from collections import deque
from datetime import datetime
import operator
import re

import numpy as np
import pytest
import pytz

import pandas as pd
from pandas import DataFrame, MultiIndex, Series
import pandas._testing as tm
import pandas.core.common as com
from pandas.tests.frame.common import _check_mixed_float, _check_mixed_int

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


class TestFrameComparisons:
    # Specifically _not_ flex-comparisons

    def test_frame_in_list(self):
        # GH#12689 this should raise at the DataFrame level, not blocks
        df = pd.DataFrame(np.random.randn(6, 4), columns=list("ABCD"))
        msg = "The truth value of a DataFrame is ambiguous"
        with pytest.raises(ValueError, match=msg):
            df in [None]

    def test_comparison_invalid(self):
        def check(df, df2):

            for (x, y) in [(df, df2), (df2, df)]:
                # we expect the result to match Series comparisons for
                # == and !=, inequalities should raise
                result = x == y
                expected = pd.DataFrame(
                    {col: x[col] == y[col] for col in x.columns},
                    index=x.index,
                    columns=x.columns,
                )
                tm.assert_frame_equal(result, expected)

                result = x != y
                expected = pd.DataFrame(
                    {col: x[col] != y[col] for col in x.columns},
                    index=x.index,
                    columns=x.columns,
                )
                tm.assert_frame_equal(result, expected)

                msgs = [
                    r"Invalid comparison between dtype=datetime64\[ns\] and ndarray",
                    "invalid type promotion",
                ]
                msg = "|".join(msgs)
                with pytest.raises(TypeError, match=msg):
                    x >= y
                with pytest.raises(TypeError, match=msg):
                    x > y
                with pytest.raises(TypeError, match=msg):
                    x < y
                with pytest.raises(TypeError, match=msg):
                    x <= y

        # GH4968
        # invalid date/int comparisons
        df = pd.DataFrame(np.random.randint(10, size=(10, 1)), columns=["a"])
        df["dates"] = pd.date_range("20010101", periods=len(df))

        df2 = df.copy()
        df2["dates"] = df["a"]
        check(df, df2)

        df = pd.DataFrame(np.random.randint(10, size=(10, 2)), columns=["a", "b"])
        df2 = pd.DataFrame(
            {
                "a": pd.date_range("20010101", periods=len(df)),
                "b": pd.date_range("20100101", periods=len(df)),
            }
        )
        check(df, df2)

    def test_timestamp_compare(self):
        # make sure we can compare Timestamps on the right AND left hand side
        # GH#4982
        df = pd.DataFrame(
            {
                "dates1": pd.date_range("20010101", periods=10),
                "dates2": pd.date_range("20010102", periods=10),
                "intcol": np.random.randint(1000000000, size=10),
                "floatcol": np.random.randn(10),
                "stringcol": list(tm.rands(10)),
            }
        )
        df.loc[np.random.rand(len(df)) > 0.5, "dates2"] = pd.NaT
        ops = {"gt": "lt", "lt": "gt", "ge": "le", "le": "ge", "eq": "eq", "ne": "ne"}

        for left, right in ops.items():
            left_f = getattr(operator, left)
            right_f = getattr(operator, right)

            # no nats
            if left in ["eq", "ne"]:
                expected = left_f(df, pd.Timestamp("20010109"))
                result = right_f(pd.Timestamp("20010109"), df)
                tm.assert_frame_equal(result, expected)
            else:
                msg = (
                    "'(<|>)=?' not supported between "
                    "instances of 'numpy.ndarray' and 'Timestamp'"
                )
                with pytest.raises(TypeError, match=msg):
                    left_f(df, pd.Timestamp("20010109"))
                with pytest.raises(TypeError, match=msg):
                    right_f(pd.Timestamp("20010109"), df)
            # nats
            expected = left_f(df, pd.Timestamp("nat"))
            result = right_f(pd.Timestamp("nat"), df)
            tm.assert_frame_equal(result, expected)

    def test_mixed_comparison(self):
        # GH#13128, GH#22163 != datetime64 vs non-dt64 should be False,
        # not raise TypeError
        # (this appears to be fixed before GH#22163, not sure when)
        df = pd.DataFrame([["1989-08-01", 1], ["1989-08-01", 2]])
        other = pd.DataFrame([["a", "b"], ["c", "d"]])

        result = df == other
        assert not result.any().any()

        result = df != other
        assert result.all().all()

    def test_df_boolean_comparison_error(self):
        # GH#4576, GH#22880
        # comparing DataFrame against list/tuple with len(obj) matching
        #  len(df.columns) is supported as of GH#22800
        df = pd.DataFrame(np.arange(6).reshape((3, 2)))

        expected = pd.DataFrame([[False, False], [True, False], [False, False]])

        result = df == (2, 2)
        tm.assert_frame_equal(result, expected)

        result = df == [2, 2]
        tm.assert_frame_equal(result, expected)

    def test_df_float_none_comparison(self):
        df = pd.DataFrame(
            np.random.randn(8, 3), index=range(8), columns=["A", "B", "C"]
        )

        result = df.__eq__(None)
        assert not result.any().any()

    def test_df_string_comparison(self):
        df = pd.DataFrame([{"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}])
        mask_a = df.a > 1
        tm.assert_frame_equal(df[mask_a], df.loc[1:1, :])
        tm.assert_frame_equal(df[-mask_a], df.loc[0:0, :])

        mask_b = df.b == "foo"
        tm.assert_frame_equal(df[mask_b], df.loc[0:0, :])
        tm.assert_frame_equal(df[-mask_b], df.loc[1:1, :])


class TestFrameFlexComparisons:
    # TODO: test_bool_flex_frame needs a better name
    def test_bool_flex_frame(self):
        data = np.random.randn(5, 3)
        other_data = np.random.randn(5, 3)
        df = pd.DataFrame(data)
        other = pd.DataFrame(other_data)
        ndim_5 = np.ones(df.shape + (1, 3))

        # Unaligned
        def _check_unaligned_frame(meth, op, df, other):
            part_o = other.loc[3:, 1:].copy()
            rs = meth(part_o)
            xp = op(df, part_o.reindex(index=df.index, columns=df.columns))
            tm.assert_frame_equal(rs, xp)

        # DataFrame
        assert df.eq(df).values.all()
        assert not df.ne(df).values.any()
        for op in ["eq", "ne", "gt", "lt", "ge", "le"]:
            f = getattr(df, op)
            o = getattr(operator, op)
            # No NAs
            tm.assert_frame_equal(f(other), o(df, other))
            _check_unaligned_frame(f, o, df, other)
            # ndarray
            tm.assert_frame_equal(f(other.values), o(df, other.values))
            # scalar
            tm.assert_frame_equal(f(0), o(df, 0))
            # NAs
            msg = "Unable to coerce to Series/DataFrame"
            tm.assert_frame_equal(f(np.nan), o(df, np.nan))
            with pytest.raises(ValueError, match=msg):
                f(ndim_5)

        # Series
        def _test_seq(df, idx_ser, col_ser):
            idx_eq = df.eq(idx_ser, axis=0)
            col_eq = df.eq(col_ser)
            idx_ne = df.ne(idx_ser, axis=0)
            col_ne = df.ne(col_ser)
            tm.assert_frame_equal(col_eq, df == pd.Series(col_ser))
            tm.assert_frame_equal(col_eq, -col_ne)
            tm.assert_frame_equal(idx_eq, -idx_ne)
            tm.assert_frame_equal(idx_eq, df.T.eq(idx_ser).T)
            tm.assert_frame_equal(col_eq, df.eq(list(col_ser)))
            tm.assert_frame_equal(idx_eq, df.eq(pd.Series(idx_ser), axis=0))
            tm.assert_frame_equal(idx_eq, df.eq(list(idx_ser), axis=0))

            idx_gt = df.gt(idx_ser, axis=0)
            col_gt = df.gt(col_ser)
            idx_le = df.le(idx_ser, axis=0)
            col_le = df.le(col_ser)

            tm.assert_frame_equal(col_gt, df > pd.Series(col_ser))
            tm.assert_frame_equal(col_gt, -col_le)
            tm.assert_frame_equal(idx_gt, -idx_le)
            tm.assert_frame_equal(idx_gt, df.T.gt(idx_ser).T)

            idx_ge = df.ge(idx_ser, axis=0)
            col_ge = df.ge(col_ser)
            idx_lt = df.lt(idx_ser, axis=0)
            col_lt = df.lt(col_ser)
            tm.assert_frame_equal(col_ge, df >= pd.Series(col_ser))
            tm.assert_frame_equal(col_ge, -col_lt)
            tm.assert_frame_equal(idx_ge, -idx_lt)
            tm.assert_frame_equal(idx_ge, df.T.ge(idx_ser).T)

        idx_ser = pd.Series(np.random.randn(5))
        col_ser = pd.Series(np.random.randn(3))
        _test_seq(df, idx_ser, col_ser)

        # list/tuple
        _test_seq(df, idx_ser.values, col_ser.values)

        # NA
        df.loc[0, 0] = np.nan
        rs = df.eq(df)
        assert not rs.loc[0, 0]
        rs = df.ne(df)
        assert rs.loc[0, 0]
        rs = df.gt(df)
        assert not rs.loc[0, 0]
        rs = df.lt(df)
        assert not rs.loc[0, 0]
        rs = df.ge(df)
        assert not rs.loc[0, 0]
        rs = df.le(df)
        assert not rs.loc[0, 0]

    def test_bool_flex_frame_complex_dtype(self):
        # complex
        arr = np.array([np.nan, 1, 6, np.nan])
        arr2 = np.array([2j, np.nan, 7, None])
        df = pd.DataFrame({"a": arr})
        df2 = pd.DataFrame({"a": arr2})

        msg = "|".join(
            [
                "'>' not supported between instances of '.*' and 'complex'",
                r"unorderable types: .*complex\(\)",  # PY35
            ]
        )
        with pytest.raises(TypeError, match=msg):
            # inequalities are not well-defined for complex numbers
            df.gt(df2)
        with pytest.raises(TypeError, match=msg):
            # regression test that we get the same behavior for Series
            df["a"].gt(df2["a"])
        with pytest.raises(TypeError, match=msg):
            # Check that we match numpy behavior here
            df.values > df2.values

        rs = df.ne(df2)
        assert rs.values.all()

        arr3 = np.array([2j, np.nan, None])
        df3 = pd.DataFrame({"a": arr3})

        with pytest.raises(TypeError, match=msg):
            # inequalities are not well-defined for complex numbers
            df3.gt(2j)
        with pytest.raises(TypeError, match=msg):
            # regression test that we get the same behavior for Series
            df3["a"].gt(2j)
        with pytest.raises(TypeError, match=msg):
            # Check that we match numpy behavior here
            df3.values > 2j

    def test_bool_flex_frame_object_dtype(self):
        # corner, dtype=object
        df1 = pd.DataFrame({"col": ["foo", np.nan, "bar"]})
        df2 = pd.DataFrame({"col": ["foo", datetime.now(), "bar"]})
        result = df1.ne(df2)
        exp = pd.DataFrame({"col": [False, True, False]})
        tm.assert_frame_equal(result, exp)

    def test_flex_comparison_nat(self):
        # GH 15697, GH 22163 df.eq(pd.NaT) should behave like df == pd.NaT,
        # and _definitely_ not be NaN
        df = pd.DataFrame([pd.NaT])

        result = df == pd.NaT
        # result.iloc[0, 0] is a np.bool_ object
        assert result.iloc[0, 0].item() is False

        result = df.eq(pd.NaT)
        assert result.iloc[0, 0].item() is False

        result = df != pd.NaT
        assert result.iloc[0, 0].item() is True

        result = df.ne(pd.NaT)
        assert result.iloc[0, 0].item() is True

    @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
    def test_df_flex_cmp_constant_return_types(self, opname):
        # GH 15077, non-empty DataFrame
        df = pd.DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]})
        const = 2

        result = getattr(df, opname)(const).dtypes.value_counts()
        tm.assert_series_equal(result, pd.Series([2], index=[np.dtype(bool)]))

    @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
    def test_df_flex_cmp_constant_return_types_empty(self, opname):
        # GH 15077 empty DataFrame
        df = pd.DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]})
        const = 2

        empty = df.iloc[:0]
        result = getattr(empty, opname)(const).dtypes.value_counts()
        tm.assert_series_equal(result, pd.Series([2], index=[np.dtype(bool)]))

    def test_df_flex_cmp_ea_dtype_with_ndarray_series(self):
        ii = pd.IntervalIndex.from_breaks([1, 2, 3])
        df = pd.DataFrame({"A": ii, "B": ii})

        ser = pd.Series([0, 0])
        res = df.eq(ser, axis=0)

        expected = pd.DataFrame({"A": [False, False], "B": [False, False]})
        tm.assert_frame_equal(res, expected)

        ser2 = pd.Series([1, 2], index=["A", "B"])
        res2 = df.eq(ser2, axis=1)
        tm.assert_frame_equal(res2, expected)


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


class TestFrameFlexArithmetic:
    def test_floordiv_axis0(self):
        # make sure we df.floordiv(ser, axis=0) matches column-wise result
        arr = np.arange(3)
        ser = pd.Series(arr)
        df = pd.DataFrame({"A": ser, "B": ser})

        result = df.floordiv(ser, axis=0)

        expected = pd.DataFrame({col: df[col] // ser for col in df.columns})

        tm.assert_frame_equal(result, expected)

        result2 = df.floordiv(ser.values, axis=0)
        tm.assert_frame_equal(result2, expected)

    @pytest.mark.slow
    @pytest.mark.parametrize("opname", ["floordiv", "pow"])
    def test_floordiv_axis0_numexpr_path(self, opname):
        # case that goes through numexpr and has to fall back to masked_arith_op
        op = getattr(operator, opname)

        arr = np.arange(10 ** 6).reshape(100, -1)
        df = pd.DataFrame(arr)
        df["C"] = 1.0

        ser = df[0]
        result = getattr(df, opname)(ser, axis=0)

        expected = pd.DataFrame({col: op(df[col], ser) for col in df.columns})
        tm.assert_frame_equal(result, expected)

        result2 = getattr(df, opname)(ser.values, axis=0)
        tm.assert_frame_equal(result2, expected)

    def test_df_add_td64_columnwise(self):
        # GH 22534 Check that column-wise addition broadcasts correctly
        dti = pd.date_range("2016-01-01", periods=10)
        tdi = pd.timedelta_range("1", periods=10)
        tser = pd.Series(tdi)
        df = pd.DataFrame({0: dti, 1: tdi})

        result = df.add(tser, axis=0)
        expected = pd.DataFrame({0: dti + tdi, 1: tdi + tdi})
        tm.assert_frame_equal(result, expected)

    def test_df_add_flex_filled_mixed_dtypes(self):
        # GH 19611
        dti = pd.date_range("2016-01-01", periods=3)
        ser = pd.Series(["1 Day", "NaT", "2 Days"], dtype="timedelta64[ns]")
        df = pd.DataFrame({"A": dti, "B": ser})
        other = pd.DataFrame({"A": ser, "B": ser})
        fill = pd.Timedelta(days=1).to_timedelta64()
        result = df.add(other, fill_value=fill)

        expected = pd.DataFrame(
            {
                "A": pd.Series(
                    ["2016-01-02", "2016-01-03", "2016-01-05"], dtype="datetime64[ns]"
                ),
                "B": ser * 2,
            }
        )
        tm.assert_frame_equal(result, expected)

    def test_arith_flex_frame(
        self, all_arithmetic_operators, float_frame, mixed_float_frame
    ):
        # one instance of parametrized fixture
        op = all_arithmetic_operators

        def f(x, y):
            # r-versions not in operator-stdlib; get op without "r" and invert
            if op.startswith("__r"):
                return getattr(operator, op.replace("__r", "__"))(y, x)
            return getattr(operator, op)(x, y)

        result = getattr(float_frame, op)(2 * float_frame)
        expected = f(float_frame, 2 * float_frame)
        tm.assert_frame_equal(result, expected)

        # vs mix float
        result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
        expected = f(mixed_float_frame, 2 * mixed_float_frame)
        tm.assert_frame_equal(result, expected)
        _check_mixed_float(result, dtype=dict(C=None))

    @pytest.mark.parametrize("op", ["__add__", "__sub__", "__mul__"])
    def test_arith_flex_frame_mixed(
        self, op, int_frame, mixed_int_frame, mixed_float_frame
    ):
        f = getattr(operator, op)

        # vs mix int
        result = getattr(mixed_int_frame, op)(2 + mixed_int_frame)
        expected = f(mixed_int_frame, 2 + mixed_int_frame)

        # no overflow in the uint
        dtype = None
        if op in ["__sub__"]:
            dtype = dict(B="uint64", C=None)
        elif op in ["__add__", "__mul__"]:
            dtype = dict(C=None)
        tm.assert_frame_equal(result, expected)
        _check_mixed_int(result, dtype=dtype)

        # vs mix float
        result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
        expected = f(mixed_float_frame, 2 * mixed_float_frame)
        tm.assert_frame_equal(result, expected)
        _check_mixed_float(result, dtype=dict(C=None))

        # vs plain int
        result = getattr(int_frame, op)(2 * int_frame)
        expected = f(int_frame, 2 * int_frame)
        tm.assert_frame_equal(result, expected)

    def test_arith_flex_frame_raise(self, all_arithmetic_operators, float_frame):
        # one instance of parametrized fixture
        op = all_arithmetic_operators

        # Check that arrays with dim >= 3 raise
        for dim in range(3, 6):
            arr = np.ones((1,) * dim)
            msg = "Unable to coerce to Series/DataFrame"
            with pytest.raises(ValueError, match=msg):
                getattr(float_frame, op)(arr)

    def test_arith_flex_frame_corner(self, float_frame):

        const_add = float_frame.add(1)
        tm.assert_frame_equal(const_add, float_frame + 1)

        # corner cases
        result = float_frame.add(float_frame[:0])
        tm.assert_frame_equal(result, float_frame * np.nan)

        result = float_frame[:0].add(float_frame)
        tm.assert_frame_equal(result, float_frame * np.nan)

        with pytest.raises(NotImplementedError, match="fill_value"):
            float_frame.add(float_frame.iloc[0], fill_value=3)

        with pytest.raises(NotImplementedError, match="fill_value"):
            float_frame.add(float_frame.iloc[0], axis="index", fill_value=3)

    def test_arith_flex_series(self, simple_frame):
        df = simple_frame

        row = df.xs("a")
        col = df["two"]
        # after arithmetic refactor, add truediv here
        ops = ["add", "sub", "mul", "mod"]
        for op in ops:
            f = getattr(df, op)
            op = getattr(operator, op)
            tm.assert_frame_equal(f(row), op(df, row))
            tm.assert_frame_equal(f(col, axis=0), op(df.T, col).T)

        # special case for some reason
        tm.assert_frame_equal(df.add(row, axis=None), df + row)

        # cases which will be refactored after big arithmetic refactor
        tm.assert_frame_equal(df.div(row), df / row)
        tm.assert_frame_equal(df.div(col, axis=0), (df.T / col).T)

        # broadcasting issue in GH 7325
        df = pd.DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype="int64")
        expected = pd.DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
        result = df.div(df[0], axis="index")
        tm.assert_frame_equal(result, expected)

        df = pd.DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype="float64")
        expected = pd.DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
        result = df.div(df[0], axis="index")
        tm.assert_frame_equal(result, expected)

    def test_arith_flex_zero_len_raises(self):
        # GH 19522 passing fill_value to frame flex arith methods should
        # raise even in the zero-length special cases
        ser_len0 = pd.Series([], dtype=object)
        df_len0 = pd.DataFrame(columns=["A", "B"])
        df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])

        with pytest.raises(NotImplementedError, match="fill_value"):
            df.add(ser_len0, fill_value="E")

        with pytest.raises(NotImplementedError, match="fill_value"):
            df_len0.sub(df["A"], axis=None, fill_value=3)

    def test_flex_add_scalar_fill_value(self):
        # GH#12723
        dat = np.array([0, 1, np.nan, 3, 4, 5], dtype="float")
        df = pd.DataFrame({"foo": dat}, index=range(6))

        exp = df.fillna(0).add(2)
        res = df.add(2, fill_value=0)
        tm.assert_frame_equal(res, exp)


class TestFrameArithmetic:
    def test_td64_op_nat_casting(self):
        # Make sure we don't accidentally treat timedelta64(NaT) as datetime64
        #  when calling dispatch_to_series in DataFrame arithmetic
        ser = pd.Series(["NaT", "NaT"], dtype="timedelta64[ns]")
        df = pd.DataFrame([[1, 2], [3, 4]])

        result = df * ser
        expected = pd.DataFrame({0: ser, 1: ser})
        tm.assert_frame_equal(result, expected)

    def test_df_add_2d_array_rowlike_broadcasts(self):
        # GH#23000
        arr = np.arange(6).reshape(3, 2)
        df = pd.DataFrame(arr, columns=[True, False], index=["A", "B", "C"])

        rowlike = arr[[1], :]  # shape --> (1, ncols)
        assert rowlike.shape == (1, df.shape[1])

        expected = pd.DataFrame(
            [[2, 4], [4, 6], [6, 8]],
            columns=df.columns,
            index=df.index,
            # specify dtype explicitly to avoid failing
            # on 32bit builds
            dtype=arr.dtype,
        )
        result = df + rowlike
        tm.assert_frame_equal(result, expected)
        result = rowlike + df
        tm.assert_frame_equal(result, expected)

    def test_df_add_2d_array_collike_broadcasts(self):
        # GH#23000
        arr = np.arange(6).reshape(3, 2)
        df = pd.DataFrame(arr, columns=[True, False], index=["A", "B", "C"])

        collike = arr[:, [1]]  # shape --> (nrows, 1)
        assert collike.shape == (df.shape[0], 1)

        expected = pd.DataFrame(
            [[1, 2], [5, 6], [9, 10]],
            columns=df.columns,
            index=df.index,
            # specify dtype explicitly to avoid failing
            # on 32bit builds
            dtype=arr.dtype,
        )
        result = df + collike
        tm.assert_frame_equal(result, expected)
        result = collike + df
        tm.assert_frame_equal(result, expected)

    def test_df_arith_2d_array_rowlike_broadcasts(self, all_arithmetic_operators):
        # GH#23000
        opname = all_arithmetic_operators

        arr = np.arange(6).reshape(3, 2)
        df = pd.DataFrame(arr, columns=[True, False], index=["A", "B", "C"])

        rowlike = arr[[1], :]  # shape --> (1, ncols)
        assert rowlike.shape == (1, df.shape[1])

        exvals = [
            getattr(df.loc["A"], opname)(rowlike.squeeze()),
            getattr(df.loc["B"], opname)(rowlike.squeeze()),
            getattr(df.loc["C"], opname)(rowlike.squeeze()),
        ]

        expected = pd.DataFrame(exvals, columns=df.columns, index=df.index)

        result = getattr(df, opname)(rowlike)
        tm.assert_frame_equal(result, expected)

    def test_df_arith_2d_array_collike_broadcasts(self, all_arithmetic_operators):
        # GH#23000
        opname = all_arithmetic_operators

        arr = np.arange(6).reshape(3, 2)
        df = pd.DataFrame(arr, columns=[True, False], index=["A", "B", "C"])

        collike = arr[:, [1]]  # shape --> (nrows, 1)
        assert collike.shape == (df.shape[0], 1)

        exvals = {
            True: getattr(df[True], opname)(collike.squeeze()),
            False: getattr(df[False], opname)(collike.squeeze()),
        }

        dtype = None
        if opname in ["__rmod__", "__rfloordiv__"]:
            # Series ops may return mixed int/float dtypes in cases where
            #   DataFrame op will return all-float.  So we upcast `expected`
            dtype = np.common_type(*[x.values for x in exvals.values()])

        expected = pd.DataFrame(exvals, columns=df.columns, index=df.index, dtype=dtype)

        result = getattr(df, opname)(collike)
        tm.assert_frame_equal(result, expected)

    def test_df_bool_mul_int(self):
        # GH 22047, GH 22163 multiplication by 1 should result in int dtype,
        # not object dtype
        df = pd.DataFrame([[False, True], [False, False]])
        result = df * 1

        # On appveyor this comes back as np.int32 instead of np.int64,
        # so we check dtype.kind instead of just dtype
        kinds = result.dtypes.apply(lambda x: x.kind)
        assert (kinds == "i").all()

        result = 1 * df
        kinds = result.dtypes.apply(lambda x: x.kind)
        assert (kinds == "i").all()

    def test_arith_mixed(self):

        left = pd.DataFrame({"A": ["a", "b", "c"], "B": [1, 2, 3]})

        result = left + left
        expected = pd.DataFrame({"A": ["aa", "bb", "cc"], "B": [2, 4, 6]})
        tm.assert_frame_equal(result, expected)

    def test_arith_getitem_commute(self):
        df = pd.DataFrame({"A": [1.1, 3.3], "B": [2.5, -3.9]})

        def _test_op(df, op):
            result = op(df, 1)

            if not df.columns.is_unique:
                raise ValueError("Only unique columns supported by this test")

            for col in result.columns:
                tm.assert_series_equal(result[col], op(df[col], 1))

        _test_op(df, operator.add)
        _test_op(df, operator.sub)
        _test_op(df, operator.mul)
        _test_op(df, operator.truediv)
        _test_op(df, operator.floordiv)
        _test_op(df, operator.pow)

        _test_op(df, lambda x, y: y + x)
        _test_op(df, lambda x, y: y - x)
        _test_op(df, lambda x, y: y * x)
        _test_op(df, lambda x, y: y / x)
        _test_op(df, lambda x, y: y ** x)

        _test_op(df, lambda x, y: x + y)
        _test_op(df, lambda x, y: x - y)
        _test_op(df, lambda x, y: x * y)
        _test_op(df, lambda x, y: x / y)
        _test_op(df, lambda x, y: x ** y)

    @pytest.mark.parametrize(
        "values", [[1, 2], (1, 2), np.array([1, 2]), range(1, 3), deque([1, 2])]
    )
    def test_arith_alignment_non_pandas_object(self, values):
        # GH#17901
        df = pd.DataFrame({"A": [1, 1], "B": [1, 1]})
        expected = pd.DataFrame({"A": [2, 2], "B": [3, 3]})
        result = df + values
        tm.assert_frame_equal(result, expected)

    def test_arith_non_pandas_object(self):
        df = pd.DataFrame(
            np.arange(1, 10, dtype="f8").reshape(3, 3),
            columns=["one", "two", "three"],
            index=["a", "b", "c"],
        )

        val1 = df.xs("a").values
        added = pd.DataFrame(df.values + val1, index=df.index, columns=df.columns)
        tm.assert_frame_equal(df + val1, added)

        added = pd.DataFrame((df.values.T + val1).T, index=df.index, columns=df.columns)
        tm.assert_frame_equal(df.add(val1, axis=0), added)

        val2 = list(df["two"])

        added = pd.DataFrame(df.values + val2, index=df.index, columns=df.columns)
        tm.assert_frame_equal(df + val2, added)

        added = pd.DataFrame((df.values.T + val2).T, index=df.index, columns=df.columns)
        tm.assert_frame_equal(df.add(val2, axis="index"), added)

        val3 = np.random.rand(*df.shape)
        added = pd.DataFrame(df.values + val3, index=df.index, columns=df.columns)
        tm.assert_frame_equal(df.add(val3), added)

    def test_operations_with_interval_categories_index(self, all_arithmetic_operators):
        # GH#27415
        op = all_arithmetic_operators
        ind = pd.CategoricalIndex(pd.interval_range(start=0.0, end=2.0))
        data = [1, 2]
        df = pd.DataFrame([data], columns=ind)
        num = 10
        result = getattr(df, op)(num)
        expected = pd.DataFrame([[getattr(n, op)(num) for n in data]], columns=ind)
        tm.assert_frame_equal(result, expected)

    def test_frame_with_frame_reindex(self):
        # GH#31623
        df = pd.DataFrame(
            {
                "foo": [pd.Timestamp("2019"), pd.Timestamp("2020")],
                "bar": [pd.Timestamp("2018"), pd.Timestamp("2021")],
            },
            columns=["foo", "bar"],
        )
        df2 = df[["foo"]]

        result = df - df2

        expected = pd.DataFrame(
            {"foo": [pd.Timedelta(0), pd.Timedelta(0)], "bar": [np.nan, np.nan]},
            columns=["bar", "foo"],
        )
        tm.assert_frame_equal(result, expected)


def test_frame_with_zero_len_series_corner_cases():
    # GH#28600
    # easy all-float case
    df = pd.DataFrame(np.random.randn(6).reshape(3, 2), columns=["A", "B"])
    ser = pd.Series(dtype=np.float64)

    result = df + ser
    expected = pd.DataFrame(df.values * np.nan, columns=df.columns)
    tm.assert_frame_equal(result, expected)

    result = df == ser
    expected = pd.DataFrame(False, index=df.index, columns=df.columns)
    tm.assert_frame_equal(result, expected)

    # non-float case should not raise on comparison
    df2 = pd.DataFrame(df.values.view("M8[ns]"), columns=df.columns)
    result = df2 == ser
    expected = pd.DataFrame(False, index=df.index, columns=df.columns)
    tm.assert_frame_equal(result, expected)


def test_zero_len_frame_with_series_corner_cases():
    # GH#28600
    df = pd.DataFrame(columns=["A", "B"], dtype=np.float64)
    ser = pd.Series([1, 2], index=["A", "B"])

    result = df + ser
    expected = df
    tm.assert_frame_equal(result, expected)


def test_frame_single_columns_object_sum_axis_1():
    # GH 13758
    data = {
        "One": pd.Series(["A", 1.2, np.nan]),
    }
    df = pd.DataFrame(data)
    result = df.sum(axis=1)
    expected = pd.Series(["A", 1.2, 0])
    tm.assert_series_equal(result, expected)


# -------------------------------------------------------------------
# Unsorted
#  These arithmetic tests were previously in other files, eventually
#  should be parametrized and put into tests.arithmetic


class TestFrameArithmeticUnsorted:
    def test_frame_add_tz_mismatch_converts_to_utc(self):
        rng = pd.date_range("1/1/2011", periods=10, freq="H", tz="US/Eastern")
        df = pd.DataFrame(np.random.randn(len(rng)), index=rng, columns=["a"])

        df_moscow = df.tz_convert("Europe/Moscow")
        result = df + df_moscow
        assert result.index.tz is pytz.utc

        result = df_moscow + df
        assert result.index.tz is pytz.utc

    def test_align_frame(self):
        rng = pd.period_range("1/1/2000", "1/1/2010", freq="A")
        ts = pd.DataFrame(np.random.randn(len(rng), 3), index=rng)

        result = ts + ts[::2]
        expected = ts + ts
        expected.values[1::2] = np.nan
        tm.assert_frame_equal(result, expected)

        half = ts[::2]
        result = ts + half.take(np.random.permutation(len(half)))
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize(
        "op", [operator.add, operator.sub, operator.mul, operator.truediv]
    )
    def test_operators_none_as_na(self, op):
        df = DataFrame(
            {"col1": [2, 5.0, 123, None], "col2": [1, 2, 3, 4]}, dtype=object
        )

        # since filling converts dtypes from object, changed expected to be
        # object
        filled = df.fillna(np.nan)
        result = op(df, 3)
        expected = op(filled, 3).astype(object)
        expected[com.isna(expected)] = None
        tm.assert_frame_equal(result, expected)

        result = op(df, df)
        expected = op(filled, filled).astype(object)
        expected[com.isna(expected)] = None
        tm.assert_frame_equal(result, expected)

        result = op(df, df.fillna(7))
        tm.assert_frame_equal(result, expected)

        result = op(df.fillna(7), df)
        tm.assert_frame_equal(result, expected, check_dtype=False)

    @pytest.mark.parametrize("op,res", [("__eq__", False), ("__ne__", True)])
    # TODO: not sure what's correct here.
    @pytest.mark.filterwarnings("ignore:elementwise:FutureWarning")
    def test_logical_typeerror_with_non_valid(self, op, res, float_frame):
        # we are comparing floats vs a string
        result = getattr(float_frame, op)("foo")
        assert bool(result.all().all()) is res

    def test_binary_ops_align(self):

        # test aligning binary ops

        # GH 6681
        index = MultiIndex.from_product(
            [list("abc"), ["one", "two", "three"], [1, 2, 3]],
            names=["first", "second", "third"],
        )

        df = DataFrame(
            np.arange(27 * 3).reshape(27, 3),
            index=index,
            columns=["value1", "value2", "value3"],
        ).sort_index()

        idx = pd.IndexSlice
        for op in ["add", "sub", "mul", "div", "truediv"]:
            opa = getattr(operator, op, None)
            if opa is None:
                continue

            x = Series([1.0, 10.0, 100.0], [1, 2, 3])
            result = getattr(df, op)(x, level="third", axis=0)

            expected = pd.concat(
                [opa(df.loc[idx[:, :, i], :], v) for i, v in x.items()]
            ).sort_index()
            tm.assert_frame_equal(result, expected)

            x = Series([1.0, 10.0], ["two", "three"])
            result = getattr(df, op)(x, level="second", axis=0)

            expected = (
                pd.concat([opa(df.loc[idx[:, i], :], v) for i, v in x.items()])
                .reindex_like(df)
                .sort_index()
            )
            tm.assert_frame_equal(result, expected)

        # GH9463 (alignment level of dataframe with series)

        midx = MultiIndex.from_product([["A", "B"], ["a", "b"]])
        df = DataFrame(np.ones((2, 4), dtype="int64"), columns=midx)
        s = pd.Series({"a": 1, "b": 2})

        df2 = df.copy()
        df2.columns.names = ["lvl0", "lvl1"]
        s2 = s.copy()
        s2.index.name = "lvl1"

        # different cases of integer/string level names:
        res1 = df.mul(s, axis=1, level=1)
        res2 = df.mul(s2, axis=1, level=1)
        res3 = df2.mul(s, axis=1, level=1)
        res4 = df2.mul(s2, axis=1, level=1)
        res5 = df2.mul(s, axis=1, level="lvl1")
        res6 = df2.mul(s2, axis=1, level="lvl1")

        exp = DataFrame(
            np.array([[1, 2, 1, 2], [1, 2, 1, 2]], dtype="int64"), columns=midx
        )

        for res in [res1, res2]:
            tm.assert_frame_equal(res, exp)

        exp.columns.names = ["lvl0", "lvl1"]
        for res in [res3, res4, res5, res6]:
            tm.assert_frame_equal(res, exp)

    def test_add_with_dti_mismatched_tzs(self):
        base = pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], tz="UTC")
        idx1 = base.tz_convert("Asia/Tokyo")[:2]
        idx2 = base.tz_convert("US/Eastern")[1:]

        df1 = DataFrame({"A": [1, 2]}, index=idx1)
        df2 = DataFrame({"A": [1, 1]}, index=idx2)
        exp = DataFrame({"A": [np.nan, 3, np.nan]}, index=base)
        tm.assert_frame_equal(df1 + df2, exp)

    def test_combineFrame(self, float_frame, mixed_float_frame, mixed_int_frame):
        frame_copy = float_frame.reindex(float_frame.index[::2])

        del frame_copy["D"]
        frame_copy["C"][:5] = np.nan

        added = float_frame + frame_copy

        indexer = added["A"].dropna().index
        exp = (float_frame["A"] * 2).copy()

        tm.assert_series_equal(added["A"].dropna(), exp.loc[indexer])

        exp.loc[~exp.index.isin(indexer)] = np.nan
        tm.assert_series_equal(added["A"], exp.loc[added["A"].index])

        assert np.isnan(added["C"].reindex(frame_copy.index)[:5]).all()

        # assert(False)

        assert np.isnan(added["D"]).all()

        self_added = float_frame + float_frame
        tm.assert_index_equal(self_added.index, float_frame.index)

        added_rev = frame_copy + float_frame
        assert np.isnan(added["D"]).all()
        assert np.isnan(added_rev["D"]).all()

        # corner cases

        # empty
        plus_empty = float_frame + DataFrame()
        assert np.isnan(plus_empty.values).all()

        empty_plus = DataFrame() + float_frame
        assert np.isnan(empty_plus.values).all()

        empty_empty = DataFrame() + DataFrame()
        assert empty_empty.empty

        # out of order
        reverse = float_frame.reindex(columns=float_frame.columns[::-1])

        tm.assert_frame_equal(reverse + float_frame, float_frame * 2)

        # mix vs float64, upcast
        added = float_frame + mixed_float_frame
        _check_mixed_float(added, dtype="float64")
        added = mixed_float_frame + float_frame
        _check_mixed_float(added, dtype="float64")

        # mix vs mix
        added = mixed_float_frame + mixed_float_frame
        _check_mixed_float(added, dtype=dict(C=None))

        # with int
        added = float_frame + mixed_int_frame
        _check_mixed_float(added, dtype="float64")

    def test_combine_series(
        self, float_frame, mixed_float_frame, mixed_int_frame, datetime_frame
    ):

        # Series
        series = float_frame.xs(float_frame.index[0])

        added = float_frame + series

        for key, s in added.items():
            tm.assert_series_equal(s, float_frame[key] + series[key])

        larger_series = series.to_dict()
        larger_series["E"] = 1
        larger_series = Series(larger_series)
        larger_added = float_frame + larger_series

        for key, s in float_frame.items():
            tm.assert_series_equal(larger_added[key], s + series[key])
        assert "E" in larger_added
        assert np.isnan(larger_added["E"]).all()

        # no upcast needed
        added = mixed_float_frame + series
        assert np.all(added.dtypes == series.dtype)

        # vs mix (upcast) as needed
        added = mixed_float_frame + series.astype("float32")
        _check_mixed_float(added, dtype=dict(C=None))
        added = mixed_float_frame + series.astype("float16")
        _check_mixed_float(added, dtype=dict(C=None))

        # FIXME: don't leave commented-out
        # these raise with numexpr.....as we are adding an int64 to an
        # uint64....weird vs int

        # added = mixed_int_frame + (100*series).astype('int64')
        # _check_mixed_int(added, dtype = dict(A = 'int64', B = 'float64', C =
        # 'int64', D = 'int64'))
        # added = mixed_int_frame + (100*series).astype('int32')
        # _check_mixed_int(added, dtype = dict(A = 'int32', B = 'float64', C =
        # 'int32', D = 'int64'))

        # TimeSeries
        ts = datetime_frame["A"]

        # 10890
        # we no longer allow auto timeseries broadcasting
        # and require explicit broadcasting
        added = datetime_frame.add(ts, axis="index")

        for key, col in datetime_frame.items():
            result = col + ts
            tm.assert_series_equal(added[key], result, check_names=False)
            assert added[key].name == key
            if col.name == ts.name:
                assert result.name == "A"
            else:
                assert result.name is None

        smaller_frame = datetime_frame[:-5]
        smaller_added = smaller_frame.add(ts, axis="index")

        tm.assert_index_equal(smaller_added.index, datetime_frame.index)

        smaller_ts = ts[:-5]
        smaller_added2 = datetime_frame.add(smaller_ts, axis="index")
        tm.assert_frame_equal(smaller_added, smaller_added2)

        # length 0, result is all-nan
        result = datetime_frame.add(ts[:0], axis="index")
        expected = DataFrame(
            np.nan, index=datetime_frame.index, columns=datetime_frame.columns
        )
        tm.assert_frame_equal(result, expected)

        # Frame is all-nan
        result = datetime_frame[:0].add(ts, axis="index")
        expected = DataFrame(
            np.nan, index=datetime_frame.index, columns=datetime_frame.columns
        )
        tm.assert_frame_equal(result, expected)

        # empty but with non-empty index
        frame = datetime_frame[:1].reindex(columns=[])
        result = frame.mul(ts, axis="index")
        assert len(result) == len(ts)

    def test_combineFunc(self, float_frame, mixed_float_frame):
        result = float_frame * 2
        tm.assert_numpy_array_equal(result.values, float_frame.values * 2)

        # vs mix
        result = mixed_float_frame * 2
        for c, s in result.items():
            tm.assert_numpy_array_equal(s.values, mixed_float_frame[c].values * 2)
        _check_mixed_float(result, dtype=dict(C=None))

        result = DataFrame() * 2
        assert result.index.equals(DataFrame().index)
        assert len(result.columns) == 0

    def test_comparisons(self, simple_frame, float_frame):
        df1 = tm.makeTimeDataFrame()
        df2 = tm.makeTimeDataFrame()

        row = simple_frame.xs("a")
        ndim_5 = np.ones(df1.shape + (1, 1, 1))

        def test_comp(func):
            result = func(df1, df2)
            tm.assert_numpy_array_equal(result.values, func(df1.values, df2.values))

            msg = (
                "Unable to coerce to Series/DataFrame, "
                "dimension must be <= 2: (30, 4, 1, 1, 1)"
            )
            with pytest.raises(ValueError, match=re.escape(msg)):
                func(df1, ndim_5)

            result2 = func(simple_frame, row)
            tm.assert_numpy_array_equal(
                result2.values, func(simple_frame.values, row.values)
            )

            result3 = func(float_frame, 0)
            tm.assert_numpy_array_equal(result3.values, func(float_frame.values, 0))

            msg = "Can only compare identically-labeled DataFrame"
            with pytest.raises(ValueError, match=msg):
                func(simple_frame, simple_frame[:2])

        test_comp(operator.eq)
        test_comp(operator.ne)
        test_comp(operator.lt)
        test_comp(operator.gt)
        test_comp(operator.ge)
        test_comp(operator.le)

    def test_strings_to_numbers_comparisons_raises(self, compare_operators_no_eq_ne):
        # GH 11565
        df = DataFrame(
            {x: {"x": "foo", "y": "bar", "z": "baz"} for x in ["a", "b", "c"]}
        )

        f = getattr(operator, compare_operators_no_eq_ne)
        msg = "'[<>]=?' not supported between instances of 'str' and 'int'"
        with pytest.raises(TypeError, match=msg):
            f(df, 0)

    def test_comparison_protected_from_errstate(self):
        missing_df = tm.makeDataFrame()
        missing_df.iloc[0]["A"] = np.nan
        with np.errstate(invalid="ignore"):
            expected = missing_df.values < 0
        with np.errstate(invalid="raise"):
            result = (missing_df < 0).values
        tm.assert_numpy_array_equal(result, expected)

    def test_boolean_comparison(self):

        # GH 4576
        # boolean comparisons with a tuple/list give unexpected results
        df = DataFrame(np.arange(6).reshape((3, 2)))
        b = np.array([2, 2])
        b_r = np.atleast_2d([2, 2])
        b_c = b_r.T
        lst = [2, 2, 2]
        tup = tuple(lst)

        # gt
        expected = DataFrame([[False, False], [False, True], [True, True]])
        result = df > b
        tm.assert_frame_equal(result, expected)

        result = df.values > b
        tm.assert_numpy_array_equal(result, expected.values)

        msg1d = "Unable to coerce to Series, length must be 2: given 3"
        msg2d = "Unable to coerce to DataFrame, shape must be"
        msg2db = "operands could not be broadcast together with shapes"
        with pytest.raises(ValueError, match=msg1d):
            # wrong shape
            df > lst

        with pytest.raises(ValueError, match=msg1d):
            # wrong shape
            result = df > tup

        # broadcasts like ndarray (GH#23000)
        result = df > b_r
        tm.assert_frame_equal(result, expected)

        result = df.values > b_r
        tm.assert_numpy_array_equal(result, expected.values)

        with pytest.raises(ValueError, match=msg2d):
            df > b_c

        with pytest.raises(ValueError, match=msg2db):
            df.values > b_c

        # ==
        expected = DataFrame([[False, False], [True, False], [False, False]])
        result = df == b
        tm.assert_frame_equal(result, expected)

        with pytest.raises(ValueError, match=msg1d):
            result = df == lst

        with pytest.raises(ValueError, match=msg1d):
            result = df == tup

        # broadcasts like ndarray (GH#23000)
        result = df == b_r
        tm.assert_frame_equal(result, expected)

        result = df.values == b_r
        tm.assert_numpy_array_equal(result, expected.values)

        with pytest.raises(ValueError, match=msg2d):
            df == b_c

        assert df.values.shape != b_c.shape

        # with alignment
        df = DataFrame(
            np.arange(6).reshape((3, 2)), columns=list("AB"), index=list("abc")
        )
        expected.index = df.index
        expected.columns = df.columns

        with pytest.raises(ValueError, match=msg1d):
            result = df == lst

        with pytest.raises(ValueError, match=msg1d):
            result = df == tup

    def test_inplace_ops_alignment(self):

        # inplace ops / ops alignment
        # GH 8511

        columns = list("abcdefg")
        X_orig = DataFrame(
            np.arange(10 * len(columns)).reshape(-1, len(columns)),
            columns=columns,
            index=range(10),
        )
        Z = 100 * X_orig.iloc[:, 1:-1].copy()
        block1 = list("bedcf")
        subs = list("bcdef")

        # add
        X = X_orig.copy()
        result1 = (X[block1] + Z).reindex(columns=subs)

        X[block1] += Z
        result2 = X.reindex(columns=subs)

        X = X_orig.copy()
        result3 = (X[block1] + Z[block1]).reindex(columns=subs)

        X[block1] += Z[block1]
        result4 = X.reindex(columns=subs)

        tm.assert_frame_equal(result1, result2)
        tm.assert_frame_equal(result1, result3)
        tm.assert_frame_equal(result1, result4)

        # sub
        X = X_orig.copy()
        result1 = (X[block1] - Z).reindex(columns=subs)

        X[block1] -= Z
        result2 = X.reindex(columns=subs)

        X = X_orig.copy()
        result3 = (X[block1] - Z[block1]).reindex(columns=subs)

        X[block1] -= Z[block1]
        result4 = X.reindex(columns=subs)

        tm.assert_frame_equal(result1, result2)
        tm.assert_frame_equal(result1, result3)
        tm.assert_frame_equal(result1, result4)

    def test_inplace_ops_identity(self):

        # GH 5104
        # make sure that we are actually changing the object
        s_orig = Series([1, 2, 3])
        df_orig = DataFrame(np.random.randint(0, 5, size=10).reshape(-1, 5))

        # no dtype change
        s = s_orig.copy()
        s2 = s
        s += 1
        tm.assert_series_equal(s, s2)
        tm.assert_series_equal(s_orig + 1, s)
        assert s is s2
        assert s._mgr is s2._mgr

        df = df_orig.copy()
        df2 = df
        df += 1
        tm.assert_frame_equal(df, df2)
        tm.assert_frame_equal(df_orig + 1, df)
        assert df is df2
        assert df._mgr is df2._mgr

        # dtype change
        s = s_orig.copy()
        s2 = s
        s += 1.5
        tm.assert_series_equal(s, s2)
        tm.assert_series_equal(s_orig + 1.5, s)

        df = df_orig.copy()
        df2 = df
        df += 1.5
        tm.assert_frame_equal(df, df2)
        tm.assert_frame_equal(df_orig + 1.5, df)
        assert df is df2
        assert df._mgr is df2._mgr

        # mixed dtype
        arr = np.random.randint(0, 10, size=5)
        df_orig = DataFrame({"A": arr.copy(), "B": "foo"})
        df = df_orig.copy()
        df2 = df
        df["A"] += 1
        expected = DataFrame({"A": arr.copy() + 1, "B": "foo"})
        tm.assert_frame_equal(df, expected)
        tm.assert_frame_equal(df2, expected)
        assert df._mgr is df2._mgr

        df = df_orig.copy()
        df2 = df
        df["A"] += 1.5
        expected = DataFrame({"A": arr.copy() + 1.5, "B": "foo"})
        tm.assert_frame_equal(df, expected)
        tm.assert_frame_equal(df2, expected)
        assert df._mgr is df2._mgr

    @pytest.mark.parametrize(
        "op",
        [
            "add",
            "and",
            "div",
            "floordiv",
            "mod",
            "mul",
            "or",
            "pow",
            "sub",
            "truediv",
            "xor",
        ],
    )
    def test_inplace_ops_identity2(self, op):

        if op == "div":
            return

        df = DataFrame({"a": [1.0, 2.0, 3.0], "b": [1, 2, 3]})

        operand = 2
        if op in ("and", "or", "xor"):
            # cannot use floats for boolean ops
            df["a"] = [True, False, True]

        df_copy = df.copy()
        iop = f"__i{op}__"
        op = f"__{op}__"

        # no id change and value is correct
        getattr(df, iop)(operand)
        expected = getattr(df_copy, op)(operand)
        tm.assert_frame_equal(df, expected)
        expected = id(df)
        assert id(df) == expected

    def test_alignment_non_pandas(self):
        index = ["A", "B", "C"]
        columns = ["X", "Y", "Z"]
        df = pd.DataFrame(np.random.randn(3, 3), index=index, columns=columns)

        align = pd.core.ops._align_method_FRAME
        for val in [
            [1, 2, 3],
            (1, 2, 3),
            np.array([1, 2, 3], dtype=np.int64),
            range(1, 4),
        ]:

            expected = DataFrame({"X": val, "Y": val, "Z": val}, index=df.index)
            tm.assert_frame_equal(align(df, val, "index")[1], expected)

            expected = DataFrame(
                {"X": [1, 1, 1], "Y": [2, 2, 2], "Z": [3, 3, 3]}, index=df.index
            )
            tm.assert_frame_equal(align(df, val, "columns")[1], expected)

        # length mismatch
        msg = "Unable to coerce to Series, length must be 3: given 2"
        for val in [[1, 2], (1, 2), np.array([1, 2]), range(1, 3)]:

            with pytest.raises(ValueError, match=msg):
                align(df, val, "index")

            with pytest.raises(ValueError, match=msg):
                align(df, val, "columns")

        val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        tm.assert_frame_equal(
            align(df, val, "index")[1],
            DataFrame(val, index=df.index, columns=df.columns),
        )
        tm.assert_frame_equal(
            align(df, val, "columns")[1],
            DataFrame(val, index=df.index, columns=df.columns),
        )

        # shape mismatch
        msg = "Unable to coerce to DataFrame, shape must be"
        val = np.array([[1, 2, 3], [4, 5, 6]])
        with pytest.raises(ValueError, match=msg):
            align(df, val, "index")

        with pytest.raises(ValueError, match=msg):
            align(df, val, "columns")

        val = np.zeros((3, 3, 3))
        msg = re.escape(
            "Unable to coerce to Series/DataFrame, dimension must be <= 2: (3, 3, 3)"
        )
        with pytest.raises(ValueError, match=msg):
            align(df, val, "index")
        with pytest.raises(ValueError, match=msg):
            align(df, val, "columns")

    def test_no_warning(self, all_arithmetic_operators):
        df = pd.DataFrame({"A": [0.0, 0.0], "B": [0.0, None]})
        b = df["B"]
        with tm.assert_produces_warning(None):
            getattr(df, all_arithmetic_operators)(b, 0)


def test_pow_with_realignment():
    # GH#32685 pow has special semantics for operating with null values
    left = pd.DataFrame({"A": [0, 1, 2]})
    right = pd.DataFrame(index=[0, 1, 2])

    result = left ** right
    expected = pd.DataFrame({"A": [np.nan, 1.0, np.nan]})
    tm.assert_frame_equal(result, expected)


# TODO: move to tests.arithmetic and parametrize
def test_pow_nan_with_zero():
    left = pd.DataFrame({"A": [np.nan, np.nan, np.nan]})
    right = pd.DataFrame({"A": [0, 0, 0]})

    expected = pd.DataFrame({"A": [1.0, 1.0, 1.0]})

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

    result = left["A"] ** right["A"]
    tm.assert_series_equal(result, expected["A"])


def test_dataframe_series_extension_dtypes():
    # https://github.com/pandas-dev/pandas/issues/34311
    df = pd.DataFrame(np.random.randint(0, 100, (10, 3)), columns=["a", "b", "c"])
    ser = pd.Series([1, 2, 3], index=["a", "b", "c"])

    expected = df.to_numpy("int64") + ser.to_numpy("int64").reshape(-1, 3)
    expected = pd.DataFrame(expected, columns=df.columns, dtype="Int64")

    df_ea = df.astype("Int64")
    result = df_ea + ser
    tm.assert_frame_equal(result, expected)
    result = df_ea + ser.astype("Int64")
    tm.assert_frame_equal(result, expected)


def test_dataframe_blockwise_slicelike():
    # GH#34367
    arr = np.random.randint(0, 1000, (100, 10))
    df1 = pd.DataFrame(arr)
    df2 = df1.copy()
    df2.iloc[0, [1, 3, 7]] = np.nan

    df3 = df1.copy()
    df3.iloc[0, [5]] = np.nan

    df4 = df1.copy()
    df4.iloc[0, np.arange(2, 5)] = np.nan
    df5 = df1.copy()
    df5.iloc[0, np.arange(4, 7)] = np.nan

    for left, right in [(df1, df2), (df2, df3), (df4, df5)]:
        res = left + right

        expected = pd.DataFrame({i: left[i] + right[i] for i in left.columns})
        tm.assert_frame_equal(res, expected)


@pytest.mark.parametrize(
    "df, col_dtype",
    [
        (pd.DataFrame([[1.0, 2.0], [4.0, 5.0]], columns=list("ab")), "float64"),
        (pd.DataFrame([[1.0, "b"], [4.0, "b"]], columns=list("ab")), "object"),
    ],
)
def test_dataframe_operation_with_non_numeric_types(df, col_dtype):
    # GH #22663
    expected = pd.DataFrame([[0.0, np.nan], [3.0, np.nan]], columns=list("ab"))
    expected = expected.astype({"b": col_dtype})
    result = df + pd.Series([-1.0], index=list("a"))
    tm.assert_frame_equal(result, expected)
