(index), other)
        result = op(index, other)
        tm.assert_series_equal(result, expected)

        other = pd.DataFrame(np.random.default_rng(2).standard_normal((2, 5)))
        expected = op(pd.DataFrame([index, index]), other)
        result = op(index, other)
        tm.assert_frame_equal(result, expected)

    def test_numeric_compat2(self):
        # validate that we are handling the RangeIndex overrides to numeric ops
        # and returning RangeIndex where possible

        idx = RangeIndex(0, 10, 2)

        result = idx * 2
        expected = RangeIndex(0, 20, 4)
        tm.assert_index_equal(result, expected, exact=True)

        result = idx + 2
        expected = RangeIndex(2, 12, 2)
        tm.assert_index_equal(result, expected, exact=True)

        result = idx - 2
        expected = RangeIndex(-2, 8, 2)
        tm.assert_index_equal(result, expected, exact=True)

        result = idx / 2
        expected = RangeIndex(0, 5, 1).astype("float64")
        tm.assert_index_equal(result, expected, exact=True)

        result = idx / 4
        expected = RangeIndex(0, 10, 2) / 4
        tm.assert_index_equal(result, expected, exact=True)

        result = idx // 1
        expected = idx
        tm.assert_index_equal(result, expected, exact=True)

        # __mul__
        result = idx * idx
        expected = Index(idx.values * idx.values)
        tm.assert_index_equal(result, expected, exact=True)

        # __pow__
        idx = RangeIndex(0, 1000, 2)
        result = idx**2
        expected = Index(idx._values) ** 2
        tm.assert_index_equal(Index(result.values), expected, exact=True)

    @pytest.mark.parametrize(
        "idx, div, expected",
        [
            # TODO: add more dtypes
            (RangeIndex(0, 1000, 2), 2, RangeIndex(0, 500, 1)),
            (RangeIndex(-99, -201, -3), -3, RangeIndex(33, 67, 1)),
            (
                RangeIndex(0, 1000, 1),
                2,
                Index(RangeIndex(0, 1000, 1)._values) // 2,
            ),
            (
                RangeIndex(0, 100, 1),
                2.0,
                Index(RangeIndex(0, 100, 1)._values) // 2.0,
            ),
            (RangeIndex(0), 50, RangeIndex(0)),
            (RangeIndex(2, 4, 2), 3, RangeIndex(0, 1, 1)),
            (RangeIndex(-5, -10, -6), 4, RangeIndex(-2, -1, 1)),
            (RangeIndex(-100, -200, 3), 2, RangeIndex(0)),
        ],
    )
    def test_numeric_compat2_floordiv(self, idx, div, expected):
        # __floordiv__
        tm.assert_index_equal(idx // div, expected, exact=True)

    @pytest.mark.parametrize("dtype", [np.int64, np.float64])
    @pytest.mark.parametrize("delta", [1, 0, -1])
    def test_addsub_arithmetic(self, dtype, delta):
        # GH#8142
        delta = dtype(delta)
        index = Index([10, 11, 12], dtype=dtype)
        result = index + delta
        expected = Index(index.values + delta, dtype=dtype)
        tm.assert_index_equal(result, expected)

        # this subtraction used to fail
        result = index - delta
        expected = Index(index.values - delta, dtype=dtype)
        tm.assert_index_equal(result, expected)

        tm.assert_index_equal(index + index, 2 * index)
        tm.assert_index_equal(index - index, 0 * index)
        assert not (index - index).empty


def test_fill_value_inf_masking():
    # GH #27464 make sure we mask 0/1 with Inf and not NaN
    df = pd.DataFrame({"A": [0, 1, 2], "B": [1.1, None, 1.1]})

    other = pd.DataFrame({"A": [1.1, 1.2, 1.3]}, index=[0, 2, 3])

    result = df.rfloordiv(other, fill_value=1)

    expected = pd.DataFrame(
        {"A": [np.inf, 1.0, 0.0, 1.0], "B": [0.0, np.nan, 0.0, np.nan]}
    )
    tm.assert_frame_equal(result, expected)


def test_dataframe_div_silenced():
    # GH#26793
    pdf1 = pd.DataFrame(
        {
            "A": np.arange(10),
            "B": [np.nan, 1, 2, 3, 4] * 2,
            "C": [np.nan] * 10,
            "D": np.arange(10),
        },
        index=list("abcdefghij"),
        columns=list("ABCD"),
    )
    pdf2 = pd.DataFrame(
        np.random.default_rng(2).standard_normal((10, 4)),
        index=list("abcdefghjk"),
        columns=list("ABCX"),
    )
    with tm.assert_produces_warning(None):
        pdf1.div(pdf2, fill_value=0)


@pytest.mark.parametrize(
    "data, expected_data",
    [([0, 1, 2], [0, 2, 4])],
)
def test_integer_array_add_list_like(
    box_pandas_1d_array, box_1d_array, data, expected_data
):
    # GH22606 Verify operators with IntegerArray and list-likes
    arr = array(data, dtype="Int64")
    container = box_pandas_1d_array(arr)
    left = container + box_1d_array(data)
    right = box_1d_array(data) + container

    if Series in [box_1d_array, box_pandas_1d_array]:
        cls = Series
    elif Index in [box_1d_array, box_pandas_1d_array]:
        cls = Index
    else:
        cls = array

    expected = cls(expected_data, dtype="Int64")

    tm.assert_equal(left, expected)
    tm.assert_equal(right, expected)


def test_sub_multiindex_swapped_levels():
    # GH 9952
    df = pd.DataFrame(
        {"a": np.random.default_rng(2).standard_normal(6)},
        index=pd.MultiIndex.from_product(
            [["a", "b"], [0, 1, 2]], names=["levA", "levB"]
        ),
    )
    df2 = df.copy()
    df2.index = df2.index.swaplevel(0, 1)
    result = df - df2
    expected = pd.DataFrame([0.0] * 6, columns=["a"], index=df.index)
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("power", [1, 2, 5])
@pytest.mark.parametrize("string_size", [0, 1, 2, 5])
def test_empty_str_comparison(power, string_size):
    # GH 37348
    a = np.array(range(10**power))
    right = pd.DataFrame(a, dtype=np.int64)
    left = " " * string_size

    result = right == left
    expected = pd.DataFrame(np.zeros(right.shape, dtype=bool))
    tm.assert_frame_equal(result, expected)


def test_series_add_sub_with_UInt64():
    # GH 22023
    series1 = Series([1, 2, 3])
    series2 = Series([2, 1, 3], dtype="UInt64")

    result = series1 + series2
    expected = Series([3, 3, 6], dtype="Float64")
    tm.assert_series_equal(result, expected)

    result = series1 - series2
    expected = Series([-1, 1, 0], dtype="Float64")
    tm.assert_series_equal(result, expected)