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env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/common.py

@@ -0,0 +1,155 @@
+"""
+Assertion helpers for arithmetic tests.
+"""
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    array,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    BooleanArray,
+    NumpyExtensionArray,
+)
+
+
+def assert_cannot_add(left, right, msg="cannot add"):
+    """
+    Helper to assert that left and right cannot be added.
+
+    Parameters
+    ----------
+    left : object
+    right : object
+    msg : str, default "cannot add"
+    """
+    with pytest.raises(TypeError, match=msg):
+        left + right
+    with pytest.raises(TypeError, match=msg):
+        right + left
+
+
+def assert_invalid_addsub_type(left, right, msg=None):
+    """
+    Helper to assert that left and right can be neither added nor subtracted.
+
+    Parameters
+    ----------
+    left : object
+    right : object
+    msg : str or None, default None
+    """
+    with pytest.raises(TypeError, match=msg):
+        left + right
+    with pytest.raises(TypeError, match=msg):
+        right + left
+    with pytest.raises(TypeError, match=msg):
+        left - right
+    with pytest.raises(TypeError, match=msg):
+        right - left
+
+
+def get_upcast_box(left, right, is_cmp: bool = False):
+    """
+    Get the box to use for 'expected' in an arithmetic or comparison operation.
+
+    Parameters
+    left : Any
+    right : Any
+    is_cmp : bool, default False
+        Whether the operation is a comparison method.
+    """
+
+    if isinstance(left, DataFrame) or isinstance(right, DataFrame):
+        return DataFrame
+    if isinstance(left, Series) or isinstance(right, Series):
+        if is_cmp and isinstance(left, Index):
+            # Index does not defer for comparisons
+            return np.array
+        return Series
+    if isinstance(left, Index) or isinstance(right, Index):
+        if is_cmp:
+            return np.array
+        return Index
+    return tm.to_array
+
+
+def assert_invalid_comparison(left, right, box):
+    """
+    Assert that comparison operations with mismatched types behave correctly.
+
+    Parameters
+    ----------
+    left : np.ndarray, ExtensionArray, Index, or Series
+    right : object
+    box : {pd.DataFrame, pd.Series, pd.Index, pd.array, tm.to_array}
+    """
+    # Not for tznaive-tzaware comparison
+
+    # Note: not quite the same as how we do this for tm.box_expected
+    xbox = box if box not in [Index, array] else np.array
+
+    def xbox2(x):
+        # Eventually we'd like this to be tighter, but for now we'll
+        #  just exclude NumpyExtensionArray[bool]
+        if isinstance(x, NumpyExtensionArray):
+            return x._ndarray
+        if isinstance(x, BooleanArray):
+            # NB: we are assuming no pd.NAs for now
+            return x.astype(bool)
+        return x
+
+    # rev_box: box to use for reversed comparisons
+    rev_box = xbox
+    if isinstance(right, Index) and isinstance(left, Series):
+        rev_box = np.array
+
+    result = xbox2(left == right)
+    expected = xbox(np.zeros(result.shape, dtype=np.bool_))
+
+    tm.assert_equal(result, expected)
+
+    result = xbox2(right == left)
+    tm.assert_equal(result, rev_box(expected))
+
+    result = xbox2(left != right)
+    tm.assert_equal(result, ~expected)
+
+    result = xbox2(right != left)
+    tm.assert_equal(result, rev_box(~expected))
+
+    msg = "|".join(
+        [
+            "Invalid comparison between",
+            "Cannot compare type",
+            "not supported between",
+            "invalid type promotion",
+            (
+                # GH#36706 npdev 1.20.0 2020-09-28
+                r"The DTypes <class 'numpy.dtype\[datetime64\]'> and "
+                r"<class 'numpy.dtype\[int64\]'> do not have a common DType. "
+                "For example they cannot be stored in a single array unless the "
+                "dtype is `object`."
+            ),
+        ]
+    )
+    with pytest.raises(TypeError, match=msg):
+        left < right
+    with pytest.raises(TypeError, match=msg):
+        left <= right
+    with pytest.raises(TypeError, match=msg):
+        left > right
+    with pytest.raises(TypeError, match=msg):
+        left >= right
+    with pytest.raises(TypeError, match=msg):
+        right < left
+    with pytest.raises(TypeError, match=msg):
+        right <= left
+    with pytest.raises(TypeError, match=msg):
+        right > left
+    with pytest.raises(TypeError, match=msg):
+        right >= left

env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/conftest.py

@@ -0,0 +1,139 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Index
+
+
+@pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
+def one(request):
+    """
+    Several variants of integer value 1. The zero-dim integer array
+    behaves like an integer.
+
+    This fixture can be used to check that datetimelike indexes handle
+    addition and subtraction of integers and zero-dimensional arrays
+    of integers.
+
+    Examples
+    --------
+    dti = pd.date_range('2016-01-01', periods=2, freq='h')
+    dti
+    DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 01:00:00'],
+    dtype='datetime64[ns]', freq='h')
+    dti + one
+    DatetimeIndex(['2016-01-01 01:00:00', '2016-01-01 02:00:00'],
+    dtype='datetime64[ns]', freq='h')
+    """
+    return request.param
+
+
+zeros = [
+    box_cls([0] * 5, dtype=dtype)
+    for box_cls in [Index, np.array, pd.array]
+    for dtype in [np.int64, np.uint64, np.float64]
+]
+zeros.extend([box_cls([-0.0] * 5, dtype=np.float64) for box_cls in [Index, np.array]])
+zeros.extend([np.array(0, dtype=dtype) for dtype in [np.int64, np.uint64, np.float64]])
+zeros.extend([np.array(-0.0, dtype=np.float64)])
+zeros.extend([0, 0.0, -0.0])
+
+
+@pytest.fixture(params=zeros)
+def zero(request):
+    """
+    Several types of scalar zeros and length 5 vectors of zeros.
+
+    This fixture can be used to check that numeric-dtype indexes handle
+    division by any zero numeric-dtype.
+
+    Uses vector of length 5 for broadcasting with `numeric_idx` fixture,
+    which creates numeric-dtype vectors also of length 5.
+
+    Examples
+    --------
+    arr = RangeIndex(5)
+    arr / zeros
+    Index([nan, inf, inf, inf, inf], dtype='float64')
+    """
+    return request.param
+
+
+# ------------------------------------------------------------------
+# Scalar Fixtures
+
+
+@pytest.fixture(
+    params=[
+        pd.Timedelta("10m7s").to_pytimedelta(),
+        pd.Timedelta("10m7s"),
+        pd.Timedelta("10m7s").to_timedelta64(),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def scalar_td(request):
+    """
+    Several variants of Timedelta scalars representing 10 minutes and 7 seconds.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        pd.offsets.Day(3),
+        pd.offsets.Hour(72),
+        pd.Timedelta(days=3).to_pytimedelta(),
+        pd.Timedelta("72:00:00"),
+        np.timedelta64(3, "D"),
+        np.timedelta64(72, "h"),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def three_days(request):
+    """
+    Several timedelta-like and DateOffset objects that each represent
+    a 3-day timedelta
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        pd.offsets.Hour(2),
+        pd.offsets.Minute(120),
+        pd.Timedelta(hours=2).to_pytimedelta(),
+        pd.Timedelta(seconds=2 * 3600),
+        np.timedelta64(2, "h"),
+        np.timedelta64(120, "m"),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def two_hours(request):
+    """
+    Several timedelta-like and DateOffset objects that each represent
+    a 2-hour timedelta
+    """
+    return request.param
+
+
+_common_mismatch = [
+    pd.offsets.YearBegin(2),
+    pd.offsets.MonthBegin(1),
+    pd.offsets.Minute(),
+]
+
+
+@pytest.fixture(
+    params=[
+        np.timedelta64(4, "h"),
+        pd.Timedelta(hours=23).to_pytimedelta(),
+        pd.Timedelta("23:00:00"),
+    ]
+    + _common_mismatch
+)
+def not_daily(request):
+    """
+    Several timedelta-like and DateOffset instances that are _not_
+    compatible with Daily frequencies.
+    """
+    return request.param

env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/test_array_ops.py

@@ -0,0 +1,39 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas._testing as tm
+from pandas.core.ops.array_ops import (
+    comparison_op,
+    na_logical_op,
+)
+
+
+def test_na_logical_op_2d():
+    left = np.arange(8).reshape(4, 2)
+    right = left.astype(object)
+    right[0, 0] = np.nan
+
+    # Check that we fall back to the vec_binop branch
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        operator.or_(left, right)
+
+    result = na_logical_op(left, right, operator.or_)
+    expected = right
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_object_comparison_2d():
+    left = np.arange(9).reshape(3, 3).astype(object)
+    right = left.T
+
+    result = comparison_op(left, right, operator.eq)
+    expected = np.eye(3).astype(bool)
+    tm.assert_numpy_array_equal(result, expected)
+
+    # Ensure that cython doesn't raise on non-writeable arg, which
+    #  we can get from np.broadcast_to
+    right.flags.writeable = False
+    result = comparison_op(left, right, operator.ne)
+    tm.assert_numpy_array_equal(result, ~expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/test_categorical.py

@@ -0,0 +1,25 @@
+import numpy as np
+
+from pandas import (
+    Categorical,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalComparisons:
+    def test_categorical_nan_equality(self):
+        cat = Series(Categorical(["a", "b", "c", np.nan]))
+        expected = Series([True, True, True, False])
+        result = cat == cat
+        tm.assert_series_equal(result, expected)
+
+    def test_categorical_tuple_equality(self):
+        # GH 18050
+        ser = Series([(0, 0), (0, 1), (0, 0), (1, 0), (1, 1)])
+        expected = Series([True, False, True, False, False])
+        result = ser == (0, 0)
+        tm.assert_series_equal(result, expected)
+
+        result = ser.astype("category") == (0, 0)
+        tm.assert_series_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/test_datetime64.py

@@ -0,0 +1,2469 @@
+# Arithmetic tests for DataFrame/Series/Index/Array classes that should
+# behave identically.
+# Specifically for datetime64 and datetime64tz dtypes
+from datetime import (
+    datetime,
+    time,
+    timedelta,
+)
+from itertools import (
+    product,
+    starmap,
+)
+import operator
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs.tslibs.conversion import localize_pydatetime
+from pandas._libs.tslibs.offsets import shift_months
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    DateOffset,
+    DatetimeIndex,
+    NaT,
+    Period,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core import roperator
+from pandas.tests.arithmetic.common import (
+    assert_cannot_add,
+    assert_invalid_addsub_type,
+    assert_invalid_comparison,
+    get_upcast_box,
+)
+
+# ------------------------------------------------------------------
+# Comparisons
+
+
+class TestDatetime64ArrayLikeComparisons:
+    # Comparison tests for datetime64 vectors fully parametrized over
+    #  DataFrame/Series/DatetimeIndex/DatetimeArray.  Ideally all comparison
+    #  tests will eventually end up here.
+
+    def test_compare_zerodim(self, tz_naive_fixture, box_with_array):
+        # Test comparison with zero-dimensional array is unboxed
+        tz = tz_naive_fixture
+        box = box_with_array
+        dti = date_range("20130101", periods=3, tz=tz)
+
+        other = np.array(dti.to_numpy()[0])
+
+        dtarr = tm.box_expected(dti, box)
+        xbox = get_upcast_box(dtarr, other, True)
+        result = dtarr <= other
+        expected = np.array([True, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            "foo",
+            -1,
+            99,
+            4.0,
+            object(),
+            timedelta(days=2),
+            # GH#19800, GH#19301 datetime.date comparison raises to
+            #  match DatetimeIndex/Timestamp.  This also matches the behavior
+            #  of stdlib datetime.datetime
+            datetime(2001, 1, 1).date(),
+            # GH#19301 None and NaN are *not* cast to NaT for comparisons
+            None,
+            np.nan,
+        ],
+    )
+    def test_dt64arr_cmp_scalar_invalid(self, other, tz_naive_fixture, box_with_array):
+        # GH#22074, GH#15966
+        tz = tz_naive_fixture
+
+        rng = date_range("1/1/2000", periods=10, tz=tz)
+        dtarr = tm.box_expected(rng, box_with_array)
+        assert_invalid_comparison(dtarr, other, box_with_array)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            # GH#4968 invalid date/int comparisons
+            list(range(10)),
+            np.arange(10),
+            np.arange(10).astype(np.float32),
+            np.arange(10).astype(object),
+            pd.timedelta_range("1ns", periods=10).array,
+            np.array(pd.timedelta_range("1ns", periods=10)),
+            list(pd.timedelta_range("1ns", periods=10)),
+            pd.timedelta_range("1 Day", periods=10).astype(object),
+            pd.period_range("1971-01-01", freq="D", periods=10).array,
+            pd.period_range("1971-01-01", freq="D", periods=10).astype(object),
+        ],
+    )
+    def test_dt64arr_cmp_arraylike_invalid(
+        self, other, tz_naive_fixture, box_with_array
+    ):
+        tz = tz_naive_fixture
+
+        dta = date_range("1970-01-01", freq="ns", periods=10, tz=tz)._data
+        obj = tm.box_expected(dta, box_with_array)
+        assert_invalid_comparison(obj, other, box_with_array)
+
+    def test_dt64arr_cmp_mixed_invalid(self, tz_naive_fixture):
+        tz = tz_naive_fixture
+
+        dta = date_range("1970-01-01", freq="h", periods=5, tz=tz)._data
+
+        other = np.array([0, 1, 2, dta[3], Timedelta(days=1)])
+        result = dta == other
+        expected = np.array([False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dta != other
+        tm.assert_numpy_array_equal(result, ~expected)
+
+        msg = "Invalid comparison between|Cannot compare type|not supported between"
+        with pytest.raises(TypeError, match=msg):
+            dta < other
+        with pytest.raises(TypeError, match=msg):
+            dta > other
+        with pytest.raises(TypeError, match=msg):
+            dta <= other
+        with pytest.raises(TypeError, match=msg):
+            dta >= other
+
+    def test_dt64arr_nat_comparison(self, tz_naive_fixture, box_with_array):
+        # GH#22242, GH#22163 DataFrame considered NaT == ts incorrectly
+        tz = tz_naive_fixture
+        box = box_with_array
+
+        ts = Timestamp("2021-01-01", tz=tz)
+        ser = Series([ts, NaT])
+
+        obj = tm.box_expected(ser, box)
+        xbox = get_upcast_box(obj, ts, True)
+
+        expected = Series([True, False], dtype=np.bool_)
+        expected = tm.box_expected(expected, xbox)
+
+        result = obj == ts
+        tm.assert_equal(result, expected)
+
+
+class TestDatetime64SeriesComparison:
+    # TODO: moved from tests.series.test_operators; needs cleanup
+
+    @pytest.mark.parametrize(
+        "pair",
+        [
+            (
+                [Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")],
+                [NaT, NaT, Timestamp("2011-01-03")],
+            ),
+            (
+                [Timedelta("1 days"), NaT, Timedelta("3 days")],
+                [NaT, NaT, Timedelta("3 days")],
+            ),
+            (
+                [Period("2011-01", freq="M"), NaT, Period("2011-03", freq="M")],
+                [NaT, NaT, Period("2011-03", freq="M")],
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("reverse", [True, False])
+    @pytest.mark.parametrize("dtype", [None, object])
+    @pytest.mark.parametrize(
+        "op, expected",
+        [
+            (operator.eq, Series([False, False, True])),
+            (operator.ne, Series([True, True, False])),
+            (operator.lt, Series([False, False, False])),
+            (operator.gt, Series([False, False, False])),
+            (operator.ge, Series([False, False, True])),
+            (operator.le, Series([False, False, True])),
+        ],
+    )
+    def test_nat_comparisons(
+        self,
+        dtype,
+        index_or_series,
+        reverse,
+        pair,
+        op,
+        expected,
+    ):
+        box = index_or_series
+        lhs, rhs = pair
+        if reverse:
+            # add lhs / rhs switched data
+            lhs, rhs = rhs, lhs
+
+        left = Series(lhs, dtype=dtype)
+        right = box(rhs, dtype=dtype)
+
+        result = op(left, right)
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            [Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")],
+            [Timedelta("1 days"), NaT, Timedelta("3 days")],
+            [Period("2011-01", freq="M"), NaT, Period("2011-03", freq="M")],
+        ],
+    )
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_nat_comparisons_scalar(self, dtype, data, box_with_array):
+        box = box_with_array
+
+        left = Series(data, dtype=dtype)
+        left = tm.box_expected(left, box)
+        xbox = get_upcast_box(left, NaT, True)
+
+        expected = [False, False, False]
+        expected = tm.box_expected(expected, xbox)
+        if box is pd.array and dtype is object:
+            expected = pd.array(expected, dtype="bool")
+
+        tm.assert_equal(left == NaT, expected)
+        tm.assert_equal(NaT == left, expected)
+
+        expected = [True, True, True]
+        expected = tm.box_expected(expected, xbox)
+        if box is pd.array and dtype is object:
+            expected = pd.array(expected, dtype="bool")
+        tm.assert_equal(left != NaT, expected)
+        tm.assert_equal(NaT != left, expected)
+
+        expected = [False, False, False]
+        expected = tm.box_expected(expected, xbox)
+        if box is pd.array and dtype is object:
+            expected = pd.array(expected, dtype="bool")
+        tm.assert_equal(left < NaT, expected)
+        tm.assert_equal(NaT > left, expected)
+        tm.assert_equal(left <= NaT, expected)
+        tm.assert_equal(NaT >= left, expected)
+
+        tm.assert_equal(left > NaT, expected)
+        tm.assert_equal(NaT < left, expected)
+        tm.assert_equal(left >= NaT, expected)
+        tm.assert_equal(NaT <= left, expected)
+
+    @pytest.mark.parametrize("val", [datetime(2000, 1, 4), datetime(2000, 1, 5)])
+    def test_series_comparison_scalars(self, val):
+        series = Series(date_range("1/1/2000", periods=10))
+
+        result = series > val
+        expected = Series([x > val for x in series])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "left,right", [("lt", "gt"), ("le", "ge"), ("eq", "eq"), ("ne", "ne")]
+    )
+    def test_timestamp_compare_series(self, left, right):
+        # see gh-4982
+        # Make sure we can compare Timestamps on the right AND left hand side.
+        ser = Series(date_range("20010101", periods=10), name="dates")
+        s_nat = ser.copy(deep=True)
+
+        ser[0] = Timestamp("nat")
+        ser[3] = Timestamp("nat")
+
+        left_f = getattr(operator, left)
+        right_f = getattr(operator, right)
+
+        # No NaT
+        expected = left_f(ser, Timestamp("20010109"))
+        result = right_f(Timestamp("20010109"), ser)
+        tm.assert_series_equal(result, expected)
+
+        # NaT
+        expected = left_f(ser, Timestamp("nat"))
+        result = right_f(Timestamp("nat"), ser)
+        tm.assert_series_equal(result, expected)
+
+        # Compare to Timestamp with series containing NaT
+        expected = left_f(s_nat, Timestamp("20010109"))
+        result = right_f(Timestamp("20010109"), s_nat)
+        tm.assert_series_equal(result, expected)
+
+        # Compare to NaT with series containing NaT
+        expected = left_f(s_nat, NaT)
+        result = right_f(NaT, s_nat)
+        tm.assert_series_equal(result, expected)
+
+    def test_dt64arr_timestamp_equality(self, box_with_array):
+        # GH#11034
+        box = box_with_array
+
+        ser = Series([Timestamp("2000-01-29 01:59:00"), Timestamp("2000-01-30"), NaT])
+        ser = tm.box_expected(ser, box)
+        xbox = get_upcast_box(ser, ser, True)
+
+        result = ser != ser
+        expected = tm.box_expected([False, False, True], xbox)
+        tm.assert_equal(result, expected)
+
+        if box is pd.DataFrame:
+            # alignment for frame vs series comparisons deprecated
+            #  in GH#46795 enforced 2.0
+            with pytest.raises(ValueError, match="not aligned"):
+                ser != ser[0]
+
+        else:
+            result = ser != ser[0]
+            expected = tm.box_expected([False, True, True], xbox)
+            tm.assert_equal(result, expected)
+
+        if box is pd.DataFrame:
+            # alignment for frame vs series comparisons deprecated
+            #  in GH#46795 enforced 2.0
+            with pytest.raises(ValueError, match="not aligned"):
+                ser != ser[2]
+        else:
+            result = ser != ser[2]
+            expected = tm.box_expected([True, True, True], xbox)
+            tm.assert_equal(result, expected)
+
+        result = ser == ser
+        expected = tm.box_expected([True, True, False], xbox)
+        tm.assert_equal(result, expected)
+
+        if box is pd.DataFrame:
+            # alignment for frame vs series comparisons deprecated
+            #  in GH#46795 enforced 2.0
+            with pytest.raises(ValueError, match="not aligned"):
+                ser == ser[0]
+        else:
+            result = ser == ser[0]
+            expected = tm.box_expected([True, False, False], xbox)
+            tm.assert_equal(result, expected)
+
+        if box is pd.DataFrame:
+            # alignment for frame vs series comparisons deprecated
+            #  in GH#46795 enforced 2.0
+            with pytest.raises(ValueError, match="not aligned"):
+                ser == ser[2]
+        else:
+            result = ser == ser[2]
+            expected = tm.box_expected([False, False, False], xbox)
+            tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "datetimelike",
+        [
+            Timestamp("20130101"),
+            datetime(2013, 1, 1),
+            np.datetime64("2013-01-01T00:00", "ns"),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "op,expected",
+        [
+            (operator.lt, [True, False, False, False]),
+            (operator.le, [True, True, False, False]),
+            (operator.eq, [False, True, False, False]),
+            (operator.gt, [False, False, False, True]),
+        ],
+    )
+    def test_dt64_compare_datetime_scalar(self, datetimelike, op, expected):
+        # GH#17965, test for ability to compare datetime64[ns] columns
+        #  to datetimelike
+        ser = Series(
+            [
+                Timestamp("20120101"),
+                Timestamp("20130101"),
+                np.nan,
+                Timestamp("20130103"),
+            ],
+            name="A",
+        )
+        result = op(ser, datetimelike)
+        expected = Series(expected, name="A")
+        tm.assert_series_equal(result, expected)
+
+
+class TestDatetimeIndexComparisons:
+    # TODO: moved from tests.indexes.test_base; parametrize and de-duplicate
+    def test_comparators(self, comparison_op):
+        index = date_range("2020-01-01", periods=10)
+        element = index[len(index) // 2]
+        element = Timestamp(element).to_datetime64()
+
+        arr = np.array(index)
+        arr_result = comparison_op(arr, element)
+        index_result = comparison_op(index, element)
+
+        assert isinstance(index_result, np.ndarray)
+        tm.assert_numpy_array_equal(arr_result, index_result)
+
+    @pytest.mark.parametrize(
+        "other",
+        [datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")],
+    )
+    def test_dti_cmp_datetimelike(self, other, tz_naive_fixture):
+        tz = tz_naive_fixture
+        dti = date_range("2016-01-01", periods=2, tz=tz)
+        if tz is not None:
+            if isinstance(other, np.datetime64):
+                pytest.skip(f"{type(other).__name__} is not tz aware")
+            other = localize_pydatetime(other, dti.tzinfo)
+
+        result = dti == other
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti > other
+        expected = np.array([False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti >= other
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti < other
+        expected = np.array([False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti <= other
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_dti_cmp_nat(self, dtype, box_with_array):
+        left = DatetimeIndex([Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")])
+        right = DatetimeIndex([NaT, NaT, Timestamp("2011-01-03")])
+
+        left = tm.box_expected(left, box_with_array)
+        right = tm.box_expected(right, box_with_array)
+        xbox = get_upcast_box(left, right, True)
+
+        lhs, rhs = left, right
+        if dtype is object:
+            lhs, rhs = left.astype(object), right.astype(object)
+
+        result = rhs == lhs
+        expected = np.array([False, False, True])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+        result = lhs != rhs
+        expected = np.array([True, True, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+        expected = np.array([False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(lhs == NaT, expected)
+        tm.assert_equal(NaT == rhs, expected)
+
+        expected = np.array([True, True, True])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(lhs != NaT, expected)
+        tm.assert_equal(NaT != lhs, expected)
+
+        expected = np.array([False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(lhs < NaT, expected)
+        tm.assert_equal(NaT > lhs, expected)
+
+    def test_dti_cmp_nat_behaves_like_float_cmp_nan(self):
+        fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0])
+        fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0])
+
+        didx1 = DatetimeIndex(
+            ["2014-01-01", NaT, "2014-03-01", NaT, "2014-05-01", "2014-07-01"]
+        )
+        didx2 = DatetimeIndex(
+            ["2014-02-01", "2014-03-01", NaT, NaT, "2014-06-01", "2014-07-01"]
+        )
+        darr = np.array(
+            [
+                np.datetime64("2014-02-01 00:00"),
+                np.datetime64("2014-03-01 00:00"),
+                np.datetime64("nat"),
+                np.datetime64("nat"),
+                np.datetime64("2014-06-01 00:00"),
+                np.datetime64("2014-07-01 00:00"),
+            ]
+        )
+
+        cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]
+
+        # Check pd.NaT is handles as the same as np.nan
+        with tm.assert_produces_warning(None):
+            for idx1, idx2 in cases:
+                result = idx1 < idx2
+                expected = np.array([True, False, False, False, True, False])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx2 > idx1
+                expected = np.array([True, False, False, False, True, False])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 <= idx2
+                expected = np.array([True, False, False, False, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx2 >= idx1
+                expected = np.array([True, False, False, False, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 == idx2
+                expected = np.array([False, False, False, False, False, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 != idx2
+                expected = np.array([True, True, True, True, True, False])
+                tm.assert_numpy_array_equal(result, expected)
+
+        with tm.assert_produces_warning(None):
+            for idx1, val in [(fidx1, np.nan), (didx1, NaT)]:
+                result = idx1 < val
+                expected = np.array([False, False, False, False, False, False])
+                tm.assert_numpy_array_equal(result, expected)
+                result = idx1 > val
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 <= val
+                tm.assert_numpy_array_equal(result, expected)
+                result = idx1 >= val
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 == val
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 != val
+                expected = np.array([True, True, True, True, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+        # Check pd.NaT is handles as the same as np.nan
+        with tm.assert_produces_warning(None):
+            for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]:
+                result = idx1 < val
+                expected = np.array([True, False, False, False, False, False])
+                tm.assert_numpy_array_equal(result, expected)
+                result = idx1 > val
+                expected = np.array([False, False, False, False, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 <= val
+                expected = np.array([True, False, True, False, False, False])
+                tm.assert_numpy_array_equal(result, expected)
+                result = idx1 >= val
+                expected = np.array([False, False, True, False, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 == val
+                expected = np.array([False, False, True, False, False, False])
+                tm.assert_numpy_array_equal(result, expected)
+
+                result = idx1 != val
+                expected = np.array([True, True, False, True, True, True])
+                tm.assert_numpy_array_equal(result, expected)
+
+    def test_comparison_tzawareness_compat(self, comparison_op, box_with_array):
+        # GH#18162
+        op = comparison_op
+        box = box_with_array
+
+        dr = date_range("2016-01-01", periods=6)
+        dz = dr.tz_localize("US/Pacific")
+
+        dr = tm.box_expected(dr, box)
+        dz = tm.box_expected(dz, box)
+
+        if box is pd.DataFrame:
+            tolist = lambda x: x.astype(object).values.tolist()[0]
+        else:
+            tolist = list
+
+        if op not in [operator.eq, operator.ne]:
+            msg = (
+                r"Invalid comparison between dtype=datetime64\[ns.*\] "
+                "and (Timestamp|DatetimeArray|list|ndarray)"
+            )
+            with pytest.raises(TypeError, match=msg):
+                op(dr, dz)
+
+            with pytest.raises(TypeError, match=msg):
+                op(dr, tolist(dz))
+            with pytest.raises(TypeError, match=msg):
+                op(dr, np.array(tolist(dz), dtype=object))
+            with pytest.raises(TypeError, match=msg):
+                op(dz, dr)
+
+            with pytest.raises(TypeError, match=msg):
+                op(dz, tolist(dr))
+            with pytest.raises(TypeError, match=msg):
+                op(dz, np.array(tolist(dr), dtype=object))
+
+        # The aware==aware and naive==naive comparisons should *not* raise
+        assert np.all(dr == dr)
+        assert np.all(dr == tolist(dr))
+        assert np.all(tolist(dr) == dr)
+        assert np.all(np.array(tolist(dr), dtype=object) == dr)
+        assert np.all(dr == np.array(tolist(dr), dtype=object))
+
+        assert np.all(dz == dz)
+        assert np.all(dz == tolist(dz))
+        assert np.all(tolist(dz) == dz)
+        assert np.all(np.array(tolist(dz), dtype=object) == dz)
+        assert np.all(dz == np.array(tolist(dz), dtype=object))
+
+    def test_comparison_tzawareness_compat_scalars(self, comparison_op, box_with_array):
+        # GH#18162
+        op = comparison_op
+
+        dr = date_range("2016-01-01", periods=6)
+        dz = dr.tz_localize("US/Pacific")
+
+        dr = tm.box_expected(dr, box_with_array)
+        dz = tm.box_expected(dz, box_with_array)
+
+        # Check comparisons against scalar Timestamps
+        ts = Timestamp("2000-03-14 01:59")
+        ts_tz = Timestamp("2000-03-14 01:59", tz="Europe/Amsterdam")
+
+        assert np.all(dr > ts)
+        msg = r"Invalid comparison between dtype=datetime64\[ns.*\] and Timestamp"
+        if op not in [operator.eq, operator.ne]:
+            with pytest.raises(TypeError, match=msg):
+                op(dr, ts_tz)
+
+        assert np.all(dz > ts_tz)
+        if op not in [operator.eq, operator.ne]:
+            with pytest.raises(TypeError, match=msg):
+                op(dz, ts)
+
+        if op not in [operator.eq, operator.ne]:
+            # GH#12601: Check comparison against Timestamps and DatetimeIndex
+            with pytest.raises(TypeError, match=msg):
+                op(ts, dz)
+
+    @pytest.mark.parametrize(
+        "other",
+        [datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")],
+    )
+    # Bug in NumPy? https://github.com/numpy/numpy/issues/13841
+    # Raising in __eq__ will fallback to NumPy, which warns, fails,
+    # then re-raises the original exception. So we just need to ignore.
+    @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
+    def test_scalar_comparison_tzawareness(
+        self, comparison_op, other, tz_aware_fixture, box_with_array
+    ):
+        op = comparison_op
+        tz = tz_aware_fixture
+        dti = date_range("2016-01-01", periods=2, tz=tz)
+
+        dtarr = tm.box_expected(dti, box_with_array)
+        xbox = get_upcast_box(dtarr, other, True)
+        if op in [operator.eq, operator.ne]:
+            exbool = op is operator.ne
+            expected = np.array([exbool, exbool], dtype=bool)
+            expected = tm.box_expected(expected, xbox)
+
+            result = op(dtarr, other)
+            tm.assert_equal(result, expected)
+
+            result = op(other, dtarr)
+            tm.assert_equal(result, expected)
+        else:
+            msg = (
+                r"Invalid comparison between dtype=datetime64\[ns, .*\] "
+                f"and {type(other).__name__}"
+            )
+            with pytest.raises(TypeError, match=msg):
+                op(dtarr, other)
+            with pytest.raises(TypeError, match=msg):
+                op(other, dtarr)
+
+    def test_nat_comparison_tzawareness(self, comparison_op):
+        # GH#19276
+        # tzaware DatetimeIndex should not raise when compared to NaT
+        op = comparison_op
+
+        dti = DatetimeIndex(
+            ["2014-01-01", NaT, "2014-03-01", NaT, "2014-05-01", "2014-07-01"]
+        )
+        expected = np.array([op == operator.ne] * len(dti))
+        result = op(dti, NaT)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = op(dti.tz_localize("US/Pacific"), NaT)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_dti_cmp_str(self, tz_naive_fixture):
+        # GH#22074
+        # regardless of tz, we expect these comparisons are valid
+        tz = tz_naive_fixture
+        rng = date_range("1/1/2000", periods=10, tz=tz)
+        other = "1/1/2000"
+
+        result = rng == other
+        expected = np.array([True] + [False] * 9)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng != other
+        expected = np.array([False] + [True] * 9)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng < other
+        expected = np.array([False] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng <= other
+        expected = np.array([True] + [False] * 9)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng > other
+        expected = np.array([False] + [True] * 9)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng >= other
+        expected = np.array([True] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_dti_cmp_list(self):
+        rng = date_range("1/1/2000", periods=10)
+
+        result = rng == list(rng)
+        expected = rng == rng
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            pd.timedelta_range("1D", periods=10),
+            pd.timedelta_range("1D", periods=10).to_series(),
+            pd.timedelta_range("1D", periods=10).asi8.view("m8[ns]"),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_dti_cmp_tdi_tzawareness(self, other):
+        # GH#22074
+        # reversion test that we _don't_ call _assert_tzawareness_compat
+        # when comparing against TimedeltaIndex
+        dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo")
+
+        result = dti == other
+        expected = np.array([False] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = dti != other
+        expected = np.array([True] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+        msg = "Invalid comparison between"
+        with pytest.raises(TypeError, match=msg):
+            dti < other
+        with pytest.raises(TypeError, match=msg):
+            dti <= other
+        with pytest.raises(TypeError, match=msg):
+            dti > other
+        with pytest.raises(TypeError, match=msg):
+            dti >= other
+
+    def test_dti_cmp_object_dtype(self):
+        # GH#22074
+        dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo")
+
+        other = dti.astype("O")
+
+        result = dti == other
+        expected = np.array([True] * 10)
+        tm.assert_numpy_array_equal(result, expected)
+
+        other = dti.tz_localize(None)
+        result = dti != other
+        tm.assert_numpy_array_equal(result, expected)
+
+        other = np.array(list(dti[:5]) + [Timedelta(days=1)] * 5)
+        result = dti == other
+        expected = np.array([True] * 5 + [False] * 5)
+        tm.assert_numpy_array_equal(result, expected)
+        msg = ">=' not supported between instances of 'Timestamp' and 'Timedelta'"
+        with pytest.raises(TypeError, match=msg):
+            dti >= other
+
+
+# ------------------------------------------------------------------
+# Arithmetic
+
+
+class TestDatetime64Arithmetic:
+    # This class is intended for "finished" tests that are fully parametrized
+    #  over DataFrame/Series/Index/DatetimeArray
+
+    # -------------------------------------------------------------
+    # Addition/Subtraction of timedelta-like
+
+    @pytest.mark.arm_slow
+    def test_dt64arr_add_timedeltalike_scalar(
+        self, tz_naive_fixture, two_hours, box_with_array
+    ):
+        # GH#22005, GH#22163 check DataFrame doesn't raise TypeError
+        tz = tz_naive_fixture
+
+        rng = date_range("2000-01-01", "2000-02-01", tz=tz)
+        expected = date_range("2000-01-01 02:00", "2000-02-01 02:00", tz=tz)
+
+        rng = tm.box_expected(rng, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = rng + two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours + rng
+        tm.assert_equal(result, expected)
+
+        rng += two_hours
+        tm.assert_equal(rng, expected)
+
+    def test_dt64arr_sub_timedeltalike_scalar(
+        self, tz_naive_fixture, two_hours, box_with_array
+    ):
+        tz = tz_naive_fixture
+
+        rng = date_range("2000-01-01", "2000-02-01", tz=tz)
+        expected = date_range("1999-12-31 22:00", "2000-01-31 22:00", tz=tz)
+
+        rng = tm.box_expected(rng, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = rng - two_hours
+        tm.assert_equal(result, expected)
+
+        rng -= two_hours
+        tm.assert_equal(rng, expected)
+
+    def test_dt64_array_sub_dt_with_different_timezone(self, box_with_array):
+        t1 = date_range("20130101", periods=3).tz_localize("US/Eastern")
+        t1 = tm.box_expected(t1, box_with_array)
+        t2 = Timestamp("20130101").tz_localize("CET")
+        tnaive = Timestamp(20130101)
+
+        result = t1 - t2
+        expected = TimedeltaIndex(
+            ["0 days 06:00:00", "1 days 06:00:00", "2 days 06:00:00"]
+        )
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = t2 - t1
+        expected = TimedeltaIndex(
+            ["-1 days +18:00:00", "-2 days +18:00:00", "-3 days +18:00:00"]
+        )
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects"
+        with pytest.raises(TypeError, match=msg):
+            t1 - tnaive
+
+        with pytest.raises(TypeError, match=msg):
+            tnaive - t1
+
+    def test_dt64_array_sub_dt64_array_with_different_timezone(self, box_with_array):
+        t1 = date_range("20130101", periods=3).tz_localize("US/Eastern")
+        t1 = tm.box_expected(t1, box_with_array)
+        t2 = date_range("20130101", periods=3).tz_localize("CET")
+        t2 = tm.box_expected(t2, box_with_array)
+        tnaive = date_range("20130101", periods=3)
+
+        result = t1 - t2
+        expected = TimedeltaIndex(
+            ["0 days 06:00:00", "0 days 06:00:00", "0 days 06:00:00"]
+        )
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = t2 - t1
+        expected = TimedeltaIndex(
+            ["-1 days +18:00:00", "-1 days +18:00:00", "-1 days +18:00:00"]
+        )
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects"
+        with pytest.raises(TypeError, match=msg):
+            t1 - tnaive
+
+        with pytest.raises(TypeError, match=msg):
+            tnaive - t1
+
+    def test_dt64arr_add_sub_td64_nat(self, box_with_array, tz_naive_fixture):
+        # GH#23320 special handling for timedelta64("NaT")
+        tz = tz_naive_fixture
+
+        dti = date_range("1994-04-01", periods=9, tz=tz, freq="QS")
+        other = np.timedelta64("NaT")
+        expected = DatetimeIndex(["NaT"] * 9, tz=tz).as_unit("ns")
+
+        obj = tm.box_expected(dti, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = obj + other
+        tm.assert_equal(result, expected)
+        result = other + obj
+        tm.assert_equal(result, expected)
+        result = obj - other
+        tm.assert_equal(result, expected)
+        msg = "cannot subtract"
+        with pytest.raises(TypeError, match=msg):
+            other - obj
+
+    def test_dt64arr_add_sub_td64ndarray(self, tz_naive_fixture, box_with_array):
+        tz = tz_naive_fixture
+        dti = date_range("2016-01-01", periods=3, tz=tz)
+        tdi = TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"])
+        tdarr = tdi.values
+
+        expected = date_range("2015-12-31", "2016-01-02", periods=3, tz=tz)
+
+        dtarr = tm.box_expected(dti, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = dtarr + tdarr
+        tm.assert_equal(result, expected)
+        result = tdarr + dtarr
+        tm.assert_equal(result, expected)
+
+        expected = date_range("2016-01-02", "2016-01-04", periods=3, tz=tz)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = dtarr - tdarr
+        tm.assert_equal(result, expected)
+        msg = "cannot subtract|(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            tdarr - dtarr
+
+    # -----------------------------------------------------------------
+    # Subtraction of datetime-like scalars
+
+    @pytest.mark.parametrize(
+        "ts",
+        [
+            Timestamp("2013-01-01"),
+            Timestamp("2013-01-01").to_pydatetime(),
+            Timestamp("2013-01-01").to_datetime64(),
+            # GH#7996, GH#22163 ensure non-nano datetime64 is converted to nano
+            #  for DataFrame operation
+            np.datetime64("2013-01-01", "D"),
+        ],
+    )
+    def test_dt64arr_sub_dtscalar(self, box_with_array, ts):
+        # GH#8554, GH#22163 DataFrame op should _not_ return dt64 dtype
+        idx = date_range("2013-01-01", periods=3)._with_freq(None)
+        idx = tm.box_expected(idx, box_with_array)
+
+        expected = TimedeltaIndex(["0 Days", "1 Day", "2 Days"])
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = idx - ts
+        tm.assert_equal(result, expected)
+
+        result = ts - idx
+        tm.assert_equal(result, -expected)
+        tm.assert_equal(result, -expected)
+
+    def test_dt64arr_sub_timestamp_tzaware(self, box_with_array):
+        ser = date_range("2014-03-17", periods=2, freq="D", tz="US/Eastern")
+        ser = ser._with_freq(None)
+        ts = ser[0]
+
+        ser = tm.box_expected(ser, box_with_array)
+
+        delta_series = Series([np.timedelta64(0, "D"), np.timedelta64(1, "D")])
+        expected = tm.box_expected(delta_series, box_with_array)
+
+        tm.assert_equal(ser - ts, expected)
+        tm.assert_equal(ts - ser, -expected)
+
+    def test_dt64arr_sub_NaT(self, box_with_array, unit):
+        # GH#18808
+        dti = DatetimeIndex([NaT, Timestamp("19900315")]).as_unit(unit)
+        ser = tm.box_expected(dti, box_with_array)
+
+        result = ser - NaT
+        expected = Series([NaT, NaT], dtype=f"timedelta64[{unit}]")
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        dti_tz = dti.tz_localize("Asia/Tokyo")
+        ser_tz = tm.box_expected(dti_tz, box_with_array)
+
+        result = ser_tz - NaT
+        expected = Series([NaT, NaT], dtype=f"timedelta64[{unit}]")
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+    # -------------------------------------------------------------
+    # Subtraction of datetime-like array-like
+
+    def test_dt64arr_sub_dt64object_array(self, box_with_array, tz_naive_fixture):
+        dti = date_range("2016-01-01", periods=3, tz=tz_naive_fixture)
+        expected = dti - dti
+
+        obj = tm.box_expected(dti, box_with_array)
+        expected = tm.box_expected(expected, box_with_array).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = obj - obj.astype(object)
+        tm.assert_equal(result, expected)
+
+    def test_dt64arr_naive_sub_dt64ndarray(self, box_with_array):
+        dti = date_range("2016-01-01", periods=3, tz=None)
+        dt64vals = dti.values
+
+        dtarr = tm.box_expected(dti, box_with_array)
+
+        expected = dtarr - dtarr
+        result = dtarr - dt64vals
+        tm.assert_equal(result, expected)
+        result = dt64vals - dtarr
+        tm.assert_equal(result, expected)
+
+    def test_dt64arr_aware_sub_dt64ndarray_raises(
+        self, tz_aware_fixture, box_with_array
+    ):
+        tz = tz_aware_fixture
+        dti = date_range("2016-01-01", periods=3, tz=tz)
+        dt64vals = dti.values
+
+        dtarr = tm.box_expected(dti, box_with_array)
+        msg = "Cannot subtract tz-naive and tz-aware datetime"
+        with pytest.raises(TypeError, match=msg):
+            dtarr - dt64vals
+        with pytest.raises(TypeError, match=msg):
+            dt64vals - dtarr
+
+    # -------------------------------------------------------------
+    # Addition of datetime-like others (invalid)
+
+    def test_dt64arr_add_dtlike_raises(self, tz_naive_fixture, box_with_array):
+        # GH#22163 ensure DataFrame doesn't cast Timestamp to i8
+        # GH#9631
+        tz = tz_naive_fixture
+
+        dti = date_range("2016-01-01", periods=3, tz=tz)
+        if tz is None:
+            dti2 = dti.tz_localize("US/Eastern")
+        else:
+            dti2 = dti.tz_localize(None)
+        dtarr = tm.box_expected(dti, box_with_array)
+
+        assert_cannot_add(dtarr, dti.values)
+        assert_cannot_add(dtarr, dti)
+        assert_cannot_add(dtarr, dtarr)
+        assert_cannot_add(dtarr, dti[0])
+        assert_cannot_add(dtarr, dti[0].to_pydatetime())
+        assert_cannot_add(dtarr, dti[0].to_datetime64())
+        assert_cannot_add(dtarr, dti2[0])
+        assert_cannot_add(dtarr, dti2[0].to_pydatetime())
+        assert_cannot_add(dtarr, np.datetime64("2011-01-01", "D"))
+
+    # -------------------------------------------------------------
+    # Other Invalid Addition/Subtraction
+
+    # Note: freq here includes both Tick and non-Tick offsets; this is
+    #  relevant because historically integer-addition was allowed if we had
+    #  a freq.
+    @pytest.mark.parametrize("freq", ["h", "D", "W", "2ME", "MS", "QE", "B", None])
+    @pytest.mark.parametrize("dtype", [None, "uint8"])
+    def test_dt64arr_addsub_intlike(
+        self, request, dtype, index_or_series_or_array, freq, tz_naive_fixture
+    ):
+        # GH#19959, GH#19123, GH#19012
+        # GH#55860 use index_or_series_or_array instead of box_with_array
+        #  bc DataFrame alignment makes it inapplicable
+        tz = tz_naive_fixture
+
+        if freq is None:
+            dti = DatetimeIndex(["NaT", "2017-04-05 06:07:08"], tz=tz)
+        else:
+            dti = date_range("2016-01-01", periods=2, freq=freq, tz=tz)
+
+        obj = index_or_series_or_array(dti)
+        other = np.array([4, -1])
+        if dtype is not None:
+            other = other.astype(dtype)
+
+        msg = "|".join(
+            [
+                "Addition/subtraction of integers",
+                "cannot subtract DatetimeArray from",
+                # IntegerArray
+                "can only perform ops with numeric values",
+                "unsupported operand type.*Categorical",
+                r"unsupported operand type\(s\) for -: 'int' and 'Timestamp'",
+            ]
+        )
+        assert_invalid_addsub_type(obj, 1, msg)
+        assert_invalid_addsub_type(obj, np.int64(2), msg)
+        assert_invalid_addsub_type(obj, np.array(3, dtype=np.int64), msg)
+        assert_invalid_addsub_type(obj, other, msg)
+        assert_invalid_addsub_type(obj, np.array(other), msg)
+        assert_invalid_addsub_type(obj, pd.array(other), msg)
+        assert_invalid_addsub_type(obj, pd.Categorical(other), msg)
+        assert_invalid_addsub_type(obj, pd.Index(other), msg)
+        assert_invalid_addsub_type(obj, Series(other), msg)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            3.14,
+            np.array([2.0, 3.0]),
+            # GH#13078 datetime +/- Period is invalid
+            Period("2011-01-01", freq="D"),
+            # https://github.com/pandas-dev/pandas/issues/10329
+            time(1, 2, 3),
+        ],
+    )
+    @pytest.mark.parametrize("dti_freq", [None, "D"])
+    def test_dt64arr_add_sub_invalid(self, dti_freq, other, box_with_array):
+        dti = DatetimeIndex(["2011-01-01", "2011-01-02"], freq=dti_freq)
+        dtarr = tm.box_expected(dti, box_with_array)
+        msg = "|".join(
+            [
+                "unsupported operand type",
+                "cannot (add|subtract)",
+                "cannot use operands with types",
+                "ufunc '?(add|subtract)'? cannot use operands with types",
+                "Concatenation operation is not implemented for NumPy arrays",
+            ]
+        )
+        assert_invalid_addsub_type(dtarr, other, msg)
+
+    @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "h"])
+    @pytest.mark.parametrize("dti_freq", [None, "D"])
+    def test_dt64arr_add_sub_parr(
+        self, dti_freq, pi_freq, box_with_array, box_with_array2
+    ):
+        # GH#20049 subtracting PeriodIndex should raise TypeError
+        dti = DatetimeIndex(["2011-01-01", "2011-01-02"], freq=dti_freq)
+        pi = dti.to_period(pi_freq)
+
+        dtarr = tm.box_expected(dti, box_with_array)
+        parr = tm.box_expected(pi, box_with_array2)
+        msg = "|".join(
+            [
+                "cannot (add|subtract)",
+                "unsupported operand",
+                "descriptor.*requires",
+                "ufunc.*cannot use operands",
+            ]
+        )
+        assert_invalid_addsub_type(dtarr, parr, msg)
+
+    @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
+    def test_dt64arr_addsub_time_objects_raises(self, box_with_array, tz_naive_fixture):
+        # https://github.com/pandas-dev/pandas/issues/10329
+
+        tz = tz_naive_fixture
+
+        obj1 = date_range("2012-01-01", periods=3, tz=tz)
+        obj2 = [time(i, i, i) for i in range(3)]
+
+        obj1 = tm.box_expected(obj1, box_with_array)
+        obj2 = tm.box_expected(obj2, box_with_array)
+
+        msg = "|".join(
+            [
+                "unsupported operand",
+                "cannot subtract DatetimeArray from ndarray",
+            ]
+        )
+        # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being
+        # applied to Series or DatetimeIndex
+        # we aren't testing that here, so ignore.
+        assert_invalid_addsub_type(obj1, obj2, msg=msg)
+
+    # -------------------------------------------------------------
+    # Other invalid operations
+
+    @pytest.mark.parametrize(
+        "dt64_series",
+        [
+            Series([Timestamp("19900315"), Timestamp("19900315")]),
+            Series([NaT, Timestamp("19900315")]),
+            Series([NaT, NaT], dtype="datetime64[ns]"),
+        ],
+    )
+    @pytest.mark.parametrize("one", [1, 1.0, np.array(1)])
+    def test_dt64_mul_div_numeric_invalid(self, one, dt64_series, box_with_array):
+        obj = tm.box_expected(dt64_series, box_with_array)
+
+        msg = "cannot perform .* with this index type"
+
+        # multiplication
+        with pytest.raises(TypeError, match=msg):
+            obj * one
+        with pytest.raises(TypeError, match=msg):
+            one * obj
+
+        # division
+        with pytest.raises(TypeError, match=msg):
+            obj / one
+        with pytest.raises(TypeError, match=msg):
+            one / obj
+
+
+class TestDatetime64DateOffsetArithmetic:
+    # -------------------------------------------------------------
+    # Tick DateOffsets
+
+    # TODO: parametrize over timezone?
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    def test_dt64arr_series_add_tick_DateOffset(self, box_with_array, unit):
+        # GH#4532
+        # operate with pd.offsets
+        ser = Series(
+            [Timestamp("20130101 9:01"), Timestamp("20130101 9:02")]
+        ).dt.as_unit(unit)
+        expected = Series(
+            [Timestamp("20130101 9:01:05"), Timestamp("20130101 9:02:05")]
+        ).dt.as_unit(unit)
+
+        ser = tm.box_expected(ser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = ser + pd.offsets.Second(5)
+        tm.assert_equal(result, expected)
+
+        result2 = pd.offsets.Second(5) + ser
+        tm.assert_equal(result2, expected)
+
+    def test_dt64arr_series_sub_tick_DateOffset(self, box_with_array):
+        # GH#4532
+        # operate with pd.offsets
+        ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
+        expected = Series(
+            [Timestamp("20130101 9:00:55"), Timestamp("20130101 9:01:55")]
+        )
+
+        ser = tm.box_expected(ser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = ser - pd.offsets.Second(5)
+        tm.assert_equal(result, expected)
+
+        result2 = -pd.offsets.Second(5) + ser
+        tm.assert_equal(result2, expected)
+        msg = "(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            pd.offsets.Second(5) - ser
+
+    @pytest.mark.parametrize(
+        "cls_name", ["Day", "Hour", "Minute", "Second", "Milli", "Micro", "Nano"]
+    )
+    def test_dt64arr_add_sub_tick_DateOffset_smoke(self, cls_name, box_with_array):
+        # GH#4532
+        # smoke tests for valid DateOffsets
+        ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
+        ser = tm.box_expected(ser, box_with_array)
+
+        offset_cls = getattr(pd.offsets, cls_name)
+        ser + offset_cls(5)
+        offset_cls(5) + ser
+        ser - offset_cls(5)
+
+    def test_dti_add_tick_tzaware(self, tz_aware_fixture, box_with_array):
+        # GH#21610, GH#22163 ensure DataFrame doesn't return object-dtype
+        tz = tz_aware_fixture
+        if tz == "US/Pacific":
+            dates = date_range("2012-11-01", periods=3, tz=tz)
+            offset = dates + pd.offsets.Hour(5)
+            assert dates[0] + pd.offsets.Hour(5) == offset[0]
+
+        dates = date_range("2010-11-01 00:00", periods=3, tz=tz, freq="h")
+        expected = DatetimeIndex(
+            ["2010-11-01 05:00", "2010-11-01 06:00", "2010-11-01 07:00"],
+            freq="h",
+            tz=tz,
+        ).as_unit("ns")
+
+        dates = tm.box_expected(dates, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        for scalar in [pd.offsets.Hour(5), np.timedelta64(5, "h"), timedelta(hours=5)]:
+            offset = dates + scalar
+            tm.assert_equal(offset, expected)
+            offset = scalar + dates
+            tm.assert_equal(offset, expected)
+
+            roundtrip = offset - scalar
+            tm.assert_equal(roundtrip, dates)
+
+            msg = "|".join(
+                ["bad operand type for unary -", "cannot subtract DatetimeArray"]
+            )
+            with pytest.raises(TypeError, match=msg):
+                scalar - dates
+
+    # -------------------------------------------------------------
+    # RelativeDelta DateOffsets
+
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    def test_dt64arr_add_sub_relativedelta_offsets(self, box_with_array, unit):
+        # GH#10699
+        vec = DatetimeIndex(
+            [
+                Timestamp("2000-01-05 00:15:00"),
+                Timestamp("2000-01-31 00:23:00"),
+                Timestamp("2000-01-01"),
+                Timestamp("2000-03-31"),
+                Timestamp("2000-02-29"),
+                Timestamp("2000-12-31"),
+                Timestamp("2000-05-15"),
+                Timestamp("2001-06-15"),
+            ]
+        ).as_unit(unit)
+        vec = tm.box_expected(vec, box_with_array)
+        vec_items = vec.iloc[0] if box_with_array is pd.DataFrame else vec
+
+        # DateOffset relativedelta fastpath
+        relative_kwargs = [
+            ("years", 2),
+            ("months", 5),
+            ("days", 3),
+            ("hours", 5),
+            ("minutes", 10),
+            ("seconds", 2),
+            ("microseconds", 5),
+        ]
+        for i, (offset_unit, value) in enumerate(relative_kwargs):
+            off = DateOffset(**{offset_unit: value})
+
+            exp_unit = unit
+            if offset_unit == "microseconds" and unit != "ns":
+                exp_unit = "us"
+
+            # TODO(GH#55564): as_unit will be unnecessary
+            expected = DatetimeIndex([x + off for x in vec_items]).as_unit(exp_unit)
+            expected = tm.box_expected(expected, box_with_array)
+            tm.assert_equal(expected, vec + off)
+
+            expected = DatetimeIndex([x - off for x in vec_items]).as_unit(exp_unit)
+            expected = tm.box_expected(expected, box_with_array)
+            tm.assert_equal(expected, vec - off)
+
+            off = DateOffset(**dict(relative_kwargs[: i + 1]))
+
+            expected = DatetimeIndex([x + off for x in vec_items]).as_unit(exp_unit)
+            expected = tm.box_expected(expected, box_with_array)
+            tm.assert_equal(expected, vec + off)
+
+            expected = DatetimeIndex([x - off for x in vec_items]).as_unit(exp_unit)
+            expected = tm.box_expected(expected, box_with_array)
+            tm.assert_equal(expected, vec - off)
+            msg = "(bad|unsupported) operand type for unary"
+            with pytest.raises(TypeError, match=msg):
+                off - vec
+
+    # -------------------------------------------------------------
+    # Non-Tick, Non-RelativeDelta DateOffsets
+
+    # TODO: redundant with test_dt64arr_add_sub_DateOffset?  that includes
+    #  tz-aware cases which this does not
+    @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
+    @pytest.mark.parametrize(
+        "cls_and_kwargs",
+        [
+            "YearBegin",
+            ("YearBegin", {"month": 5}),
+            "YearEnd",
+            ("YearEnd", {"month": 5}),
+            "MonthBegin",
+            "MonthEnd",
+            "SemiMonthEnd",
+            "SemiMonthBegin",
+            "Week",
+            ("Week", {"weekday": 3}),
+            "Week",
+            ("Week", {"weekday": 6}),
+            "BusinessDay",
+            "BDay",
+            "QuarterEnd",
+            "QuarterBegin",
+            "CustomBusinessDay",
+            "CDay",
+            "CBMonthEnd",
+            "CBMonthBegin",
+            "BMonthBegin",
+            "BMonthEnd",
+            "BusinessHour",
+            "BYearBegin",
+            "BYearEnd",
+            "BQuarterBegin",
+            ("LastWeekOfMonth", {"weekday": 2}),
+            (
+                "FY5253Quarter",
+                {
+                    "qtr_with_extra_week": 1,
+                    "startingMonth": 1,
+                    "weekday": 2,
+                    "variation": "nearest",
+                },
+            ),
+            ("FY5253", {"weekday": 0, "startingMonth": 2, "variation": "nearest"}),
+            ("WeekOfMonth", {"weekday": 2, "week": 2}),
+            "Easter",
+            ("DateOffset", {"day": 4}),
+            ("DateOffset", {"month": 5}),
+        ],
+    )
+    @pytest.mark.parametrize("normalize", [True, False])
+    @pytest.mark.parametrize("n", [0, 5])
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    def test_dt64arr_add_sub_DateOffsets(
+        self, box_with_array, n, normalize, cls_and_kwargs, unit, tz
+    ):
+        # GH#10699
+        # assert vectorized operation matches pointwise operations
+
+        if isinstance(cls_and_kwargs, tuple):
+            # If cls_name param is a tuple, then 2nd entry is kwargs for
+            # the offset constructor
+            cls_name, kwargs = cls_and_kwargs
+        else:
+            cls_name = cls_and_kwargs
+            kwargs = {}
+
+        if n == 0 and cls_name in [
+            "WeekOfMonth",
+            "LastWeekOfMonth",
+            "FY5253Quarter",
+            "FY5253",
+        ]:
+            # passing n = 0 is invalid for these offset classes
+            return
+
+        vec = (
+            DatetimeIndex(
+                [
+                    Timestamp("2000-01-05 00:15:00"),
+                    Timestamp("2000-01-31 00:23:00"),
+                    Timestamp("2000-01-01"),
+                    Timestamp("2000-03-31"),
+                    Timestamp("2000-02-29"),
+                    Timestamp("2000-12-31"),
+                    Timestamp("2000-05-15"),
+                    Timestamp("2001-06-15"),
+                ]
+            )
+            .as_unit(unit)
+            .tz_localize(tz)
+        )
+        vec = tm.box_expected(vec, box_with_array)
+        vec_items = vec.iloc[0] if box_with_array is pd.DataFrame else vec
+
+        offset_cls = getattr(pd.offsets, cls_name)
+        offset = offset_cls(n, normalize=normalize, **kwargs)
+
+        # TODO(GH#55564): as_unit will be unnecessary
+        expected = DatetimeIndex([x + offset for x in vec_items]).as_unit(unit)
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(expected, vec + offset)
+        tm.assert_equal(expected, offset + vec)
+
+        expected = DatetimeIndex([x - offset for x in vec_items]).as_unit(unit)
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(expected, vec - offset)
+
+        expected = DatetimeIndex([offset + x for x in vec_items]).as_unit(unit)
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(expected, offset + vec)
+        msg = "(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            offset - vec
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            np.array([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)]),
+            np.array([pd.offsets.DateOffset(years=1), pd.offsets.MonthEnd()]),
+            np.array(  # matching offsets
+                [pd.offsets.DateOffset(years=1), pd.offsets.DateOffset(years=1)]
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("op", [operator.add, roperator.radd, operator.sub])
+    def test_dt64arr_add_sub_offset_array(
+        self, tz_naive_fixture, box_with_array, op, other
+    ):
+        # GH#18849
+        # GH#10699 array of offsets
+
+        tz = tz_naive_fixture
+        dti = date_range("2017-01-01", periods=2, tz=tz)
+        dtarr = tm.box_expected(dti, box_with_array)
+
+        expected = DatetimeIndex([op(dti[n], other[n]) for n in range(len(dti))])
+        expected = tm.box_expected(expected, box_with_array).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = op(dtarr, other)
+        tm.assert_equal(res, expected)
+
+        # Same thing but boxing other
+        other = tm.box_expected(other, box_with_array)
+        if box_with_array is pd.array and op is roperator.radd:
+            # We expect a NumpyExtensionArray, not ndarray[object] here
+            expected = pd.array(expected, dtype=object)
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = op(dtarr, other)
+        tm.assert_equal(res, expected)
+
+    @pytest.mark.parametrize(
+        "op, offset, exp, exp_freq",
+        [
+            (
+                "__add__",
+                DateOffset(months=3, days=10),
+                [
+                    Timestamp("2014-04-11"),
+                    Timestamp("2015-04-11"),
+                    Timestamp("2016-04-11"),
+                    Timestamp("2017-04-11"),
+                ],
+                None,
+            ),
+            (
+                "__add__",
+                DateOffset(months=3),
+                [
+                    Timestamp("2014-04-01"),
+                    Timestamp("2015-04-01"),
+                    Timestamp("2016-04-01"),
+                    Timestamp("2017-04-01"),
+                ],
+                "YS-APR",
+            ),
+            (
+                "__sub__",
+                DateOffset(months=3, days=10),
+                [
+                    Timestamp("2013-09-21"),
+                    Timestamp("2014-09-21"),
+                    Timestamp("2015-09-21"),
+                    Timestamp("2016-09-21"),
+                ],
+                None,
+            ),
+            (
+                "__sub__",
+                DateOffset(months=3),
+                [
+                    Timestamp("2013-10-01"),
+                    Timestamp("2014-10-01"),
+                    Timestamp("2015-10-01"),
+                    Timestamp("2016-10-01"),
+                ],
+                "YS-OCT",
+            ),
+        ],
+    )
+    def test_dti_add_sub_nonzero_mth_offset(
+        self, op, offset, exp, exp_freq, tz_aware_fixture, box_with_array
+    ):
+        # GH 26258
+        tz = tz_aware_fixture
+        date = date_range(start="01 Jan 2014", end="01 Jan 2017", freq="YS", tz=tz)
+        date = tm.box_expected(date, box_with_array, False)
+        mth = getattr(date, op)
+        result = mth(offset)
+
+        expected = DatetimeIndex(exp, tz=tz).as_unit("ns")
+        expected = tm.box_expected(expected, box_with_array, False)
+        tm.assert_equal(result, expected)
+
+    def test_dt64arr_series_add_DateOffset_with_milli(self):
+        # GH 57529
+        dti = DatetimeIndex(
+            [
+                "2000-01-01 00:00:00.012345678",
+                "2000-01-31 00:00:00.012345678",
+                "2000-02-29 00:00:00.012345678",
+            ],
+            dtype="datetime64[ns]",
+        )
+        result = dti + DateOffset(milliseconds=4)
+        expected = DatetimeIndex(
+            [
+                "2000-01-01 00:00:00.016345678",
+                "2000-01-31 00:00:00.016345678",
+                "2000-02-29 00:00:00.016345678",
+            ],
+            dtype="datetime64[ns]",
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = dti + DateOffset(days=1, milliseconds=4)
+        expected = DatetimeIndex(
+            [
+                "2000-01-02 00:00:00.016345678",
+                "2000-02-01 00:00:00.016345678",
+                "2000-03-01 00:00:00.016345678",
+            ],
+            dtype="datetime64[ns]",
+        )
+        tm.assert_index_equal(result, expected)
+
+
+class TestDatetime64OverflowHandling:
+    # TODO: box + de-duplicate
+
+    def test_dt64_overflow_masking(self, box_with_array):
+        # GH#25317
+        left = Series([Timestamp("1969-12-31")], dtype="M8[ns]")
+        right = Series([NaT])
+
+        left = tm.box_expected(left, box_with_array)
+        right = tm.box_expected(right, box_with_array)
+
+        expected = TimedeltaIndex([NaT], dtype="m8[ns]")
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = left - right
+        tm.assert_equal(result, expected)
+
+    def test_dt64_series_arith_overflow(self):
+        # GH#12534, fixed by GH#19024
+        dt = Timestamp("1700-01-31")
+        td = Timedelta("20000 Days")
+        dti = date_range("1949-09-30", freq="100YE", periods=4)
+        ser = Series(dti)
+        msg = "Overflow in int64 addition"
+        with pytest.raises(OverflowError, match=msg):
+            ser - dt
+        with pytest.raises(OverflowError, match=msg):
+            dt - ser
+        with pytest.raises(OverflowError, match=msg):
+            ser + td
+        with pytest.raises(OverflowError, match=msg):
+            td + ser
+
+        ser.iloc[-1] = NaT
+        expected = Series(
+            ["2004-10-03", "2104-10-04", "2204-10-04", "NaT"], dtype="datetime64[ns]"
+        )
+        res = ser + td
+        tm.assert_series_equal(res, expected)
+        res = td + ser
+        tm.assert_series_equal(res, expected)
+
+        ser.iloc[1:] = NaT
+        expected = Series(["91279 Days", "NaT", "NaT", "NaT"], dtype="timedelta64[ns]")
+        res = ser - dt
+        tm.assert_series_equal(res, expected)
+        res = dt - ser
+        tm.assert_series_equal(res, -expected)
+
+    def test_datetimeindex_sub_timestamp_overflow(self):
+        dtimax = pd.to_datetime(["2021-12-28 17:19", Timestamp.max]).as_unit("ns")
+        dtimin = pd.to_datetime(["2021-12-28 17:19", Timestamp.min]).as_unit("ns")
+
+        tsneg = Timestamp("1950-01-01").as_unit("ns")
+        ts_neg_variants = [
+            tsneg,
+            tsneg.to_pydatetime(),
+            tsneg.to_datetime64().astype("datetime64[ns]"),
+            tsneg.to_datetime64().astype("datetime64[D]"),
+        ]
+
+        tspos = Timestamp("1980-01-01").as_unit("ns")
+        ts_pos_variants = [
+            tspos,
+            tspos.to_pydatetime(),
+            tspos.to_datetime64().astype("datetime64[ns]"),
+            tspos.to_datetime64().astype("datetime64[D]"),
+        ]
+        msg = "Overflow in int64 addition"
+        for variant in ts_neg_variants:
+            with pytest.raises(OverflowError, match=msg):
+                dtimax - variant
+
+        expected = Timestamp.max._value - tspos._value
+        for variant in ts_pos_variants:
+            res = dtimax - variant
+            assert res[1]._value == expected
+
+        expected = Timestamp.min._value - tsneg._value
+        for variant in ts_neg_variants:
+            res = dtimin - variant
+            assert res[1]._value == expected
+
+        for variant in ts_pos_variants:
+            with pytest.raises(OverflowError, match=msg):
+                dtimin - variant
+
+    def test_datetimeindex_sub_datetimeindex_overflow(self):
+        # GH#22492, GH#22508
+        dtimax = pd.to_datetime(["2021-12-28 17:19", Timestamp.max]).as_unit("ns")
+        dtimin = pd.to_datetime(["2021-12-28 17:19", Timestamp.min]).as_unit("ns")
+
+        ts_neg = pd.to_datetime(["1950-01-01", "1950-01-01"]).as_unit("ns")
+        ts_pos = pd.to_datetime(["1980-01-01", "1980-01-01"]).as_unit("ns")
+
+        # General tests
+        expected = Timestamp.max._value - ts_pos[1]._value
+        result = dtimax - ts_pos
+        assert result[1]._value == expected
+
+        expected = Timestamp.min._value - ts_neg[1]._value
+        result = dtimin - ts_neg
+        assert result[1]._value == expected
+        msg = "Overflow in int64 addition"
+        with pytest.raises(OverflowError, match=msg):
+            dtimax - ts_neg
+
+        with pytest.raises(OverflowError, match=msg):
+            dtimin - ts_pos
+
+        # Edge cases
+        tmin = pd.to_datetime([Timestamp.min])
+        t1 = tmin + Timedelta.max + Timedelta("1us")
+        with pytest.raises(OverflowError, match=msg):
+            t1 - tmin
+
+        tmax = pd.to_datetime([Timestamp.max])
+        t2 = tmax + Timedelta.min - Timedelta("1us")
+        with pytest.raises(OverflowError, match=msg):
+            tmax - t2
+
+
+class TestTimestampSeriesArithmetic:
+    def test_empty_series_add_sub(self, box_with_array):
+        # GH#13844
+        a = Series(dtype="M8[ns]")
+        b = Series(dtype="m8[ns]")
+        a = box_with_array(a)
+        b = box_with_array(b)
+        tm.assert_equal(a, a + b)
+        tm.assert_equal(a, a - b)
+        tm.assert_equal(a, b + a)
+        msg = "cannot subtract"
+        with pytest.raises(TypeError, match=msg):
+            b - a
+
+    def test_operators_datetimelike(self):
+        # ## timedelta64 ###
+        td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
+        td1.iloc[2] = np.nan
+
+        # ## datetime64 ###
+        dt1 = Series(
+            [
+                Timestamp("20111230"),
+                Timestamp("20120101"),
+                Timestamp("20120103"),
+            ]
+        )
+        dt1.iloc[2] = np.nan
+        dt2 = Series(
+            [
+                Timestamp("20111231"),
+                Timestamp("20120102"),
+                Timestamp("20120104"),
+            ]
+        )
+        dt1 - dt2
+        dt2 - dt1
+
+        # datetime64 with timetimedelta
+        dt1 + td1
+        td1 + dt1
+        dt1 - td1
+
+        # timetimedelta with datetime64
+        td1 + dt1
+        dt1 + td1
+
+    def test_dt64ser_sub_datetime_dtype(self, unit):
+        ts = Timestamp(datetime(1993, 1, 7, 13, 30, 00))
+        dt = datetime(1993, 6, 22, 13, 30)
+        ser = Series([ts], dtype=f"M8[{unit}]")
+        result = ser - dt
+
+        # the expected unit is the max of `unit` and the unit imputed to `dt`,
+        #  which is "us"
+        exp_unit = tm.get_finest_unit(unit, "us")
+        assert result.dtype == f"timedelta64[{exp_unit}]"
+
+    # -------------------------------------------------------------
+    # TODO: This next block of tests came from tests.series.test_operators,
+    # needs to be de-duplicated and parametrized over `box` classes
+
+    @pytest.mark.parametrize(
+        "left, right, op_fail",
+        [
+            [
+                [Timestamp("20111230"), Timestamp("20120101"), NaT],
+                [Timestamp("20111231"), Timestamp("20120102"), Timestamp("20120104")],
+                ["__sub__", "__rsub__"],
+            ],
+            [
+                [Timestamp("20111230"), Timestamp("20120101"), NaT],
+                [timedelta(minutes=5, seconds=3), timedelta(minutes=5, seconds=3), NaT],
+                ["__add__", "__radd__", "__sub__"],
+            ],
+            [
+                [
+                    Timestamp("20111230", tz="US/Eastern"),
+                    Timestamp("20111230", tz="US/Eastern"),
+                    NaT,
+                ],
+                [timedelta(minutes=5, seconds=3), NaT, timedelta(minutes=5, seconds=3)],
+                ["__add__", "__radd__", "__sub__"],
+            ],
+        ],
+    )
+    def test_operators_datetimelike_invalid(
+        self, left, right, op_fail, all_arithmetic_operators
+    ):
+        # these are all TypeError ops
+        op_str = all_arithmetic_operators
+        arg1 = Series(left)
+        arg2 = Series(right)
+        # check that we are getting a TypeError
+        # with 'operate' (from core/ops.py) for the ops that are not
+        # defined
+        op = getattr(arg1, op_str, None)
+        # Previously, _validate_for_numeric_binop in core/indexes/base.py
+        # did this for us.
+        if op_str not in op_fail:
+            with pytest.raises(
+                TypeError, match="operate|[cC]annot|unsupported operand"
+            ):
+                op(arg2)
+        else:
+            # Smoke test
+            op(arg2)
+
+    def test_sub_single_tz(self, unit):
+        # GH#12290
+        s1 = Series([Timestamp("2016-02-10", tz="America/Sao_Paulo")]).dt.as_unit(unit)
+        s2 = Series([Timestamp("2016-02-08", tz="America/Sao_Paulo")]).dt.as_unit(unit)
+        result = s1 - s2
+        expected = Series([Timedelta("2days")]).dt.as_unit(unit)
+        tm.assert_series_equal(result, expected)
+        result = s2 - s1
+        expected = Series([Timedelta("-2days")]).dt.as_unit(unit)
+        tm.assert_series_equal(result, expected)
+
+    def test_dt64tz_series_sub_dtitz(self):
+        # GH#19071 subtracting tzaware DatetimeIndex from tzaware Series
+        # (with same tz) raises, fixed by #19024
+        dti = date_range("1999-09-30", periods=10, tz="US/Pacific")
+        ser = Series(dti)
+        expected = Series(TimedeltaIndex(["0days"] * 10))
+
+        res = dti - ser
+        tm.assert_series_equal(res, expected)
+        res = ser - dti
+        tm.assert_series_equal(res, expected)
+
+    def test_sub_datetime_compat(self, unit):
+        # see GH#14088
+        ser = Series([datetime(2016, 8, 23, 12, tzinfo=pytz.utc), NaT]).dt.as_unit(unit)
+        dt = datetime(2016, 8, 22, 12, tzinfo=pytz.utc)
+        # The datetime object has "us" so we upcast lower units
+        exp_unit = tm.get_finest_unit(unit, "us")
+        exp = Series([Timedelta("1 days"), NaT]).dt.as_unit(exp_unit)
+        result = ser - dt
+        tm.assert_series_equal(result, exp)
+        result2 = ser - Timestamp(dt)
+        tm.assert_series_equal(result2, exp)
+
+    def test_dt64_series_add_mixed_tick_DateOffset(self):
+        # GH#4532
+        # operate with pd.offsets
+        s = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
+
+        result = s + pd.offsets.Milli(5)
+        result2 = pd.offsets.Milli(5) + s
+        expected = Series(
+            [Timestamp("20130101 9:01:00.005"), Timestamp("20130101 9:02:00.005")]
+        )
+        tm.assert_series_equal(result, expected)
+        tm.assert_series_equal(result2, expected)
+
+        result = s + pd.offsets.Minute(5) + pd.offsets.Milli(5)
+        expected = Series(
+            [Timestamp("20130101 9:06:00.005"), Timestamp("20130101 9:07:00.005")]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_datetime64_ops_nat(self, unit):
+        # GH#11349
+        datetime_series = Series([NaT, Timestamp("19900315")]).dt.as_unit(unit)
+        nat_series_dtype_timestamp = Series([NaT, NaT], dtype=f"datetime64[{unit}]")
+        single_nat_dtype_datetime = Series([NaT], dtype=f"datetime64[{unit}]")
+
+        # subtraction
+        tm.assert_series_equal(-NaT + datetime_series, nat_series_dtype_timestamp)
+        msg = "bad operand type for unary -: 'DatetimeArray'"
+        with pytest.raises(TypeError, match=msg):
+            -single_nat_dtype_datetime + datetime_series
+
+        tm.assert_series_equal(
+            -NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp
+        )
+        with pytest.raises(TypeError, match=msg):
+            -single_nat_dtype_datetime + nat_series_dtype_timestamp
+
+        # addition
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp + NaT, nat_series_dtype_timestamp
+        )
+        tm.assert_series_equal(
+            NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp + NaT, nat_series_dtype_timestamp
+        )
+        tm.assert_series_equal(
+            NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp
+        )
+
+    # -------------------------------------------------------------
+    # Timezone-Centric Tests
+
+    def test_operators_datetimelike_with_timezones(self):
+        tz = "US/Eastern"
+        dt1 = Series(date_range("2000-01-01 09:00:00", periods=5, tz=tz), name="foo")
+        dt2 = dt1.copy()
+        dt2.iloc[2] = np.nan
+
+        td1 = Series(pd.timedelta_range("1 days 1 min", periods=5, freq="h"))
+        td2 = td1.copy()
+        td2.iloc[1] = np.nan
+        assert td2._values.freq is None
+
+        result = dt1 + td1[0]
+        exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt2 + td2[0]
+        exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        # odd numpy behavior with scalar timedeltas
+        result = td1[0] + dt1
+        exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = td2[0] + dt2
+        exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt1 - td1[0]
+        exp = (dt1.dt.tz_localize(None) - td1[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+        msg = "(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            td1[0] - dt1
+
+        result = dt2 - td2[0]
+        exp = (dt2.dt.tz_localize(None) - td2[0]).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+        with pytest.raises(TypeError, match=msg):
+            td2[0] - dt2
+
+        result = dt1 + td1
+        exp = (dt1.dt.tz_localize(None) + td1).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt2 + td2
+        exp = (dt2.dt.tz_localize(None) + td2).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt1 - td1
+        exp = (dt1.dt.tz_localize(None) - td1).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+
+        result = dt2 - td2
+        exp = (dt2.dt.tz_localize(None) - td2).dt.tz_localize(tz)
+        tm.assert_series_equal(result, exp)
+        msg = "cannot (add|subtract)"
+        with pytest.raises(TypeError, match=msg):
+            td1 - dt1
+        with pytest.raises(TypeError, match=msg):
+            td2 - dt2
+
+
+class TestDatetimeIndexArithmetic:
+    # -------------------------------------------------------------
+    # Binary operations DatetimeIndex and TimedeltaIndex/array
+
+    def test_dti_add_tdi(self, tz_naive_fixture):
+        # GH#17558
+        tz = tz_naive_fixture
+        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        tdi = pd.timedelta_range("0 days", periods=10)
+        expected = date_range("2017-01-01", periods=10, tz=tz)
+        expected = expected._with_freq(None)
+
+        # add with TimedeltaIndex
+        result = dti + tdi
+        tm.assert_index_equal(result, expected)
+
+        result = tdi + dti
+        tm.assert_index_equal(result, expected)
+
+        # add with timedelta64 array
+        result = dti + tdi.values
+        tm.assert_index_equal(result, expected)
+
+        result = tdi.values + dti
+        tm.assert_index_equal(result, expected)
+
+    def test_dti_iadd_tdi(self, tz_naive_fixture):
+        # GH#17558
+        tz = tz_naive_fixture
+        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        tdi = pd.timedelta_range("0 days", periods=10)
+        expected = date_range("2017-01-01", periods=10, tz=tz)
+        expected = expected._with_freq(None)
+
+        # iadd with TimedeltaIndex
+        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        result += tdi
+        tm.assert_index_equal(result, expected)
+
+        result = pd.timedelta_range("0 days", periods=10)
+        result += dti
+        tm.assert_index_equal(result, expected)
+
+        # iadd with timedelta64 array
+        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        result += tdi.values
+        tm.assert_index_equal(result, expected)
+
+        result = pd.timedelta_range("0 days", periods=10)
+        result += dti
+        tm.assert_index_equal(result, expected)
+
+    def test_dti_sub_tdi(self, tz_naive_fixture):
+        # GH#17558
+        tz = tz_naive_fixture
+        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
+        tdi = pd.timedelta_range("0 days", periods=10)
+        expected = date_range("2017-01-01", periods=10, tz=tz, freq="-1D")
+        expected = expected._with_freq(None)
+
+        # sub with TimedeltaIndex
+        result = dti - tdi
+        tm.assert_index_equal(result, expected)
+
+        msg = "cannot subtract .*TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdi - dti
+
+        # sub with timedelta64 array
+        result = dti - tdi.values
+        tm.assert_index_equal(result, expected)
+
+        msg = "cannot subtract a datelike from a TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdi.values - dti
+
+    def test_dti_isub_tdi(self, tz_naive_fixture, unit):
+        # GH#17558
+        tz = tz_naive_fixture
+        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10).as_unit(unit)
+        tdi = pd.timedelta_range("0 days", periods=10, unit=unit)
+        expected = date_range("2017-01-01", periods=10, tz=tz, freq="-1D", unit=unit)
+        expected = expected._with_freq(None)
+
+        # isub with TimedeltaIndex
+        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10).as_unit(unit)
+        result -= tdi
+        tm.assert_index_equal(result, expected)
+
+        # DTA.__isub__ GH#43904
+        dta = dti._data.copy()
+        dta -= tdi
+        tm.assert_datetime_array_equal(dta, expected._data)
+
+        out = dti._data.copy()
+        np.subtract(out, tdi, out=out)
+        tm.assert_datetime_array_equal(out, expected._data)
+
+        msg = "cannot subtract a datelike from a TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdi -= dti
+
+        # isub with timedelta64 array
+        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10).as_unit(unit)
+        result -= tdi.values
+        tm.assert_index_equal(result, expected)
+
+        with pytest.raises(TypeError, match=msg):
+            tdi.values -= dti
+
+        with pytest.raises(TypeError, match=msg):
+            tdi._values -= dti
+
+    # -------------------------------------------------------------
+    # Binary Operations DatetimeIndex and datetime-like
+    # TODO: A couple other tests belong in this section.  Move them in
+    # A PR where there isn't already a giant diff.
+
+    # -------------------------------------------------------------
+
+    def test_dta_add_sub_index(self, tz_naive_fixture):
+        # Check that DatetimeArray defers to Index classes
+        dti = date_range("20130101", periods=3, tz=tz_naive_fixture)
+        dta = dti.array
+        result = dta - dti
+        expected = dti - dti
+        tm.assert_index_equal(result, expected)
+
+        tdi = result
+        result = dta + tdi
+        expected = dti + tdi
+        tm.assert_index_equal(result, expected)
+
+        result = dta - tdi
+        expected = dti - tdi
+        tm.assert_index_equal(result, expected)
+
+    def test_sub_dti_dti(self, unit):
+        # previously performed setop (deprecated in 0.16.0), now changed to
+        # return subtraction -> TimeDeltaIndex (GH ...)
+
+        dti = date_range("20130101", periods=3, unit=unit)
+        dti_tz = date_range("20130101", periods=3, unit=unit).tz_localize("US/Eastern")
+        expected = TimedeltaIndex([0, 0, 0]).as_unit(unit)
+
+        result = dti - dti
+        tm.assert_index_equal(result, expected)
+
+        result = dti_tz - dti_tz
+        tm.assert_index_equal(result, expected)
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects"
+        with pytest.raises(TypeError, match=msg):
+            dti_tz - dti
+
+        with pytest.raises(TypeError, match=msg):
+            dti - dti_tz
+
+        # isub
+        dti -= dti
+        tm.assert_index_equal(dti, expected)
+
+        # different length raises ValueError
+        dti1 = date_range("20130101", periods=3, unit=unit)
+        dti2 = date_range("20130101", periods=4, unit=unit)
+        msg = "cannot add indices of unequal length"
+        with pytest.raises(ValueError, match=msg):
+            dti1 - dti2
+
+        # NaN propagation
+        dti1 = DatetimeIndex(["2012-01-01", np.nan, "2012-01-03"]).as_unit(unit)
+        dti2 = DatetimeIndex(["2012-01-02", "2012-01-03", np.nan]).as_unit(unit)
+        expected = TimedeltaIndex(["1 days", np.nan, np.nan]).as_unit(unit)
+        result = dti2 - dti1
+        tm.assert_index_equal(result, expected)
+
+    # -------------------------------------------------------------------
+    # TODO: Most of this block is moved from series or frame tests, needs
+    # cleanup, box-parametrization, and de-duplication
+
+    @pytest.mark.parametrize("op", [operator.add, operator.sub])
+    def test_timedelta64_equal_timedelta_supported_ops(self, op, box_with_array):
+        ser = Series(
+            [
+                Timestamp("20130301"),
+                Timestamp("20130228 23:00:00"),
+                Timestamp("20130228 22:00:00"),
+                Timestamp("20130228 21:00:00"),
+            ]
+        )
+        obj = box_with_array(ser)
+
+        intervals = ["D", "h", "m", "s", "us"]
+
+        def timedelta64(*args):
+            # see casting notes in NumPy gh-12927
+            return np.sum(list(starmap(np.timedelta64, zip(args, intervals))))
+
+        for d, h, m, s, us in product(*([range(2)] * 5)):
+            nptd = timedelta64(d, h, m, s, us)
+            pytd = timedelta(days=d, hours=h, minutes=m, seconds=s, microseconds=us)
+            lhs = op(obj, nptd)
+            rhs = op(obj, pytd)
+
+            tm.assert_equal(lhs, rhs)
+
+    def test_ops_nat_mixed_datetime64_timedelta64(self):
+        # GH#11349
+        timedelta_series = Series([NaT, Timedelta("1s")])
+        datetime_series = Series([NaT, Timestamp("19900315")])
+        nat_series_dtype_timedelta = Series([NaT, NaT], dtype="timedelta64[ns]")
+        nat_series_dtype_timestamp = Series([NaT, NaT], dtype="datetime64[ns]")
+        single_nat_dtype_datetime = Series([NaT], dtype="datetime64[ns]")
+        single_nat_dtype_timedelta = Series([NaT], dtype="timedelta64[ns]")
+
+        # subtraction
+        tm.assert_series_equal(
+            datetime_series - single_nat_dtype_datetime, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(
+            datetime_series - single_nat_dtype_timedelta, nat_series_dtype_timestamp
+        )
+        tm.assert_series_equal(
+            -single_nat_dtype_timedelta + datetime_series, nat_series_dtype_timestamp
+        )
+
+        # without a Series wrapping the NaT, it is ambiguous
+        # whether it is a datetime64 or timedelta64
+        # defaults to interpreting it as timedelta64
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp - single_nat_dtype_datetime,
+            nat_series_dtype_timedelta,
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp - single_nat_dtype_timedelta,
+            nat_series_dtype_timestamp,
+        )
+        tm.assert_series_equal(
+            -single_nat_dtype_timedelta + nat_series_dtype_timestamp,
+            nat_series_dtype_timestamp,
+        )
+        msg = "cannot subtract a datelike"
+        with pytest.raises(TypeError, match=msg):
+            timedelta_series - single_nat_dtype_datetime
+
+        # addition
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp + single_nat_dtype_timedelta,
+            nat_series_dtype_timestamp,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + nat_series_dtype_timestamp,
+            nat_series_dtype_timestamp,
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timestamp + single_nat_dtype_timedelta,
+            nat_series_dtype_timestamp,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + nat_series_dtype_timestamp,
+            nat_series_dtype_timestamp,
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + single_nat_dtype_datetime,
+            nat_series_dtype_timestamp,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_datetime + nat_series_dtype_timedelta,
+            nat_series_dtype_timestamp,
+        )
+
+    def test_ufunc_coercions(self, unit):
+        idx = date_range("2011-01-01", periods=3, freq="2D", name="x", unit=unit)
+
+        delta = np.timedelta64(1, "D")
+        exp = date_range("2011-01-02", periods=3, freq="2D", name="x", unit=unit)
+        for result in [idx + delta, np.add(idx, delta)]:
+            assert isinstance(result, DatetimeIndex)
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "2D"
+
+        exp = date_range("2010-12-31", periods=3, freq="2D", name="x", unit=unit)
+
+        for result in [idx - delta, np.subtract(idx, delta)]:
+            assert isinstance(result, DatetimeIndex)
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "2D"
+
+        # When adding/subtracting an ndarray (which has no .freq), the result
+        #  does not infer freq
+        idx = idx._with_freq(None)
+        delta = np.array(
+            [np.timedelta64(1, "D"), np.timedelta64(2, "D"), np.timedelta64(3, "D")]
+        )
+        exp = DatetimeIndex(
+            ["2011-01-02", "2011-01-05", "2011-01-08"], name="x"
+        ).as_unit(unit)
+
+        for result in [idx + delta, np.add(idx, delta)]:
+            tm.assert_index_equal(result, exp)
+            assert result.freq == exp.freq
+
+        exp = DatetimeIndex(
+            ["2010-12-31", "2011-01-01", "2011-01-02"], name="x"
+        ).as_unit(unit)
+        for result in [idx - delta, np.subtract(idx, delta)]:
+            assert isinstance(result, DatetimeIndex)
+            tm.assert_index_equal(result, exp)
+            assert result.freq == exp.freq
+
+    def test_dti_add_series(self, tz_naive_fixture, names):
+        # GH#13905
+        tz = tz_naive_fixture
+        index = DatetimeIndex(
+            ["2016-06-28 05:30", "2016-06-28 05:31"], tz=tz, name=names[0]
+        ).as_unit("ns")
+        ser = Series([Timedelta(seconds=5)] * 2, index=index, name=names[1])
+        expected = Series(index + Timedelta(seconds=5), index=index, name=names[2])
+
+        # passing name arg isn't enough when names[2] is None
+        expected.name = names[2]
+        assert expected.dtype == index.dtype
+        result = ser + index
+        tm.assert_series_equal(result, expected)
+        result2 = index + ser
+        tm.assert_series_equal(result2, expected)
+
+        expected = index + Timedelta(seconds=5)
+        result3 = ser.values + index
+        tm.assert_index_equal(result3, expected)
+        result4 = index + ser.values
+        tm.assert_index_equal(result4, expected)
+
+    @pytest.mark.parametrize("op", [operator.add, roperator.radd, operator.sub])
+    def test_dti_addsub_offset_arraylike(
+        self, tz_naive_fixture, names, op, index_or_series
+    ):
+        # GH#18849, GH#19744
+        other_box = index_or_series
+
+        tz = tz_naive_fixture
+        dti = date_range("2017-01-01", periods=2, tz=tz, name=names[0])
+        other = other_box([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)], name=names[1])
+
+        xbox = get_upcast_box(dti, other)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = op(dti, other)
+
+        expected = DatetimeIndex(
+            [op(dti[n], other[n]) for n in range(len(dti))], name=names[2], freq="infer"
+        )
+        expected = tm.box_expected(expected, xbox).astype(object)
+        tm.assert_equal(res, expected)
+
+    @pytest.mark.parametrize("other_box", [pd.Index, np.array])
+    def test_dti_addsub_object_arraylike(
+        self, tz_naive_fixture, box_with_array, other_box
+    ):
+        tz = tz_naive_fixture
+
+        dti = date_range("2017-01-01", periods=2, tz=tz)
+        dtarr = tm.box_expected(dti, box_with_array)
+        other = other_box([pd.offsets.MonthEnd(), Timedelta(days=4)])
+        xbox = get_upcast_box(dtarr, other)
+
+        expected = DatetimeIndex(["2017-01-31", "2017-01-06"], tz=tz_naive_fixture)
+        expected = tm.box_expected(expected, xbox).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = dtarr + other
+        tm.assert_equal(result, expected)
+
+        expected = DatetimeIndex(["2016-12-31", "2016-12-29"], tz=tz_naive_fixture)
+        expected = tm.box_expected(expected, xbox).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = dtarr - other
+        tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("years", [-1, 0, 1])
+@pytest.mark.parametrize("months", [-2, 0, 2])
+@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+def test_shift_months(years, months, unit):
+    dti = DatetimeIndex(
+        [
+            Timestamp("2000-01-05 00:15:00"),
+            Timestamp("2000-01-31 00:23:00"),
+            Timestamp("2000-01-01"),
+            Timestamp("2000-02-29"),
+            Timestamp("2000-12-31"),
+        ]
+    ).as_unit(unit)
+    shifted = shift_months(dti.asi8, years * 12 + months, reso=dti._data._creso)
+    shifted_dt64 = shifted.view(f"M8[{dti.unit}]")
+    actual = DatetimeIndex(shifted_dt64)
+
+    raw = [x + pd.offsets.DateOffset(years=years, months=months) for x in dti]
+    expected = DatetimeIndex(raw).as_unit(dti.unit)
+    tm.assert_index_equal(actual, expected)
+
+
+def test_dt64arr_addsub_object_dtype_2d():
+    # block-wise DataFrame operations will require operating on 2D
+    #  DatetimeArray/TimedeltaArray, so check that specifically.
+    dti = date_range("1994-02-13", freq="2W", periods=4)
+    dta = dti._data.reshape((4, 1))
+
+    other = np.array([[pd.offsets.Day(n)] for n in range(4)])
+    assert other.shape == dta.shape
+
+    with tm.assert_produces_warning(PerformanceWarning):
+        result = dta + other
+    with tm.assert_produces_warning(PerformanceWarning):
+        expected = (dta[:, 0] + other[:, 0]).reshape(-1, 1)
+
+    tm.assert_numpy_array_equal(result, expected)
+
+    with tm.assert_produces_warning(PerformanceWarning):
+        # Case where we expect to get a TimedeltaArray back
+        result2 = dta - dta.astype(object)
+
+    assert result2.shape == (4, 1)
+    assert all(td._value == 0 for td in result2.ravel())
+
+
+def test_non_nano_dt64_addsub_np_nat_scalars():
+    # GH 52295
+    ser = Series([1233242342344, 232432434324, 332434242344], dtype="datetime64[ms]")
+    result = ser - np.datetime64("nat", "ms")
+    expected = Series([NaT] * 3, dtype="timedelta64[ms]")
+    tm.assert_series_equal(result, expected)
+
+    result = ser + np.timedelta64("nat", "ms")
+    expected = Series([NaT] * 3, dtype="datetime64[ms]")
+    tm.assert_series_equal(result, expected)
+
+
+def test_non_nano_dt64_addsub_np_nat_scalars_unitless():
+    # GH 52295
+    # TODO: Can we default to the ser unit?
+    ser = Series([1233242342344, 232432434324, 332434242344], dtype="datetime64[ms]")
+    result = ser - np.datetime64("nat")
+    expected = Series([NaT] * 3, dtype="timedelta64[ns]")
+    tm.assert_series_equal(result, expected)
+
+    result = ser + np.timedelta64("nat")
+    expected = Series([NaT] * 3, dtype="datetime64[ns]")
+    tm.assert_series_equal(result, expected)
+
+
+def test_non_nano_dt64_addsub_np_nat_scalars_unsupported_unit():
+    # GH 52295
+    ser = Series([12332, 23243, 33243], dtype="datetime64[s]")
+    result = ser - np.datetime64("nat", "D")
+    expected = Series([NaT] * 3, dtype="timedelta64[s]")
+    tm.assert_series_equal(result, expected)
+
+    result = ser + np.timedelta64("nat", "D")
+    expected = Series([NaT] * 3, dtype="datetime64[s]")
+    tm.assert_series_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/test_interval.py

@@ -0,0 +1,306 @@
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_list_like
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    Index,
+    Interval,
+    IntervalIndex,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    BooleanArray,
+    IntervalArray,
+)
+from pandas.tests.arithmetic.common import get_upcast_box
+
+
+@pytest.fixture(
+    params=[
+        (Index([0, 2, 4, 4]), Index([1, 3, 5, 8])),
+        (Index([0.0, 1.0, 2.0, np.nan]), Index([1.0, 2.0, 3.0, np.nan])),
+        (
+            timedelta_range("0 days", periods=3).insert(3, pd.NaT),
+            timedelta_range("1 day", periods=3).insert(3, pd.NaT),
+        ),
+        (
+            date_range("20170101", periods=3).insert(3, pd.NaT),
+            date_range("20170102", periods=3).insert(3, pd.NaT),
+        ),
+        (
+            date_range("20170101", periods=3, tz="US/Eastern").insert(3, pd.NaT),
+            date_range("20170102", periods=3, tz="US/Eastern").insert(3, pd.NaT),
+        ),
+    ],
+    ids=lambda x: str(x[0].dtype),
+)
+def left_right_dtypes(request):
+    """
+    Fixture for building an IntervalArray from various dtypes
+    """
+    return request.param
+
+
+@pytest.fixture
+def interval_array(left_right_dtypes):
+    """
+    Fixture to generate an IntervalArray of various dtypes containing NA if possible
+    """
+    left, right = left_right_dtypes
+    return IntervalArray.from_arrays(left, right)
+
+
+def create_categorical_intervals(left, right, closed="right"):
+    return Categorical(IntervalIndex.from_arrays(left, right, closed))
+
+
+def create_series_intervals(left, right, closed="right"):
+    return Series(IntervalArray.from_arrays(left, right, closed))
+
+
+def create_series_categorical_intervals(left, right, closed="right"):
+    return Series(Categorical(IntervalIndex.from_arrays(left, right, closed)))
+
+
+class TestComparison:
+    @pytest.fixture(params=[operator.eq, operator.ne])
+    def op(self, request):
+        return request.param
+
+    @pytest.fixture(
+        params=[
+            IntervalArray.from_arrays,
+            IntervalIndex.from_arrays,
+            create_categorical_intervals,
+            create_series_intervals,
+            create_series_categorical_intervals,
+        ],
+        ids=[
+            "IntervalArray",
+            "IntervalIndex",
+            "Categorical[Interval]",
+            "Series[Interval]",
+            "Series[Categorical[Interval]]",
+        ],
+    )
+    def interval_constructor(self, request):
+        """
+        Fixture for all pandas native interval constructors.
+        To be used as the LHS of IntervalArray comparisons.
+        """
+        return request.param
+
+    def elementwise_comparison(self, op, interval_array, other):
+        """
+        Helper that performs elementwise comparisons between `array` and `other`
+        """
+        other = other if is_list_like(other) else [other] * len(interval_array)
+        expected = np.array([op(x, y) for x, y in zip(interval_array, other)])
+        if isinstance(other, Series):
+            return Series(expected, index=other.index)
+        return expected
+
+    def test_compare_scalar_interval(self, op, interval_array):
+        # matches first interval
+        other = interval_array[0]
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # matches on a single endpoint but not both
+        other = Interval(interval_array.left[0], interval_array.right[1])
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_compare_scalar_interval_mixed_closed(self, op, closed, other_closed):
+        interval_array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
+        other = Interval(0, 1, closed=other_closed)
+
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_compare_scalar_na(self, op, interval_array, nulls_fixture, box_with_array):
+        box = box_with_array
+        obj = tm.box_expected(interval_array, box)
+        result = op(obj, nulls_fixture)
+
+        if nulls_fixture is pd.NA:
+            # GH#31882
+            exp = np.ones(interval_array.shape, dtype=bool)
+            expected = BooleanArray(exp, exp)
+        else:
+            expected = self.elementwise_comparison(op, interval_array, nulls_fixture)
+
+        if not (box is Index and nulls_fixture is pd.NA):
+            # don't cast expected from BooleanArray to ndarray[object]
+            xbox = get_upcast_box(obj, nulls_fixture, True)
+            expected = tm.box_expected(expected, xbox)
+
+        tm.assert_equal(result, expected)
+
+        rev = op(nulls_fixture, obj)
+        tm.assert_equal(rev, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            0,
+            1.0,
+            True,
+            "foo",
+            Timestamp("2017-01-01"),
+            Timestamp("2017-01-01", tz="US/Eastern"),
+            Timedelta("0 days"),
+            Period("2017-01-01", "D"),
+        ],
+    )
+    def test_compare_scalar_other(self, op, interval_array, other):
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_compare_list_like_interval(self, op, interval_array, interval_constructor):
+        # same endpoints
+        other = interval_constructor(interval_array.left, interval_array.right)
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_equal(result, expected)
+
+        # different endpoints
+        other = interval_constructor(
+            interval_array.left[::-1], interval_array.right[::-1]
+        )
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_equal(result, expected)
+
+        # all nan endpoints
+        other = interval_constructor([np.nan] * 4, [np.nan] * 4)
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_equal(result, expected)
+
+    def test_compare_list_like_interval_mixed_closed(
+        self, op, interval_constructor, closed, other_closed
+    ):
+        interval_array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
+        other = interval_constructor(range(2), range(1, 3), closed=other_closed)
+
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            (
+                Interval(0, 1),
+                Interval(Timedelta("1 day"), Timedelta("2 days")),
+                Interval(4, 5, "both"),
+                Interval(10, 20, "neither"),
+            ),
+            (0, 1.5, Timestamp("20170103"), np.nan),
+            (
+                Timestamp("20170102", tz="US/Eastern"),
+                Timedelta("2 days"),
+                "baz",
+                pd.NaT,
+            ),
+        ],
+    )
+    def test_compare_list_like_object(self, op, interval_array, other):
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_compare_list_like_nan(self, op, interval_array, nulls_fixture):
+        other = [nulls_fixture] * 4
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            np.arange(4, dtype="int64"),
+            np.arange(4, dtype="float64"),
+            date_range("2017-01-01", periods=4),
+            date_range("2017-01-01", periods=4, tz="US/Eastern"),
+            timedelta_range("0 days", periods=4),
+            period_range("2017-01-01", periods=4, freq="D"),
+            Categorical(list("abab")),
+            Categorical(date_range("2017-01-01", periods=4)),
+            pd.array(list("abcd")),
+            pd.array(["foo", 3.14, None, object()], dtype=object),
+        ],
+        ids=lambda x: str(x.dtype),
+    )
+    def test_compare_list_like_other(self, op, interval_array, other):
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("length", [1, 3, 5])
+    @pytest.mark.parametrize("other_constructor", [IntervalArray, list])
+    def test_compare_length_mismatch_errors(self, op, other_constructor, length):
+        interval_array = IntervalArray.from_arrays(range(4), range(1, 5))
+        other = other_constructor([Interval(0, 1)] * length)
+        with pytest.raises(ValueError, match="Lengths must match to compare"):
+            op(interval_array, other)
+
+    @pytest.mark.parametrize(
+        "constructor, expected_type, assert_func",
+        [
+            (IntervalIndex, np.array, tm.assert_numpy_array_equal),
+            (Series, Series, tm.assert_series_equal),
+        ],
+    )
+    def test_index_series_compat(self, op, constructor, expected_type, assert_func):
+        # IntervalIndex/Series that rely on IntervalArray for comparisons
+        breaks = range(4)
+        index = constructor(IntervalIndex.from_breaks(breaks))
+
+        # scalar comparisons
+        other = index[0]
+        result = op(index, other)
+        expected = expected_type(self.elementwise_comparison(op, index, other))
+        assert_func(result, expected)
+
+        other = breaks[0]
+        result = op(index, other)
+        expected = expected_type(self.elementwise_comparison(op, index, other))
+        assert_func(result, expected)
+
+        # list-like comparisons
+        other = IntervalArray.from_breaks(breaks)
+        result = op(index, other)
+        expected = expected_type(self.elementwise_comparison(op, index, other))
+        assert_func(result, expected)
+
+        other = [index[0], breaks[0], "foo"]
+        result = op(index, other)
+        expected = expected_type(self.elementwise_comparison(op, index, other))
+        assert_func(result, expected)
+
+    @pytest.mark.parametrize("scalars", ["a", False, 1, 1.0, None])
+    def test_comparison_operations(self, scalars):
+        # GH #28981
+        expected = Series([False, False])
+        s = Series([Interval(0, 1), Interval(1, 2)], dtype="interval")
+        result = s == scalars
+        tm.assert_series_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/test_numeric.py

@@ -0,0 +1,1567 @@
+# 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,
+    date_range,
+)
+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
+
+
+@pytest.fixture(
+    params=[
+        # TODO: add more  dtypes here
+        Index(np.arange(5, dtype="float64")),
+        Index(np.arange(5, dtype="int64")),
+        Index(np.arange(5, dtype="uint64")),
+        RangeIndex(5),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def numeric_idx(request):
+    """
+    Several types of numeric-dtypes Index objects
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[Index, Series, tm.to_array, np.array, list], ids=lambda x: x.__name__
+)
+def box_1d_array(request):
+    """
+    Fixture to test behavior for Index, Series, tm.to_array, numpy Array and list
+    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]")
+        elif isinstance(
+            three_days,
+            (pd.offsets.Day, pd.offsets.Hour, pd.offsets.Minute, pd.offsets.Second),
+        ):
+            # closest reso is Second
+            expected = expected.astype("m8[s]")
+
+        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"Div by negative 0 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 * 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 = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        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([str(i) for i in range(5)])
+        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 = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            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(
+            date_range("20130101", periods=5),
+            index=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=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 = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="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 = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="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 = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="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 = Series(
+            np.arange(1, 11, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="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_pow_nan_with_zero(self, box_with_array):
+        left = Index([np.nan, np.nan, np.nan])
+        right = Index([0, 0, 0])
+        expected = Index([1.0, 1.0, 1.0])
+
+        left = tm.box_expected(left, box_with_array)
+        right = tm.box_expected(right, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = left**right
+        tm.assert_equal(result, expected)
+
+
+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)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/test_object.py

@@ -0,0 +1,420 @@
+# Arithmetic tests for DataFrame/Series/Index/Array classes that should
+# behave identically.
+# Specifically for object dtype
+import datetime
+from decimal import Decimal
+import operator
+
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    Series,
+    Timestamp,
+    option_context,
+)
+import pandas._testing as tm
+from pandas.core import ops
+
+# ------------------------------------------------------------------
+# Comparisons
+
+
+class TestObjectComparisons:
+    def test_comparison_object_numeric_nas(self, comparison_op):
+        ser = Series(np.random.default_rng(2).standard_normal(10), dtype=object)
+        shifted = ser.shift(2)
+
+        func = comparison_op
+
+        result = func(ser, shifted)
+        expected = func(ser.astype(float), shifted.astype(float))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
+    )
+    def test_object_comparisons(self, infer_string):
+        with option_context("future.infer_string", infer_string):
+            ser = Series(["a", "b", np.nan, "c", "a"])
+
+            result = ser == "a"
+            expected = Series([True, False, False, False, True])
+            tm.assert_series_equal(result, expected)
+
+            result = ser < "a"
+            expected = Series([False, False, False, False, False])
+            tm.assert_series_equal(result, expected)
+
+            result = ser != "a"
+            expected = -(ser == "a")
+            tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_more_na_comparisons(self, dtype):
+        left = Series(["a", np.nan, "c"], dtype=dtype)
+        right = Series(["a", np.nan, "d"], dtype=dtype)
+
+        result = left == right
+        expected = Series([True, False, False])
+        tm.assert_series_equal(result, expected)
+
+        result = left != right
+        expected = Series([False, True, True])
+        tm.assert_series_equal(result, expected)
+
+        result = left == np.nan
+        expected = Series([False, False, False])
+        tm.assert_series_equal(result, expected)
+
+        result = left != np.nan
+        expected = Series([True, True, True])
+        tm.assert_series_equal(result, expected)
+
+
+# ------------------------------------------------------------------
+# Arithmetic
+
+
+class TestArithmetic:
+    def test_add_period_to_array_of_offset(self):
+        # GH#50162
+        per = pd.Period("2012-1-1", freq="D")
+        pi = pd.period_range("2012-1-1", periods=10, freq="D")
+        idx = per - pi
+
+        expected = pd.Index([x + per for x in idx], dtype=object)
+        result = idx + per
+        tm.assert_index_equal(result, expected)
+
+        result = per + idx
+        tm.assert_index_equal(result, expected)
+
+    # TODO: parametrize
+    def test_pow_ops_object(self):
+        # GH#22922
+        # pow is weird with masking & 1, so testing here
+        a = Series([1, np.nan, 1, np.nan], dtype=object)
+        b = Series([1, np.nan, np.nan, 1], dtype=object)
+        result = a**b
+        expected = Series(a.values**b.values, dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        result = b**a
+        expected = Series(b.values**a.values, dtype=object)
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    @pytest.mark.parametrize("other", ["category", "Int64"])
+    def test_add_extension_scalar(self, other, box_with_array, op):
+        # GH#22378
+        # Check that scalars satisfying is_extension_array_dtype(obj)
+        # do not incorrectly try to dispatch to an ExtensionArray operation
+
+        arr = Series(["a", "b", "c"])
+        expected = Series([op(x, other) for x in arr])
+
+        arr = tm.box_expected(arr, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = op(arr, other)
+        tm.assert_equal(result, expected)
+
+    def test_objarr_add_str(self, box_with_array):
+        ser = Series(["x", np.nan, "x"])
+        expected = Series(["xa", np.nan, "xa"])
+
+        ser = tm.box_expected(ser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = ser + "a"
+        tm.assert_equal(result, expected)
+
+    def test_objarr_radd_str(self, box_with_array):
+        ser = Series(["x", np.nan, "x"])
+        expected = Series(["ax", np.nan, "ax"])
+
+        ser = tm.box_expected(ser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = "a" + ser
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            [1, 2, 3],
+            [1.1, 2.2, 3.3],
+            [Timestamp("2011-01-01"), Timestamp("2011-01-02"), pd.NaT],
+            ["x", "y", 1],
+        ],
+    )
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_objarr_radd_str_invalid(self, dtype, data, box_with_array):
+        ser = Series(data, dtype=dtype)
+
+        ser = tm.box_expected(ser, box_with_array)
+        msg = "|".join(
+            [
+                "can only concatenate str",
+                "did not contain a loop with signature matching types",
+                "unsupported operand type",
+                "must be str",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            "foo_" + ser
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd, operator.sub, ops.rsub])
+    def test_objarr_add_invalid(self, op, box_with_array):
+        # invalid ops
+        box = box_with_array
+
+        obj_ser = Series(list("abc"), dtype=object, name="objects")
+
+        obj_ser = tm.box_expected(obj_ser, box)
+        msg = "|".join(
+            [
+                "can only concatenate str",
+                "unsupported operand type",
+                "must be str",
+                "has no kernel",
+            ]
+        )
+        with pytest.raises(Exception, match=msg):
+            op(obj_ser, 1)
+        with pytest.raises(Exception, match=msg):
+            op(obj_ser, np.array(1, dtype=np.int64))
+
+    # TODO: Moved from tests.series.test_operators; needs cleanup
+    def test_operators_na_handling(self):
+        ser = Series(["foo", "bar", "baz", np.nan])
+        result = "prefix_" + ser
+        expected = Series(["prefix_foo", "prefix_bar", "prefix_baz", np.nan])
+        tm.assert_series_equal(result, expected)
+
+        result = ser + "_suffix"
+        expected = Series(["foo_suffix", "bar_suffix", "baz_suffix", np.nan])
+        tm.assert_series_equal(result, expected)
+
+    # TODO: parametrize over box
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_series_with_dtype_radd_timedelta(self, dtype):
+        # note this test is _not_ aimed at timedelta64-dtyped Series
+        # as of 2.0 we retain object dtype when ser.dtype == object
+        ser = Series(
+            [pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.Timedelta("3 days")],
+            dtype=dtype,
+        )
+        expected = Series(
+            [pd.Timedelta("4 days"), pd.Timedelta("5 days"), pd.Timedelta("6 days")],
+            dtype=dtype,
+        )
+
+        result = pd.Timedelta("3 days") + ser
+        tm.assert_series_equal(result, expected)
+
+        result = ser + pd.Timedelta("3 days")
+        tm.assert_series_equal(result, expected)
+
+    # TODO: cleanup & parametrize over box
+    def test_mixed_timezone_series_ops_object(self):
+        # GH#13043
+        ser = Series(
+            [
+                Timestamp("2015-01-01", tz="US/Eastern"),
+                Timestamp("2015-01-01", tz="Asia/Tokyo"),
+            ],
+            name="xxx",
+        )
+        assert ser.dtype == object
+
+        exp = Series(
+            [
+                Timestamp("2015-01-02", tz="US/Eastern"),
+                Timestamp("2015-01-02", tz="Asia/Tokyo"),
+            ],
+            name="xxx",
+        )
+        tm.assert_series_equal(ser + pd.Timedelta("1 days"), exp)
+        tm.assert_series_equal(pd.Timedelta("1 days") + ser, exp)
+
+        # object series & object series
+        ser2 = Series(
+            [
+                Timestamp("2015-01-03", tz="US/Eastern"),
+                Timestamp("2015-01-05", tz="Asia/Tokyo"),
+            ],
+            name="xxx",
+        )
+        assert ser2.dtype == object
+        exp = Series(
+            [pd.Timedelta("2 days"), pd.Timedelta("4 days")], name="xxx", dtype=object
+        )
+        tm.assert_series_equal(ser2 - ser, exp)
+        tm.assert_series_equal(ser - ser2, -exp)
+
+        ser = Series(
+            [pd.Timedelta("01:00:00"), pd.Timedelta("02:00:00")],
+            name="xxx",
+            dtype=object,
+        )
+        assert ser.dtype == object
+
+        exp = Series(
+            [pd.Timedelta("01:30:00"), pd.Timedelta("02:30:00")],
+            name="xxx",
+            dtype=object,
+        )
+        tm.assert_series_equal(ser + pd.Timedelta("00:30:00"), exp)
+        tm.assert_series_equal(pd.Timedelta("00:30:00") + ser, exp)
+
+    # TODO: cleanup & parametrize over box
+    def test_iadd_preserves_name(self):
+        # GH#17067, GH#19723 __iadd__ and __isub__ should preserve index name
+        ser = Series([1, 2, 3])
+        ser.index.name = "foo"
+
+        ser.index += 1
+        assert ser.index.name == "foo"
+
+        ser.index -= 1
+        assert ser.index.name == "foo"
+
+    def test_add_string(self):
+        # from bug report
+        index = pd.Index(["a", "b", "c"])
+        index2 = index + "foo"
+
+        assert "a" not in index2
+        assert "afoo" in index2
+
+    def test_iadd_string(self):
+        index = pd.Index(["a", "b", "c"])
+        # doesn't fail test unless there is a check before `+=`
+        assert "a" in index
+
+        index += "_x"
+        assert "a_x" in index
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="add doesn't work")
+    def test_add(self):
+        index = pd.Index([str(i) for i in range(10)])
+        expected = pd.Index(index.values * 2)
+        tm.assert_index_equal(index + index, expected)
+        tm.assert_index_equal(index + index.tolist(), expected)
+        tm.assert_index_equal(index.tolist() + index, expected)
+
+        # test add and radd
+        index = pd.Index(list("abc"))
+        expected = pd.Index(["a1", "b1", "c1"])
+        tm.assert_index_equal(index + "1", expected)
+        expected = pd.Index(["1a", "1b", "1c"])
+        tm.assert_index_equal("1" + index, expected)
+
+    def test_sub_fail(self, using_infer_string):
+        index = pd.Index([str(i) for i in range(10)])
+
+        if using_infer_string:
+            import pyarrow as pa
+
+            err = pa.lib.ArrowNotImplementedError
+            msg = "has no kernel"
+        else:
+            err = TypeError
+            msg = "unsupported operand type|Cannot broadcast"
+        with pytest.raises(err, match=msg):
+            index - "a"
+        with pytest.raises(err, match=msg):
+            index - index
+        with pytest.raises(err, match=msg):
+            index - index.tolist()
+        with pytest.raises(err, match=msg):
+            index.tolist() - index
+
+    def test_sub_object(self):
+        # GH#19369
+        index = pd.Index([Decimal(1), Decimal(2)])
+        expected = pd.Index([Decimal(0), Decimal(1)])
+
+        result = index - Decimal(1)
+        tm.assert_index_equal(result, expected)
+
+        result = index - pd.Index([Decimal(1), Decimal(1)])
+        tm.assert_index_equal(result, expected)
+
+        msg = "unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            index - "foo"
+
+        with pytest.raises(TypeError, match=msg):
+            index - np.array([2, "foo"], dtype=object)
+
+    def test_rsub_object(self, fixed_now_ts):
+        # GH#19369
+        index = pd.Index([Decimal(1), Decimal(2)])
+        expected = pd.Index([Decimal(1), Decimal(0)])
+
+        result = Decimal(2) - index
+        tm.assert_index_equal(result, expected)
+
+        result = np.array([Decimal(2), Decimal(2)]) - index
+        tm.assert_index_equal(result, expected)
+
+        msg = "unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            "foo" - index
+
+        with pytest.raises(TypeError, match=msg):
+            np.array([True, fixed_now_ts]) - index
+
+
+class MyIndex(pd.Index):
+    # Simple index subclass that tracks ops calls.
+
+    _calls: int
+
+    @classmethod
+    def _simple_new(cls, values, name=None, dtype=None):
+        result = object.__new__(cls)
+        result._data = values
+        result._name = name
+        result._calls = 0
+        result._reset_identity()
+
+        return result
+
+    def __add__(self, other):
+        self._calls += 1
+        return self._simple_new(self._data)
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        [datetime.timedelta(1), datetime.timedelta(2)],
+        [datetime.datetime(2000, 1, 1), datetime.datetime(2000, 1, 2)],
+        [pd.Period("2000"), pd.Period("2001")],
+        ["a", "b"],
+    ],
+    ids=["timedelta", "datetime", "period", "object"],
+)
+def test_index_ops_defer_to_unknown_subclasses(other):
+    # https://github.com/pandas-dev/pandas/issues/31109
+    values = np.array(
+        [datetime.date(2000, 1, 1), datetime.date(2000, 1, 2)], dtype=object
+    )
+    a = MyIndex._simple_new(values)
+    other = pd.Index(other)
+    result = other + a
+    assert isinstance(result, MyIndex)
+    assert a._calls == 1

env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/test_period.py

@@ -0,0 +1,1675 @@
+# Arithmetic tests for DataFrame/Series/Index/Array classes that should
+# behave identically.
+# Specifically for Period dtype
+import operator
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import (
+    IncompatibleFrequency,
+    Period,
+    Timestamp,
+    to_offset,
+)
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    PeriodIndex,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    period_range,
+)
+import pandas._testing as tm
+from pandas.core import ops
+from pandas.core.arrays import TimedeltaArray
+from pandas.tests.arithmetic.common import (
+    assert_invalid_addsub_type,
+    assert_invalid_comparison,
+    get_upcast_box,
+)
+
+_common_mismatch = [
+    pd.offsets.YearBegin(2),
+    pd.offsets.MonthBegin(1),
+    pd.offsets.Minute(),
+]
+
+
+@pytest.fixture(
+    params=[
+        Timedelta(minutes=30).to_pytimedelta(),
+        np.timedelta64(30, "s"),
+        Timedelta(seconds=30),
+    ]
+    + _common_mismatch
+)
+def not_hourly(request):
+    """
+    Several timedelta-like and DateOffset instances that are _not_
+    compatible with Hourly frequencies.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        np.timedelta64(365, "D"),
+        Timedelta(days=365).to_pytimedelta(),
+        Timedelta(days=365),
+    ]
+    + _common_mismatch
+)
+def mismatched_freq(request):
+    """
+    Several timedelta-like and DateOffset instances that are _not_
+    compatible with Monthly or Annual frequencies.
+    """
+    return request.param
+
+
+# ------------------------------------------------------------------
+# Comparisons
+
+
+class TestPeriodArrayLikeComparisons:
+    # Comparison tests for PeriodDtype vectors fully parametrized over
+    #  DataFrame/Series/PeriodIndex/PeriodArray.  Ideally all comparison
+    #  tests will eventually end up here.
+
+    @pytest.mark.parametrize("other", ["2017", Period("2017", freq="D")])
+    def test_eq_scalar(self, other, box_with_array):
+        idx = PeriodIndex(["2017", "2017", "2018"], freq="D")
+        idx = tm.box_expected(idx, box_with_array)
+        xbox = get_upcast_box(idx, other, True)
+
+        expected = np.array([True, True, False])
+        expected = tm.box_expected(expected, xbox)
+
+        result = idx == other
+
+        tm.assert_equal(result, expected)
+
+    def test_compare_zerodim(self, box_with_array):
+        # GH#26689 make sure we unbox zero-dimensional arrays
+
+        pi = period_range("2000", periods=4)
+        other = np.array(pi.to_numpy()[0])
+
+        pi = tm.box_expected(pi, box_with_array)
+        xbox = get_upcast_box(pi, other, True)
+
+        result = pi <= other
+        expected = np.array([True, False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            "foo",
+            Timestamp("2021-01-01"),
+            Timedelta(days=4),
+            9,
+            9.5,
+            2000,  # specifically don't consider 2000 to match Period("2000", "D")
+            False,
+            None,
+        ],
+    )
+    def test_compare_invalid_scalar(self, box_with_array, scalar):
+        # GH#28980
+        # comparison with scalar that cannot be interpreted as a Period
+        pi = period_range("2000", periods=4)
+        parr = tm.box_expected(pi, box_with_array)
+        assert_invalid_comparison(parr, scalar, box_with_array)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            pd.date_range("2000", periods=4).array,
+            pd.timedelta_range("1D", periods=4).array,
+            np.arange(4),
+            np.arange(4).astype(np.float64),
+            list(range(4)),
+            # match Period semantics by not treating integers as Periods
+            [2000, 2001, 2002, 2003],
+            np.arange(2000, 2004),
+            np.arange(2000, 2004).astype(object),
+            pd.Index([2000, 2001, 2002, 2003]),
+        ],
+    )
+    def test_compare_invalid_listlike(self, box_with_array, other):
+        pi = period_range("2000", periods=4)
+        parr = tm.box_expected(pi, box_with_array)
+        assert_invalid_comparison(parr, other, box_with_array)
+
+    @pytest.mark.parametrize("other_box", [list, np.array, lambda x: x.astype(object)])
+    def test_compare_object_dtype(self, box_with_array, other_box):
+        pi = period_range("2000", periods=5)
+        parr = tm.box_expected(pi, box_with_array)
+
+        other = other_box(pi)
+        xbox = get_upcast_box(parr, other, True)
+
+        expected = np.array([True, True, True, True, True])
+        expected = tm.box_expected(expected, xbox)
+
+        result = parr == other
+        tm.assert_equal(result, expected)
+        result = parr <= other
+        tm.assert_equal(result, expected)
+        result = parr >= other
+        tm.assert_equal(result, expected)
+
+        result = parr != other
+        tm.assert_equal(result, ~expected)
+        result = parr < other
+        tm.assert_equal(result, ~expected)
+        result = parr > other
+        tm.assert_equal(result, ~expected)
+
+        other = other_box(pi[::-1])
+
+        expected = np.array([False, False, True, False, False])
+        expected = tm.box_expected(expected, xbox)
+        result = parr == other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([True, True, True, False, False])
+        expected = tm.box_expected(expected, xbox)
+        result = parr <= other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([False, False, True, True, True])
+        expected = tm.box_expected(expected, xbox)
+        result = parr >= other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([True, True, False, True, True])
+        expected = tm.box_expected(expected, xbox)
+        result = parr != other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([True, True, False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        result = parr < other
+        tm.assert_equal(result, expected)
+
+        expected = np.array([False, False, False, True, True])
+        expected = tm.box_expected(expected, xbox)
+        result = parr > other
+        tm.assert_equal(result, expected)
+
+
+class TestPeriodIndexComparisons:
+    # TODO: parameterize over boxes
+
+    def test_pi_cmp_period(self):
+        idx = period_range("2007-01", periods=20, freq="M")
+        per = idx[10]
+
+        result = idx < per
+        exp = idx.values < idx.values[10]
+        tm.assert_numpy_array_equal(result, exp)
+
+        # Tests Period.__richcmp__ against ndarray[object, ndim=2]
+        result = idx.values.reshape(10, 2) < per
+        tm.assert_numpy_array_equal(result, exp.reshape(10, 2))
+
+        # Tests Period.__richcmp__ against ndarray[object, ndim=0]
+        result = idx < np.array(per)
+        tm.assert_numpy_array_equal(result, exp)
+
+    # TODO: moved from test_datetime64; de-duplicate with version below
+    def test_parr_cmp_period_scalar2(self, box_with_array):
+        pi = period_range("2000-01-01", periods=10, freq="D")
+
+        val = pi[3]
+        expected = [x > val for x in pi]
+
+        ser = tm.box_expected(pi, box_with_array)
+        xbox = get_upcast_box(ser, val, True)
+
+        expected = tm.box_expected(expected, xbox)
+        result = ser > val
+        tm.assert_equal(result, expected)
+
+        val = pi[5]
+        result = ser > val
+        expected = [x > val for x in pi]
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_parr_cmp_period_scalar(self, freq, box_with_array):
+        # GH#13200
+        base = PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq=freq)
+        base = tm.box_expected(base, box_with_array)
+        per = Period("2011-02", freq=freq)
+        xbox = get_upcast_box(base, per, True)
+
+        exp = np.array([False, True, False, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base == per, exp)
+        tm.assert_equal(per == base, exp)
+
+        exp = np.array([True, False, True, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base != per, exp)
+        tm.assert_equal(per != base, exp)
+
+        exp = np.array([False, False, True, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base > per, exp)
+        tm.assert_equal(per < base, exp)
+
+        exp = np.array([True, False, False, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base < per, exp)
+        tm.assert_equal(per > base, exp)
+
+        exp = np.array([False, True, True, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base >= per, exp)
+        tm.assert_equal(per <= base, exp)
+
+        exp = np.array([True, True, False, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base <= per, exp)
+        tm.assert_equal(per >= base, exp)
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_parr_cmp_pi(self, freq, box_with_array):
+        # GH#13200
+        base = PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq=freq)
+        base = tm.box_expected(base, box_with_array)
+
+        # TODO: could also box idx?
+        idx = PeriodIndex(["2011-02", "2011-01", "2011-03", "2011-05"], freq=freq)
+
+        xbox = get_upcast_box(base, idx, True)
+
+        exp = np.array([False, False, True, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base == idx, exp)
+
+        exp = np.array([True, True, False, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base != idx, exp)
+
+        exp = np.array([False, True, False, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base > idx, exp)
+
+        exp = np.array([True, False, False, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base < idx, exp)
+
+        exp = np.array([False, True, True, False])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base >= idx, exp)
+
+        exp = np.array([True, False, True, True])
+        exp = tm.box_expected(exp, xbox)
+        tm.assert_equal(base <= idx, exp)
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_parr_cmp_pi_mismatched_freq(self, freq, box_with_array):
+        # GH#13200
+        # different base freq
+        base = PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq=freq)
+        base = tm.box_expected(base, box_with_array)
+
+        msg = rf"Invalid comparison between dtype=period\[{freq}\] and Period"
+        with pytest.raises(TypeError, match=msg):
+            base <= Period("2011", freq="Y")
+
+        with pytest.raises(TypeError, match=msg):
+            Period("2011", freq="Y") >= base
+
+        # TODO: Could parametrize over boxes for idx?
+        idx = PeriodIndex(["2011", "2012", "2013", "2014"], freq="Y")
+        rev_msg = r"Invalid comparison between dtype=period\[Y-DEC\] and PeriodArray"
+        idx_msg = rev_msg if box_with_array in [tm.to_array, pd.array] else msg
+        with pytest.raises(TypeError, match=idx_msg):
+            base <= idx
+
+        # Different frequency
+        msg = rf"Invalid comparison between dtype=period\[{freq}\] and Period"
+        with pytest.raises(TypeError, match=msg):
+            base <= Period("2011", freq="4M")
+
+        with pytest.raises(TypeError, match=msg):
+            Period("2011", freq="4M") >= base
+
+        idx = PeriodIndex(["2011", "2012", "2013", "2014"], freq="4M")
+        rev_msg = r"Invalid comparison between dtype=period\[4M\] and PeriodArray"
+        idx_msg = rev_msg if box_with_array in [tm.to_array, pd.array] else msg
+        with pytest.raises(TypeError, match=idx_msg):
+            base <= idx
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_pi_cmp_nat(self, freq):
+        idx1 = PeriodIndex(["2011-01", "2011-02", "NaT", "2011-05"], freq=freq)
+        per = idx1[1]
+
+        result = idx1 > per
+        exp = np.array([False, False, False, True])
+        tm.assert_numpy_array_equal(result, exp)
+        result = per < idx1
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 == pd.NaT
+        exp = np.array([False, False, False, False])
+        tm.assert_numpy_array_equal(result, exp)
+        result = pd.NaT == idx1
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 != pd.NaT
+        exp = np.array([True, True, True, True])
+        tm.assert_numpy_array_equal(result, exp)
+        result = pd.NaT != idx1
+        tm.assert_numpy_array_equal(result, exp)
+
+        idx2 = PeriodIndex(["2011-02", "2011-01", "2011-04", "NaT"], freq=freq)
+        result = idx1 < idx2
+        exp = np.array([True, False, False, False])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 == idx2
+        exp = np.array([False, False, False, False])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 != idx2
+        exp = np.array([True, True, True, True])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 == idx1
+        exp = np.array([True, True, False, True])
+        tm.assert_numpy_array_equal(result, exp)
+
+        result = idx1 != idx1
+        exp = np.array([False, False, True, False])
+        tm.assert_numpy_array_equal(result, exp)
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_pi_cmp_nat_mismatched_freq_raises(self, freq):
+        idx1 = PeriodIndex(["2011-01", "2011-02", "NaT", "2011-05"], freq=freq)
+
+        diff = PeriodIndex(["2011-02", "2011-01", "2011-04", "NaT"], freq="4M")
+        msg = rf"Invalid comparison between dtype=period\[{freq}\] and PeriodArray"
+        with pytest.raises(TypeError, match=msg):
+            idx1 > diff
+
+        result = idx1 == diff
+        expected = np.array([False, False, False, False], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+    # TODO: De-duplicate with test_pi_cmp_nat
+    @pytest.mark.parametrize("dtype", [object, None])
+    def test_comp_nat(self, dtype):
+        left = PeriodIndex([Period("2011-01-01"), pd.NaT, Period("2011-01-03")])
+        right = PeriodIndex([pd.NaT, pd.NaT, Period("2011-01-03")])
+
+        if dtype is not None:
+            left = left.astype(dtype)
+            right = right.astype(dtype)
+
+        result = left == right
+        expected = np.array([False, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = left != right
+        expected = np.array([True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(left == pd.NaT, expected)
+        tm.assert_numpy_array_equal(pd.NaT == right, expected)
+
+        expected = np.array([True, True, True])
+        tm.assert_numpy_array_equal(left != pd.NaT, expected)
+        tm.assert_numpy_array_equal(pd.NaT != left, expected)
+
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(left < pd.NaT, expected)
+        tm.assert_numpy_array_equal(pd.NaT > left, expected)
+
+
+class TestPeriodSeriesComparisons:
+    def test_cmp_series_period_series_mixed_freq(self):
+        # GH#13200
+        base = Series(
+            [
+                Period("2011", freq="Y"),
+                Period("2011-02", freq="M"),
+                Period("2013", freq="Y"),
+                Period("2011-04", freq="M"),
+            ]
+        )
+
+        ser = Series(
+            [
+                Period("2012", freq="Y"),
+                Period("2011-01", freq="M"),
+                Period("2013", freq="Y"),
+                Period("2011-05", freq="M"),
+            ]
+        )
+
+        exp = Series([False, False, True, False])
+        tm.assert_series_equal(base == ser, exp)
+
+        exp = Series([True, True, False, True])
+        tm.assert_series_equal(base != ser, exp)
+
+        exp = Series([False, True, False, False])
+        tm.assert_series_equal(base > ser, exp)
+
+        exp = Series([True, False, False, True])
+        tm.assert_series_equal(base < ser, exp)
+
+        exp = Series([False, True, True, False])
+        tm.assert_series_equal(base >= ser, exp)
+
+        exp = Series([True, False, True, True])
+        tm.assert_series_equal(base <= ser, exp)
+
+
+class TestPeriodIndexSeriesComparisonConsistency:
+    """Test PeriodIndex and Period Series Ops consistency"""
+
+    # TODO: needs parametrization+de-duplication
+
+    def _check(self, values, func, expected):
+        # Test PeriodIndex and Period Series Ops consistency
+
+        idx = PeriodIndex(values)
+        result = func(idx)
+
+        # check that we don't pass an unwanted type to tm.assert_equal
+        assert isinstance(expected, (pd.Index, np.ndarray))
+        tm.assert_equal(result, expected)
+
+        s = Series(values)
+        result = func(s)
+
+        exp = Series(expected, name=values.name)
+        tm.assert_series_equal(result, exp)
+
+    def test_pi_comp_period(self):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+        per = idx[2]
+
+        f = lambda x: x == per
+        exp = np.array([False, False, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: per == x
+        self._check(idx, f, exp)
+
+        f = lambda x: x != per
+        exp = np.array([True, True, False, True], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: per != x
+        self._check(idx, f, exp)
+
+        f = lambda x: per >= x
+        exp = np.array([True, True, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: x > per
+        exp = np.array([False, False, False, True], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: per >= x
+        exp = np.array([True, True, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+    def test_pi_comp_period_nat(self):
+        idx = PeriodIndex(
+            ["2011-01", "NaT", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+        per = idx[2]
+
+        f = lambda x: x == per
+        exp = np.array([False, False, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: per == x
+        self._check(idx, f, exp)
+
+        f = lambda x: x == pd.NaT
+        exp = np.array([False, False, False, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: pd.NaT == x
+        self._check(idx, f, exp)
+
+        f = lambda x: x != per
+        exp = np.array([True, True, False, True], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: per != x
+        self._check(idx, f, exp)
+
+        f = lambda x: x != pd.NaT
+        exp = np.array([True, True, True, True], dtype=np.bool_)
+        self._check(idx, f, exp)
+        f = lambda x: pd.NaT != x
+        self._check(idx, f, exp)
+
+        f = lambda x: per >= x
+        exp = np.array([True, False, True, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: x < per
+        exp = np.array([True, False, False, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: x > pd.NaT
+        exp = np.array([False, False, False, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+        f = lambda x: pd.NaT >= x
+        exp = np.array([False, False, False, False], dtype=np.bool_)
+        self._check(idx, f, exp)
+
+
+# ------------------------------------------------------------------
+# Arithmetic
+
+
+class TestPeriodFrameArithmetic:
+    def test_ops_frame_period(self):
+        # GH#13043
+        df = pd.DataFrame(
+            {
+                "A": [Period("2015-01", freq="M"), Period("2015-02", freq="M")],
+                "B": [Period("2014-01", freq="M"), Period("2014-02", freq="M")],
+            }
+        )
+        assert df["A"].dtype == "Period[M]"
+        assert df["B"].dtype == "Period[M]"
+
+        p = Period("2015-03", freq="M")
+        off = p.freq
+        # dtype will be object because of original dtype
+        exp = pd.DataFrame(
+            {
+                "A": np.array([2 * off, 1 * off], dtype=object),
+                "B": np.array([14 * off, 13 * off], dtype=object),
+            }
+        )
+        tm.assert_frame_equal(p - df, exp)
+        tm.assert_frame_equal(df - p, -1 * exp)
+
+        df2 = pd.DataFrame(
+            {
+                "A": [Period("2015-05", freq="M"), Period("2015-06", freq="M")],
+                "B": [Period("2015-05", freq="M"), Period("2015-06", freq="M")],
+            }
+        )
+        assert df2["A"].dtype == "Period[M]"
+        assert df2["B"].dtype == "Period[M]"
+
+        exp = pd.DataFrame(
+            {
+                "A": np.array([4 * off, 4 * off], dtype=object),
+                "B": np.array([16 * off, 16 * off], dtype=object),
+            }
+        )
+        tm.assert_frame_equal(df2 - df, exp)
+        tm.assert_frame_equal(df - df2, -1 * exp)
+
+
+class TestPeriodIndexArithmetic:
+    # ---------------------------------------------------------------
+    # __add__/__sub__ with PeriodIndex
+    # PeriodIndex + other is defined for integers and timedelta-like others
+    # PeriodIndex - other is defined for integers, timedelta-like others,
+    #   and PeriodIndex (with matching freq)
+
+    def test_parr_add_iadd_parr_raises(self, box_with_array):
+        rng = period_range("1/1/2000", freq="D", periods=5)
+        other = period_range("1/6/2000", freq="D", periods=5)
+        # TODO: parametrize over boxes for other?
+
+        rng = tm.box_expected(rng, box_with_array)
+        # An earlier implementation of PeriodIndex addition performed
+        # a set operation (union).  This has since been changed to
+        # raise a TypeError. See GH#14164 and GH#13077 for historical
+        # reference.
+        msg = r"unsupported operand type\(s\) for \+: .* and .*"
+        with pytest.raises(TypeError, match=msg):
+            rng + other
+
+        with pytest.raises(TypeError, match=msg):
+            rng += other
+
+    def test_pi_sub_isub_pi(self):
+        # GH#20049
+        # For historical reference see GH#14164, GH#13077.
+        # PeriodIndex subtraction originally performed set difference,
+        # then changed to raise TypeError before being implemented in GH#20049
+        rng = period_range("1/1/2000", freq="D", periods=5)
+        other = period_range("1/6/2000", freq="D", periods=5)
+
+        off = rng.freq
+        expected = pd.Index([-5 * off] * 5)
+        result = rng - other
+        tm.assert_index_equal(result, expected)
+
+        rng -= other
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_sub_pi_with_nat(self):
+        rng = period_range("1/1/2000", freq="D", periods=5)
+        other = rng[1:].insert(0, pd.NaT)
+        assert other[1:].equals(rng[1:])
+
+        result = rng - other
+        off = rng.freq
+        expected = pd.Index([pd.NaT, 0 * off, 0 * off, 0 * off, 0 * off])
+        tm.assert_index_equal(result, expected)
+
+    def test_parr_sub_pi_mismatched_freq(self, box_with_array, box_with_array2):
+        rng = period_range("1/1/2000", freq="D", periods=5)
+        other = period_range("1/6/2000", freq="h", periods=5)
+
+        rng = tm.box_expected(rng, box_with_array)
+        other = tm.box_expected(other, box_with_array2)
+        msg = r"Input has different freq=[hD] from PeriodArray\(freq=[Dh]\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng - other
+
+    @pytest.mark.parametrize("n", [1, 2, 3, 4])
+    def test_sub_n_gt_1_ticks(self, tick_classes, n):
+        # GH 23878
+        p1_d = "19910905"
+        p2_d = "19920406"
+        p1 = PeriodIndex([p1_d], freq=tick_classes(n))
+        p2 = PeriodIndex([p2_d], freq=tick_classes(n))
+
+        expected = PeriodIndex([p2_d], freq=p2.freq.base) - PeriodIndex(
+            [p1_d], freq=p1.freq.base
+        )
+
+        tm.assert_index_equal((p2 - p1), expected)
+
+    @pytest.mark.parametrize("n", [1, 2, 3, 4])
+    @pytest.mark.parametrize(
+        "offset, kwd_name",
+        [
+            (pd.offsets.YearEnd, "month"),
+            (pd.offsets.QuarterEnd, "startingMonth"),
+            (pd.offsets.MonthEnd, None),
+            (pd.offsets.Week, "weekday"),
+        ],
+    )
+    def test_sub_n_gt_1_offsets(self, offset, kwd_name, n):
+        # GH 23878
+        kwds = {kwd_name: 3} if kwd_name is not None else {}
+        p1_d = "19910905"
+        p2_d = "19920406"
+        freq = offset(n, normalize=False, **kwds)
+        p1 = PeriodIndex([p1_d], freq=freq)
+        p2 = PeriodIndex([p2_d], freq=freq)
+
+        result = p2 - p1
+        expected = PeriodIndex([p2_d], freq=freq.base) - PeriodIndex(
+            [p1_d], freq=freq.base
+        )
+
+        tm.assert_index_equal(result, expected)
+
+    # -------------------------------------------------------------
+    # Invalid Operations
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            # datetime scalars
+            Timestamp("2016-01-01"),
+            Timestamp("2016-01-01").to_pydatetime(),
+            Timestamp("2016-01-01").to_datetime64(),
+            # datetime-like arrays
+            pd.date_range("2016-01-01", periods=3, freq="h"),
+            pd.date_range("2016-01-01", periods=3, tz="Europe/Brussels"),
+            pd.date_range("2016-01-01", periods=3, freq="s")._data,
+            pd.date_range("2016-01-01", periods=3, tz="Asia/Tokyo")._data,
+            # Miscellaneous invalid types
+            3.14,
+            np.array([2.0, 3.0, 4.0]),
+        ],
+    )
+    def test_parr_add_sub_invalid(self, other, box_with_array):
+        # GH#23215
+        rng = period_range("1/1/2000", freq="D", periods=3)
+        rng = tm.box_expected(rng, box_with_array)
+
+        msg = "|".join(
+            [
+                r"(:?cannot add PeriodArray and .*)",
+                r"(:?cannot subtract .* from (:?a\s)?.*)",
+                r"(:?unsupported operand type\(s\) for \+: .* and .*)",
+                r"unsupported operand type\(s\) for [+-]: .* and .*",
+            ]
+        )
+        assert_invalid_addsub_type(rng, other, msg)
+        with pytest.raises(TypeError, match=msg):
+            rng + other
+        with pytest.raises(TypeError, match=msg):
+            other + rng
+        with pytest.raises(TypeError, match=msg):
+            rng - other
+        with pytest.raises(TypeError, match=msg):
+            other - rng
+
+    # -----------------------------------------------------------------
+    # __add__/__sub__ with ndarray[datetime64] and ndarray[timedelta64]
+
+    def test_pi_add_sub_td64_array_non_tick_raises(self):
+        rng = period_range("1/1/2000", freq="Q", periods=3)
+        tdi = TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"])
+        tdarr = tdi.values
+
+        msg = r"Cannot add or subtract timedelta64\[ns\] dtype from period\[Q-DEC\]"
+        with pytest.raises(TypeError, match=msg):
+            rng + tdarr
+        with pytest.raises(TypeError, match=msg):
+            tdarr + rng
+
+        with pytest.raises(TypeError, match=msg):
+            rng - tdarr
+        msg = r"cannot subtract PeriodArray from TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdarr - rng
+
+    def test_pi_add_sub_td64_array_tick(self):
+        # PeriodIndex + Timedelta-like is allowed only with
+        #   tick-like frequencies
+        rng = period_range("1/1/2000", freq="90D", periods=3)
+        tdi = TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"])
+        tdarr = tdi.values
+
+        expected = period_range("12/31/1999", freq="90D", periods=3)
+        result = rng + tdi
+        tm.assert_index_equal(result, expected)
+        result = rng + tdarr
+        tm.assert_index_equal(result, expected)
+        result = tdi + rng
+        tm.assert_index_equal(result, expected)
+        result = tdarr + rng
+        tm.assert_index_equal(result, expected)
+
+        expected = period_range("1/2/2000", freq="90D", periods=3)
+
+        result = rng - tdi
+        tm.assert_index_equal(result, expected)
+        result = rng - tdarr
+        tm.assert_index_equal(result, expected)
+
+        msg = r"cannot subtract .* from .*"
+        with pytest.raises(TypeError, match=msg):
+            tdarr - rng
+
+        with pytest.raises(TypeError, match=msg):
+            tdi - rng
+
+    @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "h"])
+    @pytest.mark.parametrize("tdi_freq", [None, "h"])
+    def test_parr_sub_td64array(self, box_with_array, tdi_freq, pi_freq):
+        box = box_with_array
+        xbox = box if box not in [pd.array, tm.to_array] else pd.Index
+
+        tdi = TimedeltaIndex(["1 hours", "2 hours"], freq=tdi_freq)
+        dti = Timestamp("2018-03-07 17:16:40") + tdi
+        pi = dti.to_period(pi_freq)
+
+        # TODO: parametrize over box for pi?
+        td64obj = tm.box_expected(tdi, box)
+
+        if pi_freq == "h":
+            result = pi - td64obj
+            expected = (pi.to_timestamp("s") - tdi).to_period(pi_freq)
+            expected = tm.box_expected(expected, xbox)
+            tm.assert_equal(result, expected)
+
+            # Subtract from scalar
+            result = pi[0] - td64obj
+            expected = (pi[0].to_timestamp("s") - tdi).to_period(pi_freq)
+            expected = tm.box_expected(expected, box)
+            tm.assert_equal(result, expected)
+
+        elif pi_freq == "D":
+            # Tick, but non-compatible
+            msg = (
+                "Cannot add/subtract timedelta-like from PeriodArray that is "
+                "not an integer multiple of the PeriodArray's freq."
+            )
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                pi - td64obj
+
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                pi[0] - td64obj
+
+        else:
+            # With non-Tick freq, we could not add timedelta64 array regardless
+            #  of what its resolution is
+            msg = "Cannot add or subtract timedelta64"
+            with pytest.raises(TypeError, match=msg):
+                pi - td64obj
+            with pytest.raises(TypeError, match=msg):
+                pi[0] - td64obj
+
+    # -----------------------------------------------------------------
+    # operations with array/Index of DateOffset objects
+
+    @pytest.mark.parametrize("box", [np.array, pd.Index])
+    def test_pi_add_offset_array(self, box):
+        # GH#18849
+        pi = PeriodIndex([Period("2015Q1"), Period("2016Q2")])
+        offs = box(
+            [
+                pd.offsets.QuarterEnd(n=1, startingMonth=12),
+                pd.offsets.QuarterEnd(n=-2, startingMonth=12),
+            ]
+        )
+        expected = PeriodIndex([Period("2015Q2"), Period("2015Q4")]).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = pi + offs
+        tm.assert_index_equal(res, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res2 = offs + pi
+        tm.assert_index_equal(res2, expected)
+
+        unanchored = np.array([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)])
+        # addition/subtraction ops with incompatible offsets should issue
+        # a PerformanceWarning and _then_ raise a TypeError.
+        msg = r"Input cannot be converted to Period\(freq=Q-DEC\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                pi + unanchored
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                unanchored + pi
+
+    @pytest.mark.parametrize("box", [np.array, pd.Index])
+    def test_pi_sub_offset_array(self, box):
+        # GH#18824
+        pi = PeriodIndex([Period("2015Q1"), Period("2016Q2")])
+        other = box(
+            [
+                pd.offsets.QuarterEnd(n=1, startingMonth=12),
+                pd.offsets.QuarterEnd(n=-2, startingMonth=12),
+            ]
+        )
+
+        expected = PeriodIndex([pi[n] - other[n] for n in range(len(pi))])
+        expected = expected.astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = pi - other
+        tm.assert_index_equal(res, expected)
+
+        anchored = box([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)])
+
+        # addition/subtraction ops with anchored offsets should issue
+        # a PerformanceWarning and _then_ raise a TypeError.
+        msg = r"Input has different freq=-1M from Period\(freq=Q-DEC\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                pi - anchored
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                anchored - pi
+
+    def test_pi_add_iadd_int(self, one):
+        # Variants of `one` for #19012
+        rng = period_range("2000-01-01 09:00", freq="h", periods=10)
+        result = rng + one
+        expected = period_range("2000-01-01 10:00", freq="h", periods=10)
+        tm.assert_index_equal(result, expected)
+        rng += one
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_sub_isub_int(self, one):
+        """
+        PeriodIndex.__sub__ and __isub__ with several representations of
+        the integer 1, e.g. int, np.int64, np.uint8, ...
+        """
+        rng = period_range("2000-01-01 09:00", freq="h", periods=10)
+        result = rng - one
+        expected = period_range("2000-01-01 08:00", freq="h", periods=10)
+        tm.assert_index_equal(result, expected)
+        rng -= one
+        tm.assert_index_equal(rng, expected)
+
+    @pytest.mark.parametrize("five", [5, np.array(5, dtype=np.int64)])
+    def test_pi_sub_intlike(self, five):
+        rng = period_range("2007-01", periods=50)
+
+        result = rng - five
+        exp = rng + (-five)
+        tm.assert_index_equal(result, exp)
+
+    def test_pi_add_sub_int_array_freqn_gt1(self):
+        # GH#47209 test adding array of ints when freq.n > 1 matches
+        #  scalar behavior
+        pi = period_range("2016-01-01", periods=10, freq="2D")
+        arr = np.arange(10)
+        result = pi + arr
+        expected = pd.Index([x + y for x, y in zip(pi, arr)])
+        tm.assert_index_equal(result, expected)
+
+        result = pi - arr
+        expected = pd.Index([x - y for x, y in zip(pi, arr)])
+        tm.assert_index_equal(result, expected)
+
+    def test_pi_sub_isub_offset(self):
+        # offset
+        # DateOffset
+        rng = period_range("2014", "2024", freq="Y")
+        result = rng - pd.offsets.YearEnd(5)
+        expected = period_range("2009", "2019", freq="Y")
+        tm.assert_index_equal(result, expected)
+        rng -= pd.offsets.YearEnd(5)
+        tm.assert_index_equal(rng, expected)
+
+        rng = period_range("2014-01", "2016-12", freq="M")
+        result = rng - pd.offsets.MonthEnd(5)
+        expected = period_range("2013-08", "2016-07", freq="M")
+        tm.assert_index_equal(result, expected)
+
+        rng -= pd.offsets.MonthEnd(5)
+        tm.assert_index_equal(rng, expected)
+
+    @pytest.mark.parametrize("transpose", [True, False])
+    def test_pi_add_offset_n_gt1(self, box_with_array, transpose):
+        # GH#23215
+        # add offset to PeriodIndex with freq.n > 1
+
+        per = Period("2016-01", freq="2M")
+        pi = PeriodIndex([per])
+
+        expected = PeriodIndex(["2016-03"], freq="2M")
+
+        pi = tm.box_expected(pi, box_with_array, transpose=transpose)
+        expected = tm.box_expected(expected, box_with_array, transpose=transpose)
+
+        result = pi + per.freq
+        tm.assert_equal(result, expected)
+
+        result = per.freq + pi
+        tm.assert_equal(result, expected)
+
+    def test_pi_add_offset_n_gt1_not_divisible(self, box_with_array):
+        # GH#23215
+        # PeriodIndex with freq.n > 1 add offset with offset.n % freq.n != 0
+        pi = PeriodIndex(["2016-01"], freq="2M")
+        expected = PeriodIndex(["2016-04"], freq="2M")
+
+        pi = tm.box_expected(pi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = pi + to_offset("3ME")
+        tm.assert_equal(result, expected)
+
+        result = to_offset("3ME") + pi
+        tm.assert_equal(result, expected)
+
+    # ---------------------------------------------------------------
+    # __add__/__sub__ with integer arrays
+
+    @pytest.mark.parametrize("int_holder", [np.array, pd.Index])
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    def test_pi_add_intarray(self, int_holder, op):
+        # GH#19959
+        pi = PeriodIndex([Period("2015Q1"), Period("NaT")])
+        other = int_holder([4, -1])
+
+        result = op(pi, other)
+        expected = PeriodIndex([Period("2016Q1"), Period("NaT")])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("int_holder", [np.array, pd.Index])
+    def test_pi_sub_intarray(self, int_holder):
+        # GH#19959
+        pi = PeriodIndex([Period("2015Q1"), Period("NaT")])
+        other = int_holder([4, -1])
+
+        result = pi - other
+        expected = PeriodIndex([Period("2014Q1"), Period("NaT")])
+        tm.assert_index_equal(result, expected)
+
+        msg = r"bad operand type for unary -: 'PeriodArray'"
+        with pytest.raises(TypeError, match=msg):
+            other - pi
+
+    # ---------------------------------------------------------------
+    # Timedelta-like (timedelta, timedelta64, Timedelta, Tick)
+    # TODO: Some of these are misnomers because of non-Tick DateOffsets
+
+    def test_parr_add_timedeltalike_minute_gt1(self, three_days, box_with_array):
+        # GH#23031 adding a time-delta-like offset to a PeriodArray that has
+        # minute frequency with n != 1.  A more general case is tested below
+        # in test_pi_add_timedeltalike_tick_gt1, but here we write out the
+        # expected result more explicitly.
+        other = three_days
+        rng = period_range("2014-05-01", periods=3, freq="2D")
+        rng = tm.box_expected(rng, box_with_array)
+
+        expected = PeriodIndex(["2014-05-04", "2014-05-06", "2014-05-08"], freq="2D")
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = rng + other
+        tm.assert_equal(result, expected)
+
+        result = other + rng
+        tm.assert_equal(result, expected)
+
+        # subtraction
+        expected = PeriodIndex(["2014-04-28", "2014-04-30", "2014-05-02"], freq="2D")
+        expected = tm.box_expected(expected, box_with_array)
+        result = rng - other
+        tm.assert_equal(result, expected)
+
+        msg = "|".join(
+            [
+                r"bad operand type for unary -: 'PeriodArray'",
+                r"cannot subtract PeriodArray from timedelta64\[[hD]\]",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            other - rng
+
+    @pytest.mark.parametrize("freqstr", ["5ns", "5us", "5ms", "5s", "5min", "5h", "5d"])
+    def test_parr_add_timedeltalike_tick_gt1(self, three_days, freqstr, box_with_array):
+        # GH#23031 adding a time-delta-like offset to a PeriodArray that has
+        # tick-like frequency with n != 1
+        other = three_days
+        rng = period_range("2014-05-01", periods=6, freq=freqstr)
+        first = rng[0]
+        rng = tm.box_expected(rng, box_with_array)
+
+        expected = period_range(first + other, periods=6, freq=freqstr)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = rng + other
+        tm.assert_equal(result, expected)
+
+        result = other + rng
+        tm.assert_equal(result, expected)
+
+        # subtraction
+        expected = period_range(first - other, periods=6, freq=freqstr)
+        expected = tm.box_expected(expected, box_with_array)
+        result = rng - other
+        tm.assert_equal(result, expected)
+        msg = "|".join(
+            [
+                r"bad operand type for unary -: 'PeriodArray'",
+                r"cannot subtract PeriodArray from timedelta64\[[hD]\]",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            other - rng
+
+    def test_pi_add_iadd_timedeltalike_daily(self, three_days):
+        # Tick
+        other = three_days
+        rng = period_range("2014-05-01", "2014-05-15", freq="D")
+        expected = period_range("2014-05-04", "2014-05-18", freq="D")
+
+        result = rng + other
+        tm.assert_index_equal(result, expected)
+
+        rng += other
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_sub_isub_timedeltalike_daily(self, three_days):
+        # Tick-like 3 Days
+        other = three_days
+        rng = period_range("2014-05-01", "2014-05-15", freq="D")
+        expected = period_range("2014-04-28", "2014-05-12", freq="D")
+
+        result = rng - other
+        tm.assert_index_equal(result, expected)
+
+        rng -= other
+        tm.assert_index_equal(rng, expected)
+
+    def test_parr_add_sub_timedeltalike_freq_mismatch_daily(
+        self, not_daily, box_with_array
+    ):
+        other = not_daily
+        rng = period_range("2014-05-01", "2014-05-15", freq="D")
+        rng = tm.box_expected(rng, box_with_array)
+
+        msg = "|".join(
+            [
+                # non-timedelta-like DateOffset
+                "Input has different freq(=.+)? from Period.*?\\(freq=D\\)",
+                # timedelta/td64/Timedelta but not a multiple of 24H
+                "Cannot add/subtract timedelta-like from PeriodArray that is "
+                "not an integer multiple of the PeriodArray's freq.",
+            ]
+        )
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng + other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng += other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng - other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng -= other
+
+    def test_pi_add_iadd_timedeltalike_hourly(self, two_hours):
+        other = two_hours
+        rng = period_range("2014-01-01 10:00", "2014-01-05 10:00", freq="h")
+        expected = period_range("2014-01-01 12:00", "2014-01-05 12:00", freq="h")
+
+        result = rng + other
+        tm.assert_index_equal(result, expected)
+
+        rng += other
+        tm.assert_index_equal(rng, expected)
+
+    def test_parr_add_timedeltalike_mismatched_freq_hourly(
+        self, not_hourly, box_with_array
+    ):
+        other = not_hourly
+        rng = period_range("2014-01-01 10:00", "2014-01-05 10:00", freq="h")
+        rng = tm.box_expected(rng, box_with_array)
+        msg = "|".join(
+            [
+                # non-timedelta-like DateOffset
+                "Input has different freq(=.+)? from Period.*?\\(freq=h\\)",
+                # timedelta/td64/Timedelta but not a multiple of 24H
+                "Cannot add/subtract timedelta-like from PeriodArray that is "
+                "not an integer multiple of the PeriodArray's freq.",
+            ]
+        )
+
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng + other
+
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng += other
+
+    def test_pi_sub_isub_timedeltalike_hourly(self, two_hours):
+        other = two_hours
+        rng = period_range("2014-01-01 10:00", "2014-01-05 10:00", freq="h")
+        expected = period_range("2014-01-01 08:00", "2014-01-05 08:00", freq="h")
+
+        result = rng - other
+        tm.assert_index_equal(result, expected)
+
+        rng -= other
+        tm.assert_index_equal(rng, expected)
+
+    def test_add_iadd_timedeltalike_annual(self):
+        # offset
+        # DateOffset
+        rng = period_range("2014", "2024", freq="Y")
+        result = rng + pd.offsets.YearEnd(5)
+        expected = period_range("2019", "2029", freq="Y")
+        tm.assert_index_equal(result, expected)
+        rng += pd.offsets.YearEnd(5)
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_add_sub_timedeltalike_freq_mismatch_annual(self, mismatched_freq):
+        other = mismatched_freq
+        rng = period_range("2014", "2024", freq="Y")
+        msg = "Input has different freq(=.+)? from Period.*?\\(freq=Y-DEC\\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng + other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng += other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng - other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng -= other
+
+    def test_pi_add_iadd_timedeltalike_M(self):
+        rng = period_range("2014-01", "2016-12", freq="M")
+        expected = period_range("2014-06", "2017-05", freq="M")
+
+        result = rng + pd.offsets.MonthEnd(5)
+        tm.assert_index_equal(result, expected)
+
+        rng += pd.offsets.MonthEnd(5)
+        tm.assert_index_equal(rng, expected)
+
+    def test_pi_add_sub_timedeltalike_freq_mismatch_monthly(self, mismatched_freq):
+        other = mismatched_freq
+        rng = period_range("2014-01", "2016-12", freq="M")
+        msg = "Input has different freq(=.+)? from Period.*?\\(freq=M\\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng + other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng += other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng - other
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            rng -= other
+
+    @pytest.mark.parametrize("transpose", [True, False])
+    def test_parr_add_sub_td64_nat(self, box_with_array, transpose):
+        # GH#23320 special handling for timedelta64("NaT")
+        pi = period_range("1994-04-01", periods=9, freq="19D")
+        other = np.timedelta64("NaT")
+        expected = PeriodIndex(["NaT"] * 9, freq="19D")
+
+        obj = tm.box_expected(pi, box_with_array, transpose=transpose)
+        expected = tm.box_expected(expected, box_with_array, transpose=transpose)
+
+        result = obj + other
+        tm.assert_equal(result, expected)
+        result = other + obj
+        tm.assert_equal(result, expected)
+        result = obj - other
+        tm.assert_equal(result, expected)
+        msg = r"cannot subtract .* from .*"
+        with pytest.raises(TypeError, match=msg):
+            other - obj
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            np.array(["NaT"] * 9, dtype="m8[ns]"),
+            TimedeltaArray._from_sequence(["NaT"] * 9, dtype="m8[ns]"),
+        ],
+    )
+    def test_parr_add_sub_tdt64_nat_array(self, box_with_array, other):
+        pi = period_range("1994-04-01", periods=9, freq="19D")
+        expected = PeriodIndex(["NaT"] * 9, freq="19D")
+
+        obj = tm.box_expected(pi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = obj + other
+        tm.assert_equal(result, expected)
+        result = other + obj
+        tm.assert_equal(result, expected)
+        result = obj - other
+        tm.assert_equal(result, expected)
+        msg = r"cannot subtract .* from .*"
+        with pytest.raises(TypeError, match=msg):
+            other - obj
+
+        # some but not *all* NaT
+        other = other.copy()
+        other[0] = np.timedelta64(0, "ns")
+        expected = PeriodIndex([pi[0]] + ["NaT"] * 8, freq="19D")
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = obj + other
+        tm.assert_equal(result, expected)
+        result = other + obj
+        tm.assert_equal(result, expected)
+        result = obj - other
+        tm.assert_equal(result, expected)
+        with pytest.raises(TypeError, match=msg):
+            other - obj
+
+    # ---------------------------------------------------------------
+    # Unsorted
+
+    def test_parr_add_sub_index(self):
+        # Check that PeriodArray defers to Index on arithmetic ops
+        pi = period_range("2000-12-31", periods=3)
+        parr = pi.array
+
+        result = parr - pi
+        expected = pi - pi
+        tm.assert_index_equal(result, expected)
+
+    def test_parr_add_sub_object_array(self):
+        pi = period_range("2000-12-31", periods=3, freq="D")
+        parr = pi.array
+
+        other = np.array([Timedelta(days=1), pd.offsets.Day(2), 3])
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = parr + other
+
+        expected = PeriodIndex(
+            ["2001-01-01", "2001-01-03", "2001-01-05"], freq="D"
+        )._data.astype(object)
+        tm.assert_equal(result, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = parr - other
+
+        expected = PeriodIndex(["2000-12-30"] * 3, freq="D")._data.astype(object)
+        tm.assert_equal(result, expected)
+
+    def test_period_add_timestamp_raises(self, box_with_array):
+        # GH#17983
+        ts = Timestamp("2017")
+        per = Period("2017", freq="M")
+
+        arr = pd.Index([per], dtype="Period[M]")
+        arr = tm.box_expected(arr, box_with_array)
+
+        msg = "cannot add PeriodArray and Timestamp"
+        with pytest.raises(TypeError, match=msg):
+            arr + ts
+        with pytest.raises(TypeError, match=msg):
+            ts + arr
+        msg = "cannot add PeriodArray and DatetimeArray"
+        with pytest.raises(TypeError, match=msg):
+            arr + Series([ts])
+        with pytest.raises(TypeError, match=msg):
+            Series([ts]) + arr
+        with pytest.raises(TypeError, match=msg):
+            arr + pd.Index([ts])
+        with pytest.raises(TypeError, match=msg):
+            pd.Index([ts]) + arr
+
+        if box_with_array is pd.DataFrame:
+            msg = "cannot add PeriodArray and DatetimeArray"
+        else:
+            msg = r"unsupported operand type\(s\) for \+: 'Period' and 'DatetimeArray"
+        with pytest.raises(TypeError, match=msg):
+            arr + pd.DataFrame([ts])
+        if box_with_array is pd.DataFrame:
+            msg = "cannot add PeriodArray and DatetimeArray"
+        else:
+            msg = r"unsupported operand type\(s\) for \+: 'DatetimeArray' and 'Period'"
+        with pytest.raises(TypeError, match=msg):
+            pd.DataFrame([ts]) + arr
+
+
+class TestPeriodSeriesArithmetic:
+    def test_parr_add_timedeltalike_scalar(self, three_days, box_with_array):
+        # GH#13043
+        ser = Series(
+            [Period("2015-01-01", freq="D"), Period("2015-01-02", freq="D")],
+            name="xxx",
+        )
+        assert ser.dtype == "Period[D]"
+
+        expected = Series(
+            [Period("2015-01-04", freq="D"), Period("2015-01-05", freq="D")],
+            name="xxx",
+        )
+
+        obj = tm.box_expected(ser, box_with_array)
+        if box_with_array is pd.DataFrame:
+            assert (obj.dtypes == "Period[D]").all()
+
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = obj + three_days
+        tm.assert_equal(result, expected)
+
+        result = three_days + obj
+        tm.assert_equal(result, expected)
+
+    def test_ops_series_period(self):
+        # GH#13043
+        ser = Series(
+            [Period("2015-01-01", freq="D"), Period("2015-01-02", freq="D")],
+            name="xxx",
+        )
+        assert ser.dtype == "Period[D]"
+
+        per = Period("2015-01-10", freq="D")
+        off = per.freq
+        # dtype will be object because of original dtype
+        expected = Series([9 * off, 8 * off], name="xxx", dtype=object)
+        tm.assert_series_equal(per - ser, expected)
+        tm.assert_series_equal(ser - per, -1 * expected)
+
+        s2 = Series(
+            [Period("2015-01-05", freq="D"), Period("2015-01-04", freq="D")],
+            name="xxx",
+        )
+        assert s2.dtype == "Period[D]"
+
+        expected = Series([4 * off, 2 * off], name="xxx", dtype=object)
+        tm.assert_series_equal(s2 - ser, expected)
+        tm.assert_series_equal(ser - s2, -1 * expected)
+
+
+class TestPeriodIndexSeriesMethods:
+    """Test PeriodIndex and Period Series Ops consistency"""
+
+    def _check(self, values, func, expected):
+        idx = PeriodIndex(values)
+        result = func(idx)
+        tm.assert_equal(result, expected)
+
+        ser = Series(values)
+        result = func(ser)
+
+        exp = Series(expected, name=values.name)
+        tm.assert_series_equal(result, exp)
+
+    def test_pi_ops(self):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+
+        expected = PeriodIndex(
+            ["2011-03", "2011-04", "2011-05", "2011-06"], freq="M", name="idx"
+        )
+
+        self._check(idx, lambda x: x + 2, expected)
+        self._check(idx, lambda x: 2 + x, expected)
+
+        self._check(idx + 2, lambda x: x - 2, idx)
+
+        result = idx - Period("2011-01", freq="M")
+        off = idx.freq
+        exp = pd.Index([0 * off, 1 * off, 2 * off, 3 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        result = Period("2011-01", freq="M") - idx
+        exp = pd.Index([0 * off, -1 * off, -2 * off, -3 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+    @pytest.mark.parametrize("ng", ["str", 1.5])
+    @pytest.mark.parametrize(
+        "func",
+        [
+            lambda obj, ng: obj + ng,
+            lambda obj, ng: ng + obj,
+            lambda obj, ng: obj - ng,
+            lambda obj, ng: ng - obj,
+            lambda obj, ng: np.add(obj, ng),
+            lambda obj, ng: np.add(ng, obj),
+            lambda obj, ng: np.subtract(obj, ng),
+            lambda obj, ng: np.subtract(ng, obj),
+        ],
+    )
+    def test_parr_ops_errors(self, ng, func, box_with_array):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+        obj = tm.box_expected(idx, box_with_array)
+        msg = "|".join(
+            [
+                r"unsupported operand type\(s\)",
+                "can only concatenate",
+                r"must be str",
+                "object to str implicitly",
+            ]
+        )
+
+        with pytest.raises(TypeError, match=msg):
+            func(obj, ng)
+
+    def test_pi_ops_nat(self):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "NaT", "2011-04"], freq="M", name="idx"
+        )
+        expected = PeriodIndex(
+            ["2011-03", "2011-04", "NaT", "2011-06"], freq="M", name="idx"
+        )
+
+        self._check(idx, lambda x: x + 2, expected)
+        self._check(idx, lambda x: 2 + x, expected)
+        self._check(idx, lambda x: np.add(x, 2), expected)
+
+        self._check(idx + 2, lambda x: x - 2, idx)
+        self._check(idx + 2, lambda x: np.subtract(x, 2), idx)
+
+        # freq with mult
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "NaT", "2011-04"], freq="2M", name="idx"
+        )
+        expected = PeriodIndex(
+            ["2011-07", "2011-08", "NaT", "2011-10"], freq="2M", name="idx"
+        )
+
+        self._check(idx, lambda x: x + 3, expected)
+        self._check(idx, lambda x: 3 + x, expected)
+        self._check(idx, lambda x: np.add(x, 3), expected)
+
+        self._check(idx + 3, lambda x: x - 3, idx)
+        self._check(idx + 3, lambda x: np.subtract(x, 3), idx)
+
+    def test_pi_ops_array_int(self):
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "NaT", "2011-04"], freq="M", name="idx"
+        )
+        f = lambda x: x + np.array([1, 2, 3, 4])
+        exp = PeriodIndex(
+            ["2011-02", "2011-04", "NaT", "2011-08"], freq="M", name="idx"
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: np.add(x, np.array([4, -1, 1, 2]))
+        exp = PeriodIndex(
+            ["2011-05", "2011-01", "NaT", "2011-06"], freq="M", name="idx"
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: x - np.array([1, 2, 3, 4])
+        exp = PeriodIndex(
+            ["2010-12", "2010-12", "NaT", "2010-12"], freq="M", name="idx"
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: np.subtract(x, np.array([3, 2, 3, -2]))
+        exp = PeriodIndex(
+            ["2010-10", "2010-12", "NaT", "2011-06"], freq="M", name="idx"
+        )
+        self._check(idx, f, exp)
+
+    def test_pi_ops_offset(self):
+        idx = PeriodIndex(
+            ["2011-01-01", "2011-02-01", "2011-03-01", "2011-04-01"],
+            freq="D",
+            name="idx",
+        )
+        f = lambda x: x + pd.offsets.Day()
+        exp = PeriodIndex(
+            ["2011-01-02", "2011-02-02", "2011-03-02", "2011-04-02"],
+            freq="D",
+            name="idx",
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: x + pd.offsets.Day(2)
+        exp = PeriodIndex(
+            ["2011-01-03", "2011-02-03", "2011-03-03", "2011-04-03"],
+            freq="D",
+            name="idx",
+        )
+        self._check(idx, f, exp)
+
+        f = lambda x: x - pd.offsets.Day(2)
+        exp = PeriodIndex(
+            ["2010-12-30", "2011-01-30", "2011-02-27", "2011-03-30"],
+            freq="D",
+            name="idx",
+        )
+        self._check(idx, f, exp)
+
+    def test_pi_offset_errors(self):
+        idx = PeriodIndex(
+            ["2011-01-01", "2011-02-01", "2011-03-01", "2011-04-01"],
+            freq="D",
+            name="idx",
+        )
+        ser = Series(idx)
+
+        msg = (
+            "Cannot add/subtract timedelta-like from PeriodArray that is not "
+            "an integer multiple of the PeriodArray's freq"
+        )
+        for obj in [idx, ser]:
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                obj + pd.offsets.Hour(2)
+
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                pd.offsets.Hour(2) + obj
+
+            with pytest.raises(IncompatibleFrequency, match=msg):
+                obj - pd.offsets.Hour(2)
+
+    def test_pi_sub_period(self):
+        # GH#13071
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+
+        result = idx - Period("2012-01", freq="M")
+        off = idx.freq
+        exp = pd.Index([-12 * off, -11 * off, -10 * off, -9 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        result = np.subtract(idx, Period("2012-01", freq="M"))
+        tm.assert_index_equal(result, exp)
+
+        result = Period("2012-01", freq="M") - idx
+        exp = pd.Index([12 * off, 11 * off, 10 * off, 9 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        result = np.subtract(Period("2012-01", freq="M"), idx)
+        tm.assert_index_equal(result, exp)
+
+        exp = TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name="idx")
+        result = idx - Period("NaT", freq="M")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == exp.freq
+
+        result = Period("NaT", freq="M") - idx
+        tm.assert_index_equal(result, exp)
+        assert result.freq == exp.freq
+
+    def test_pi_sub_pdnat(self):
+        # GH#13071, GH#19389
+        idx = PeriodIndex(
+            ["2011-01", "2011-02", "NaT", "2011-04"], freq="M", name="idx"
+        )
+        exp = TimedeltaIndex([pd.NaT] * 4, name="idx")
+        tm.assert_index_equal(pd.NaT - idx, exp)
+        tm.assert_index_equal(idx - pd.NaT, exp)
+
+    def test_pi_sub_period_nat(self):
+        # GH#13071
+        idx = PeriodIndex(
+            ["2011-01", "NaT", "2011-03", "2011-04"], freq="M", name="idx"
+        )
+
+        result = idx - Period("2012-01", freq="M")
+        off = idx.freq
+        exp = pd.Index([-12 * off, pd.NaT, -10 * off, -9 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        result = Period("2012-01", freq="M") - idx
+        exp = pd.Index([12 * off, pd.NaT, 10 * off, 9 * off], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        exp = TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name="idx")
+        tm.assert_index_equal(idx - Period("NaT", freq="M"), exp)
+        tm.assert_index_equal(Period("NaT", freq="M") - idx, exp)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arithmetic/test_timedelta64.py

@@ -0,0 +1,2173 @@
+# Arithmetic tests for DataFrame/Series/Index/Array classes that should
+# behave identically.
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+from pandas.errors import (
+    OutOfBoundsDatetime,
+    PerformanceWarning,
+)
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    NaT,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+    offsets,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import NumpyExtensionArray
+from pandas.tests.arithmetic.common import (
+    assert_invalid_addsub_type,
+    assert_invalid_comparison,
+    get_upcast_box,
+)
+
+
+def assert_dtype(obj, expected_dtype):
+    """
+    Helper to check the dtype for a Series, Index, or single-column DataFrame.
+    """
+    dtype = tm.get_dtype(obj)
+
+    assert dtype == expected_dtype
+
+
+def get_expected_name(box, names):
+    if box is DataFrame:
+        # Since we are operating with a DataFrame and a non-DataFrame,
+        # the non-DataFrame is cast to Series and its name ignored.
+        exname = names[0]
+    elif box in [tm.to_array, pd.array]:
+        exname = names[1]
+    else:
+        exname = names[2]
+    return exname
+
+
+# ------------------------------------------------------------------
+# Timedelta64[ns] dtype Comparisons
+
+
+class TestTimedelta64ArrayLikeComparisons:
+    # Comparison tests for timedelta64[ns] vectors fully parametrized over
+    #  DataFrame/Series/TimedeltaIndex/TimedeltaArray.  Ideally all comparison
+    #  tests will eventually end up here.
+
+    def test_compare_timedelta64_zerodim(self, box_with_array):
+        # GH#26689 should unbox when comparing with zerodim array
+        box = box_with_array
+        xbox = box_with_array if box_with_array not in [Index, pd.array] else np.ndarray
+
+        tdi = timedelta_range("2h", periods=4)
+        other = np.array(tdi.to_numpy()[0])
+
+        tdi = tm.box_expected(tdi, box)
+        res = tdi <= other
+        expected = np.array([True, False, False, False])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(res, expected)
+
+    @pytest.mark.parametrize(
+        "td_scalar",
+        [
+            timedelta(days=1),
+            Timedelta(days=1),
+            Timedelta(days=1).to_timedelta64(),
+            offsets.Hour(24),
+        ],
+    )
+    def test_compare_timedeltalike_scalar(self, box_with_array, td_scalar):
+        # regression test for GH#5963
+        box = box_with_array
+        xbox = box if box not in [Index, pd.array] else np.ndarray
+
+        ser = Series([timedelta(days=1), timedelta(days=2)])
+        ser = tm.box_expected(ser, box)
+        actual = ser > td_scalar
+        expected = Series([False, True])
+        expected = tm.box_expected(expected, xbox)
+        tm.assert_equal(actual, expected)
+
+    @pytest.mark.parametrize(
+        "invalid",
+        [
+            345600000000000,
+            "a",
+            Timestamp("2021-01-01"),
+            Timestamp("2021-01-01").now("UTC"),
+            Timestamp("2021-01-01").now().to_datetime64(),
+            Timestamp("2021-01-01").now().to_pydatetime(),
+            Timestamp("2021-01-01").date(),
+            np.array(4),  # zero-dim mismatched dtype
+        ],
+    )
+    def test_td64_comparisons_invalid(self, box_with_array, invalid):
+        # GH#13624 for str
+        box = box_with_array
+
+        rng = timedelta_range("1 days", periods=10)
+        obj = tm.box_expected(rng, box)
+
+        assert_invalid_comparison(obj, invalid, box)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            list(range(10)),
+            np.arange(10),
+            np.arange(10).astype(np.float32),
+            np.arange(10).astype(object),
+            pd.date_range("1970-01-01", periods=10, tz="UTC").array,
+            np.array(pd.date_range("1970-01-01", periods=10)),
+            list(pd.date_range("1970-01-01", periods=10)),
+            pd.date_range("1970-01-01", periods=10).astype(object),
+            pd.period_range("1971-01-01", freq="D", periods=10).array,
+            pd.period_range("1971-01-01", freq="D", periods=10).astype(object),
+        ],
+    )
+    def test_td64arr_cmp_arraylike_invalid(self, other, box_with_array):
+        # We don't parametrize this over box_with_array because listlike
+        #  other plays poorly with assert_invalid_comparison reversed checks
+
+        rng = timedelta_range("1 days", periods=10)._data
+        rng = tm.box_expected(rng, box_with_array)
+        assert_invalid_comparison(rng, other, box_with_array)
+
+    def test_td64arr_cmp_mixed_invalid(self):
+        rng = timedelta_range("1 days", periods=5)._data
+        other = np.array([0, 1, 2, rng[3], Timestamp("2021-01-01")])
+
+        result = rng == other
+        expected = np.array([False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng != other
+        tm.assert_numpy_array_equal(result, ~expected)
+
+        msg = "Invalid comparison between|Cannot compare type|not supported between"
+        with pytest.raises(TypeError, match=msg):
+            rng < other
+        with pytest.raises(TypeError, match=msg):
+            rng > other
+        with pytest.raises(TypeError, match=msg):
+            rng <= other
+        with pytest.raises(TypeError, match=msg):
+            rng >= other
+
+
+class TestTimedelta64ArrayComparisons:
+    # TODO: All of these need to be parametrized over box
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_comp_nat(self, dtype):
+        left = TimedeltaIndex([Timedelta("1 days"), NaT, Timedelta("3 days")])
+        right = TimedeltaIndex([NaT, NaT, Timedelta("3 days")])
+
+        lhs, rhs = left, right
+        if dtype is object:
+            lhs, rhs = left.astype(object), right.astype(object)
+
+        result = rhs == lhs
+        expected = np.array([False, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rhs != lhs
+        expected = np.array([True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(lhs == NaT, expected)
+        tm.assert_numpy_array_equal(NaT == rhs, expected)
+
+        expected = np.array([True, True, True])
+        tm.assert_numpy_array_equal(lhs != NaT, expected)
+        tm.assert_numpy_array_equal(NaT != lhs, expected)
+
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(lhs < NaT, expected)
+        tm.assert_numpy_array_equal(NaT > lhs, expected)
+
+    @pytest.mark.parametrize(
+        "idx2",
+        [
+            TimedeltaIndex(
+                ["2 day", "2 day", NaT, NaT, "1 day 00:00:02", "5 days 00:00:03"]
+            ),
+            np.array(
+                [
+                    np.timedelta64(2, "D"),
+                    np.timedelta64(2, "D"),
+                    np.timedelta64("nat"),
+                    np.timedelta64("nat"),
+                    np.timedelta64(1, "D") + np.timedelta64(2, "s"),
+                    np.timedelta64(5, "D") + np.timedelta64(3, "s"),
+                ]
+            ),
+        ],
+    )
+    def test_comparisons_nat(self, idx2):
+        idx1 = TimedeltaIndex(
+            [
+                "1 day",
+                NaT,
+                "1 day 00:00:01",
+                NaT,
+                "1 day 00:00:01",
+                "5 day 00:00:03",
+            ]
+        )
+        # Check pd.NaT is handles as the same as np.nan
+        result = idx1 < idx2
+        expected = np.array([True, False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx2 > idx1
+        expected = np.array([True, False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx1 <= idx2
+        expected = np.array([True, False, False, False, True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx2 >= idx1
+        expected = np.array([True, False, False, False, True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx1 == idx2
+        expected = np.array([False, False, False, False, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx1 != idx2
+        expected = np.array([True, True, True, True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    # TODO: better name
+    def test_comparisons_coverage(self):
+        rng = timedelta_range("1 days", periods=10)
+
+        result = rng < rng[3]
+        expected = np.array([True, True, True] + [False] * 7)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = rng == list(rng)
+        exp = rng == rng
+        tm.assert_numpy_array_equal(result, exp)
+
+
+# ------------------------------------------------------------------
+# Timedelta64[ns] dtype Arithmetic Operations
+
+
+class TestTimedelta64ArithmeticUnsorted:
+    # Tests moved from type-specific test files but not
+    #  yet sorted/parametrized/de-duplicated
+
+    def test_ufunc_coercions(self):
+        # normal ops are also tested in tseries/test_timedeltas.py
+        idx = TimedeltaIndex(["2h", "4h", "6h", "8h", "10h"], freq="2h", name="x")
+
+        for result in [idx * 2, np.multiply(idx, 2)]:
+            assert isinstance(result, TimedeltaIndex)
+            exp = TimedeltaIndex(["4h", "8h", "12h", "16h", "20h"], freq="4h", name="x")
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "4h"
+
+        for result in [idx / 2, np.divide(idx, 2)]:
+            assert isinstance(result, TimedeltaIndex)
+            exp = TimedeltaIndex(["1h", "2h", "3h", "4h", "5h"], freq="h", name="x")
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "h"
+
+        for result in [-idx, np.negative(idx)]:
+            assert isinstance(result, TimedeltaIndex)
+            exp = TimedeltaIndex(
+                ["-2h", "-4h", "-6h", "-8h", "-10h"], freq="-2h", name="x"
+            )
+            tm.assert_index_equal(result, exp)
+            assert result.freq == "-2h"
+
+        idx = TimedeltaIndex(["-2h", "-1h", "0h", "1h", "2h"], freq="h", name="x")
+        for result in [abs(idx), np.absolute(idx)]:
+            assert isinstance(result, TimedeltaIndex)
+            exp = TimedeltaIndex(["2h", "1h", "0h", "1h", "2h"], freq=None, name="x")
+            tm.assert_index_equal(result, exp)
+            assert result.freq is None
+
+    def test_subtraction_ops(self):
+        # with datetimes/timedelta and tdi/dti
+        tdi = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo")
+        dti = pd.date_range("20130101", periods=3, name="bar")
+        td = Timedelta("1 days")
+        dt = Timestamp("20130101")
+
+        msg = "cannot subtract a datelike from a TimedeltaArray"
+        with pytest.raises(TypeError, match=msg):
+            tdi - dt
+        with pytest.raises(TypeError, match=msg):
+            tdi - dti
+
+        msg = r"unsupported operand type\(s\) for -"
+        with pytest.raises(TypeError, match=msg):
+            td - dt
+
+        msg = "(bad|unsupported) operand type for unary"
+        with pytest.raises(TypeError, match=msg):
+            td - dti
+
+        result = dt - dti
+        expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"], name="bar")
+        tm.assert_index_equal(result, expected)
+
+        result = dti - dt
+        expected = TimedeltaIndex(["0 days", "1 days", "2 days"], name="bar")
+        tm.assert_index_equal(result, expected)
+
+        result = tdi - td
+        expected = TimedeltaIndex(["0 days", NaT, "1 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = td - tdi
+        expected = TimedeltaIndex(["0 days", NaT, "-1 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = dti - td
+        expected = DatetimeIndex(
+            ["20121231", "20130101", "20130102"], dtype="M8[ns]", freq="D", name="bar"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = dt - tdi
+        expected = DatetimeIndex(
+            ["20121231", NaT, "20121230"], dtype="M8[ns]", name="foo"
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_subtraction_ops_with_tz(self, box_with_array):
+        # check that dt/dti subtraction ops with tz are validated
+        dti = pd.date_range("20130101", periods=3)
+        dti = tm.box_expected(dti, box_with_array)
+        ts = Timestamp("20130101")
+        dt = ts.to_pydatetime()
+        dti_tz = pd.date_range("20130101", periods=3).tz_localize("US/Eastern")
+        dti_tz = tm.box_expected(dti_tz, box_with_array)
+        ts_tz = Timestamp("20130101").tz_localize("US/Eastern")
+        ts_tz2 = Timestamp("20130101").tz_localize("CET")
+        dt_tz = ts_tz.to_pydatetime()
+        td = Timedelta("1 days")
+
+        def _check(result, expected):
+            assert result == expected
+            assert isinstance(result, Timedelta)
+
+        # scalars
+        result = ts - ts
+        expected = Timedelta("0 days")
+        _check(result, expected)
+
+        result = dt_tz - ts_tz
+        expected = Timedelta("0 days")
+        _check(result, expected)
+
+        result = ts_tz - dt_tz
+        expected = Timedelta("0 days")
+        _check(result, expected)
+
+        # tz mismatches
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects."
+        with pytest.raises(TypeError, match=msg):
+            dt_tz - ts
+        msg = "can't subtract offset-naive and offset-aware datetimes"
+        with pytest.raises(TypeError, match=msg):
+            dt_tz - dt
+        msg = "can't subtract offset-naive and offset-aware datetimes"
+        with pytest.raises(TypeError, match=msg):
+            dt - dt_tz
+        msg = "Cannot subtract tz-naive and tz-aware datetime-like objects."
+        with pytest.raises(TypeError, match=msg):
+            ts - dt_tz
+        with pytest.raises(TypeError, match=msg):
+            ts_tz2 - ts
+        with pytest.raises(TypeError, match=msg):
+            ts_tz2 - dt
+
+        msg = "Cannot subtract tz-naive and tz-aware"
+        # with dti
+        with pytest.raises(TypeError, match=msg):
+            dti - ts_tz
+        with pytest.raises(TypeError, match=msg):
+            dti_tz - ts
+
+        result = dti_tz - dt_tz
+        expected = TimedeltaIndex(["0 days", "1 days", "2 days"])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = dt_tz - dti_tz
+        expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = dti_tz - ts_tz
+        expected = TimedeltaIndex(["0 days", "1 days", "2 days"])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = ts_tz - dti_tz
+        expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"])
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+        result = td - td
+        expected = Timedelta("0 days")
+        _check(result, expected)
+
+        result = dti_tz - td
+        expected = DatetimeIndex(
+            ["20121231", "20130101", "20130102"], tz="US/Eastern"
+        ).as_unit("ns")
+        expected = tm.box_expected(expected, box_with_array)
+        tm.assert_equal(result, expected)
+
+    def test_dti_tdi_numeric_ops(self):
+        # These are normally union/diff set-like ops
+        tdi = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo")
+        dti = pd.date_range("20130101", periods=3, name="bar")
+
+        result = tdi - tdi
+        expected = TimedeltaIndex(["0 days", NaT, "0 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = tdi + tdi
+        expected = TimedeltaIndex(["2 days", NaT, "4 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = dti - tdi  # name will be reset
+        expected = DatetimeIndex(["20121231", NaT, "20130101"], dtype="M8[ns]")
+        tm.assert_index_equal(result, expected)
+
+    def test_addition_ops(self):
+        # with datetimes/timedelta and tdi/dti
+        tdi = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo")
+        dti = pd.date_range("20130101", periods=3, name="bar")
+        td = Timedelta("1 days")
+        dt = Timestamp("20130101")
+
+        result = tdi + dt
+        expected = DatetimeIndex(
+            ["20130102", NaT, "20130103"], dtype="M8[ns]", name="foo"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = dt + tdi
+        expected = DatetimeIndex(
+            ["20130102", NaT, "20130103"], dtype="M8[ns]", name="foo"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = td + tdi
+        expected = TimedeltaIndex(["2 days", NaT, "3 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        result = tdi + td
+        expected = TimedeltaIndex(["2 days", NaT, "3 days"], name="foo")
+        tm.assert_index_equal(result, expected)
+
+        # unequal length
+        msg = "cannot add indices of unequal length"
+        with pytest.raises(ValueError, match=msg):
+            tdi + dti[0:1]
+        with pytest.raises(ValueError, match=msg):
+            tdi[0:1] + dti
+
+        # random indexes
+        msg = "Addition/subtraction of integers and integer-arrays"
+        with pytest.raises(TypeError, match=msg):
+            tdi + Index([1, 2, 3], dtype=np.int64)
+
+        # this is a union!
+        # FIXME: don't leave commented-out
+        # pytest.raises(TypeError, lambda : Index([1,2,3]) + tdi)
+
+        result = tdi + dti  # name will be reset
+        expected = DatetimeIndex(["20130102", NaT, "20130105"], dtype="M8[ns]")
+        tm.assert_index_equal(result, expected)
+
+        result = dti + tdi  # name will be reset
+        expected = DatetimeIndex(["20130102", NaT, "20130105"], dtype="M8[ns]")
+        tm.assert_index_equal(result, expected)
+
+        result = dt + td
+        expected = Timestamp("20130102")
+        assert result == expected
+
+        result = td + dt
+        expected = Timestamp("20130102")
+        assert result == expected
+
+    # TODO: Needs more informative name, probably split up into
+    # more targeted tests
+    @pytest.mark.parametrize("freq", ["D", "B"])
+    def test_timedelta(self, freq):
+        index = pd.date_range("1/1/2000", periods=50, freq=freq)
+
+        shifted = index + timedelta(1)
+        back = shifted + timedelta(-1)
+        back = back._with_freq("infer")
+        tm.assert_index_equal(index, back)
+
+        if freq == "D":
+            expected = pd.tseries.offsets.Day(1)
+            assert index.freq == expected
+            assert shifted.freq == expected
+            assert back.freq == expected
+        else:  # freq == 'B'
+            assert index.freq == pd.tseries.offsets.BusinessDay(1)
+            assert shifted.freq is None
+            assert back.freq == pd.tseries.offsets.BusinessDay(1)
+
+        result = index - timedelta(1)
+        expected = index + timedelta(-1)
+        tm.assert_index_equal(result, expected)
+
+    def test_timedelta_tick_arithmetic(self):
+        # GH#4134, buggy with timedeltas
+        rng = pd.date_range("2013", "2014")
+        s = Series(rng)
+        result1 = rng - offsets.Hour(1)
+        result2 = DatetimeIndex(s - np.timedelta64(100000000))
+        result3 = rng - np.timedelta64(100000000)
+        result4 = DatetimeIndex(s - offsets.Hour(1))
+
+        assert result1.freq == rng.freq
+        result1 = result1._with_freq(None)
+        tm.assert_index_equal(result1, result4)
+
+        assert result3.freq == rng.freq
+        result3 = result3._with_freq(None)
+        tm.assert_index_equal(result2, result3)
+
+    def test_tda_add_sub_index(self):
+        # Check that TimedeltaArray defers to Index on arithmetic ops
+        tdi = TimedeltaIndex(["1 days", NaT, "2 days"])
+        tda = tdi.array
+
+        dti = pd.date_range("1999-12-31", periods=3, freq="D")
+
+        result = tda + dti
+        expected = tdi + dti
+        tm.assert_index_equal(result, expected)
+
+        result = tda + tdi
+        expected = tdi + tdi
+        tm.assert_index_equal(result, expected)
+
+        result = tda - tdi
+        expected = tdi - tdi
+        tm.assert_index_equal(result, expected)
+
+    def test_tda_add_dt64_object_array(self, box_with_array, tz_naive_fixture):
+        # Result should be cast back to DatetimeArray
+        box = box_with_array
+
+        dti = pd.date_range("2016-01-01", periods=3, tz=tz_naive_fixture)
+        dti = dti._with_freq(None)
+        tdi = dti - dti
+
+        obj = tm.box_expected(tdi, box)
+        other = tm.box_expected(dti, box)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = obj + other.astype(object)
+        tm.assert_equal(result, other.astype(object))
+
+    # -------------------------------------------------------------
+    # Binary operations TimedeltaIndex and timedelta-like
+
+    def test_tdi_iadd_timedeltalike(self, two_hours, box_with_array):
+        # only test adding/sub offsets as + is now numeric
+        rng = timedelta_range("1 days", "10 days")
+        expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D")
+
+        rng = tm.box_expected(rng, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        orig_rng = rng
+        rng += two_hours
+        tm.assert_equal(rng, expected)
+        if box_with_array is not Index:
+            # Check that operation is actually inplace
+            tm.assert_equal(orig_rng, expected)
+
+    def test_tdi_isub_timedeltalike(self, two_hours, box_with_array):
+        # only test adding/sub offsets as - is now numeric
+        rng = timedelta_range("1 days", "10 days")
+        expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00")
+
+        rng = tm.box_expected(rng, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        orig_rng = rng
+        rng -= two_hours
+        tm.assert_equal(rng, expected)
+        if box_with_array is not Index:
+            # Check that operation is actually inplace
+            tm.assert_equal(orig_rng, expected)
+
+    # -------------------------------------------------------------
+
+    def test_tdi_ops_attributes(self):
+        rng = timedelta_range("2 days", periods=5, freq="2D", name="x")
+
+        result = rng + 1 * rng.freq
+        exp = timedelta_range("4 days", periods=5, freq="2D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "2D"
+
+        result = rng - 2 * rng.freq
+        exp = timedelta_range("-2 days", periods=5, freq="2D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "2D"
+
+        result = rng * 2
+        exp = timedelta_range("4 days", periods=5, freq="4D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "4D"
+
+        result = rng / 2
+        exp = timedelta_range("1 days", periods=5, freq="D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "D"
+
+        result = -rng
+        exp = timedelta_range("-2 days", periods=5, freq="-2D", name="x")
+        tm.assert_index_equal(result, exp)
+        assert result.freq == "-2D"
+
+        rng = timedelta_range("-2 days", periods=5, freq="D", name="x")
+
+        result = abs(rng)
+        exp = TimedeltaIndex(
+            ["2 days", "1 days", "0 days", "1 days", "2 days"], name="x"
+        )
+        tm.assert_index_equal(result, exp)
+        assert result.freq is None
+
+
+class TestAddSubNaTMasking:
+    # TODO: parametrize over boxes
+
+    @pytest.mark.parametrize("str_ts", ["1950-01-01", "1980-01-01"])
+    def test_tdarr_add_timestamp_nat_masking(self, box_with_array, str_ts):
+        # GH#17991 checking for overflow-masking with NaT
+        tdinat = pd.to_timedelta(["24658 days 11:15:00", "NaT"])
+        tdobj = tm.box_expected(tdinat, box_with_array)
+
+        ts = Timestamp(str_ts)
+        ts_variants = [
+            ts,
+            ts.to_pydatetime(),
+            ts.to_datetime64().astype("datetime64[ns]"),
+            ts.to_datetime64().astype("datetime64[D]"),
+        ]
+
+        for variant in ts_variants:
+            res = tdobj + variant
+            if box_with_array is DataFrame:
+                assert res.iloc[1, 1] is NaT
+            else:
+                assert res[1] is NaT
+
+    def test_tdi_add_overflow(self):
+        # See GH#14068
+        # preliminary test scalar analogue of vectorized tests below
+        # TODO: Make raised error message more informative and test
+        with pytest.raises(OutOfBoundsDatetime, match="10155196800000000000"):
+            pd.to_timedelta(106580, "D") + Timestamp("2000")
+        with pytest.raises(OutOfBoundsDatetime, match="10155196800000000000"):
+            Timestamp("2000") + pd.to_timedelta(106580, "D")
+
+        _NaT = NaT._value + 1
+        msg = "Overflow in int64 addition"
+        with pytest.raises(OverflowError, match=msg):
+            pd.to_timedelta([106580], "D") + Timestamp("2000")
+        with pytest.raises(OverflowError, match=msg):
+            Timestamp("2000") + pd.to_timedelta([106580], "D")
+        with pytest.raises(OverflowError, match=msg):
+            pd.to_timedelta([_NaT]) - Timedelta("1 days")
+        with pytest.raises(OverflowError, match=msg):
+            pd.to_timedelta(["5 days", _NaT]) - Timedelta("1 days")
+        with pytest.raises(OverflowError, match=msg):
+            (
+                pd.to_timedelta([_NaT, "5 days", "1 hours"])
+                - pd.to_timedelta(["7 seconds", _NaT, "4 hours"])
+            )
+
+        # These should not overflow!
+        exp = TimedeltaIndex([NaT])
+        result = pd.to_timedelta([NaT]) - Timedelta("1 days")
+        tm.assert_index_equal(result, exp)
+
+        exp = TimedeltaIndex(["4 days", NaT])
+        result = pd.to_timedelta(["5 days", NaT]) - Timedelta("1 days")
+        tm.assert_index_equal(result, exp)
+
+        exp = TimedeltaIndex([NaT, NaT, "5 hours"])
+        result = pd.to_timedelta([NaT, "5 days", "1 hours"]) + pd.to_timedelta(
+            ["7 seconds", NaT, "4 hours"]
+        )
+        tm.assert_index_equal(result, exp)
+
+
+class TestTimedeltaArraylikeAddSubOps:
+    # Tests for timedelta64[ns] __add__, __sub__, __radd__, __rsub__
+
+    def test_sub_nat_retain_unit(self):
+        ser = pd.to_timedelta(Series(["00:00:01"])).astype("m8[s]")
+
+        result = ser - NaT
+        expected = Series([NaT], dtype="m8[s]")
+        tm.assert_series_equal(result, expected)
+
+    # TODO: moved from tests.indexes.timedeltas.test_arithmetic; needs
+    #  parametrization+de-duplication
+    def test_timedelta_ops_with_missing_values(self):
+        # setup
+        s1 = pd.to_timedelta(Series(["00:00:01"]))
+        s2 = pd.to_timedelta(Series(["00:00:02"]))
+
+        sn = pd.to_timedelta(Series([NaT], dtype="m8[ns]"))
+
+        df1 = DataFrame(["00:00:01"]).apply(pd.to_timedelta)
+        df2 = DataFrame(["00:00:02"]).apply(pd.to_timedelta)
+
+        dfn = DataFrame([NaT._value]).apply(pd.to_timedelta)
+
+        scalar1 = pd.to_timedelta("00:00:01")
+        scalar2 = pd.to_timedelta("00:00:02")
+        timedelta_NaT = pd.to_timedelta("NaT")
+
+        actual = scalar1 + scalar1
+        assert actual == scalar2
+        actual = scalar2 - scalar1
+        assert actual == scalar1
+
+        actual = s1 + s1
+        tm.assert_series_equal(actual, s2)
+        actual = s2 - s1
+        tm.assert_series_equal(actual, s1)
+
+        actual = s1 + scalar1
+        tm.assert_series_equal(actual, s2)
+        actual = scalar1 + s1
+        tm.assert_series_equal(actual, s2)
+        actual = s2 - scalar1
+        tm.assert_series_equal(actual, s1)
+        actual = -scalar1 + s2
+        tm.assert_series_equal(actual, s1)
+
+        actual = s1 + timedelta_NaT
+        tm.assert_series_equal(actual, sn)
+        actual = timedelta_NaT + s1
+        tm.assert_series_equal(actual, sn)
+        actual = s1 - timedelta_NaT
+        tm.assert_series_equal(actual, sn)
+        actual = -timedelta_NaT + s1
+        tm.assert_series_equal(actual, sn)
+
+        msg = "unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            s1 + np.nan
+        with pytest.raises(TypeError, match=msg):
+            np.nan + s1
+        with pytest.raises(TypeError, match=msg):
+            s1 - np.nan
+        with pytest.raises(TypeError, match=msg):
+            -np.nan + s1
+
+        actual = s1 + NaT
+        tm.assert_series_equal(actual, sn)
+        actual = s2 - NaT
+        tm.assert_series_equal(actual, sn)
+
+        actual = s1 + df1
+        tm.assert_frame_equal(actual, df2)
+        actual = s2 - df1
+        tm.assert_frame_equal(actual, df1)
+        actual = df1 + s1
+        tm.assert_frame_equal(actual, df2)
+        actual = df2 - s1
+        tm.assert_frame_equal(actual, df1)
+
+        actual = df1 + df1
+        tm.assert_frame_equal(actual, df2)
+        actual = df2 - df1
+        tm.assert_frame_equal(actual, df1)
+
+        actual = df1 + scalar1
+        tm.assert_frame_equal(actual, df2)
+        actual = df2 - scalar1
+        tm.assert_frame_equal(actual, df1)
+
+        actual = df1 + timedelta_NaT
+        tm.assert_frame_equal(actual, dfn)
+        actual = df1 - timedelta_NaT
+        tm.assert_frame_equal(actual, dfn)
+
+        msg = "cannot subtract a datelike from|unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            df1 + np.nan
+        with pytest.raises(TypeError, match=msg):
+            df1 - np.nan
+
+        actual = df1 + NaT  # NaT is datetime, not timedelta
+        tm.assert_frame_equal(actual, dfn)
+        actual = df1 - NaT
+        tm.assert_frame_equal(actual, dfn)
+
+    # TODO: moved from tests.series.test_operators, needs splitting, cleanup,
+    # de-duplication, box-parametrization...
+    def test_operators_timedelta64(self):
+        # series ops
+        v1 = pd.date_range("2012-1-1", periods=3, freq="D")
+        v2 = pd.date_range("2012-1-2", periods=3, freq="D")
+        rs = Series(v2) - Series(v1)
+        xp = Series(1e9 * 3600 * 24, rs.index).astype("int64").astype("timedelta64[ns]")
+        tm.assert_series_equal(rs, xp)
+        assert rs.dtype == "timedelta64[ns]"
+
+        df = DataFrame({"A": v1})
+        td = Series([timedelta(days=i) for i in range(3)])
+        assert td.dtype == "timedelta64[ns]"
+
+        # series on the rhs
+        result = df["A"] - df["A"].shift()
+        assert result.dtype == "timedelta64[ns]"
+
+        result = df["A"] + td
+        assert result.dtype == "M8[ns]"
+
+        # scalar Timestamp on rhs
+        maxa = df["A"].max()
+        assert isinstance(maxa, Timestamp)
+
+        resultb = df["A"] - df["A"].max()
+        assert resultb.dtype == "timedelta64[ns]"
+
+        # timestamp on lhs
+        result = resultb + df["A"]
+        values = [Timestamp("20111230"), Timestamp("20120101"), Timestamp("20120103")]
+        expected = Series(values, dtype="M8[ns]", name="A")
+        tm.assert_series_equal(result, expected)
+
+        # datetimes on rhs
+        result = df["A"] - datetime(2001, 1, 1)
+        expected = Series([timedelta(days=4017 + i) for i in range(3)], name="A")
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "m8[ns]"
+
+        d = datetime(2001, 1, 1, 3, 4)
+        resulta = df["A"] - d
+        assert resulta.dtype == "m8[ns]"
+
+        # roundtrip
+        resultb = resulta + d
+        tm.assert_series_equal(df["A"], resultb)
+
+        # timedeltas on rhs
+        td = timedelta(days=1)
+        resulta = df["A"] + td
+        resultb = resulta - td
+        tm.assert_series_equal(resultb, df["A"])
+        assert resultb.dtype == "M8[ns]"
+
+        # roundtrip
+        td = timedelta(minutes=5, seconds=3)
+        resulta = df["A"] + td
+        resultb = resulta - td
+        tm.assert_series_equal(df["A"], resultb)
+        assert resultb.dtype == "M8[ns]"
+
+        # inplace
+        value = rs[2] + np.timedelta64(timedelta(minutes=5, seconds=1))
+        rs[2] += np.timedelta64(timedelta(minutes=5, seconds=1))
+        assert rs[2] == value
+
+    def test_timedelta64_ops_nat(self):
+        # GH 11349
+        timedelta_series = Series([NaT, Timedelta("1s")])
+        nat_series_dtype_timedelta = Series([NaT, NaT], dtype="timedelta64[ns]")
+        single_nat_dtype_timedelta = Series([NaT], dtype="timedelta64[ns]")
+
+        # subtraction
+        tm.assert_series_equal(timedelta_series - NaT, nat_series_dtype_timedelta)
+        tm.assert_series_equal(-NaT + timedelta_series, nat_series_dtype_timedelta)
+
+        tm.assert_series_equal(
+            timedelta_series - single_nat_dtype_timedelta, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            -single_nat_dtype_timedelta + timedelta_series, nat_series_dtype_timedelta
+        )
+
+        # addition
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + NaT, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            NaT + nat_series_dtype_timedelta, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + single_nat_dtype_timedelta,
+            nat_series_dtype_timedelta,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + nat_series_dtype_timedelta,
+            nat_series_dtype_timedelta,
+        )
+
+        tm.assert_series_equal(timedelta_series + NaT, nat_series_dtype_timedelta)
+        tm.assert_series_equal(NaT + timedelta_series, nat_series_dtype_timedelta)
+
+        tm.assert_series_equal(
+            timedelta_series + single_nat_dtype_timedelta, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + timedelta_series, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + NaT, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            NaT + nat_series_dtype_timedelta, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta + single_nat_dtype_timedelta,
+            nat_series_dtype_timedelta,
+        )
+        tm.assert_series_equal(
+            single_nat_dtype_timedelta + nat_series_dtype_timedelta,
+            nat_series_dtype_timedelta,
+        )
+
+        # multiplication
+        tm.assert_series_equal(
+            nat_series_dtype_timedelta * 1.0, nat_series_dtype_timedelta
+        )
+        tm.assert_series_equal(
+            1.0 * nat_series_dtype_timedelta, nat_series_dtype_timedelta
+        )
+
+        tm.assert_series_equal(timedelta_series * 1, timedelta_series)
+        tm.assert_series_equal(1 * timedelta_series, timedelta_series)
+
+        tm.assert_series_equal(timedelta_series * 1.5, Series([NaT, Timedelta("1.5s")]))
+        tm.assert_series_equal(1.5 * timedelta_series, Series([NaT, Timedelta("1.5s")]))
+
+        tm.assert_series_equal(timedelta_series * np.nan, nat_series_dtype_timedelta)
+        tm.assert_series_equal(np.nan * timedelta_series, nat_series_dtype_timedelta)
+
+        # division
+        tm.assert_series_equal(timedelta_series / 2, Series([NaT, Timedelta("0.5s")]))
+        tm.assert_series_equal(timedelta_series / 2.0, Series([NaT, Timedelta("0.5s")]))
+        tm.assert_series_equal(timedelta_series / np.nan, nat_series_dtype_timedelta)
+
+    # -------------------------------------------------------------
+    # Binary operations td64 arraylike and datetime-like
+
+    @pytest.mark.parametrize("cls", [Timestamp, datetime, np.datetime64])
+    def test_td64arr_add_sub_datetimelike_scalar(
+        self, cls, box_with_array, tz_naive_fixture
+    ):
+        # GH#11925, GH#29558, GH#23215
+        tz = tz_naive_fixture
+
+        dt_scalar = Timestamp("2012-01-01", tz=tz)
+        if cls is datetime:
+            ts = dt_scalar.to_pydatetime()
+        elif cls is np.datetime64:
+            if tz_naive_fixture is not None:
+                pytest.skip(f"{cls} doesn support {tz_naive_fixture}")
+            ts = dt_scalar.to_datetime64()
+        else:
+            ts = dt_scalar
+
+        tdi = timedelta_range("1 day", periods=3)
+        expected = pd.date_range("2012-01-02", periods=3, tz=tz)
+
+        tdarr = tm.box_expected(tdi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        tm.assert_equal(ts + tdarr, expected)
+        tm.assert_equal(tdarr + ts, expected)
+
+        expected2 = pd.date_range("2011-12-31", periods=3, freq="-1D", tz=tz)
+        expected2 = tm.box_expected(expected2, box_with_array)
+
+        tm.assert_equal(ts - tdarr, expected2)
+        tm.assert_equal(ts + (-tdarr), expected2)
+
+        msg = "cannot subtract a datelike"
+        with pytest.raises(TypeError, match=msg):
+            tdarr - ts
+
+    def test_td64arr_add_datetime64_nat(self, box_with_array):
+        # GH#23215
+        other = np.datetime64("NaT")
+
+        tdi = timedelta_range("1 day", periods=3)
+        expected = DatetimeIndex(["NaT", "NaT", "NaT"], dtype="M8[ns]")
+
+        tdser = tm.box_expected(tdi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        tm.assert_equal(tdser + other, expected)
+        tm.assert_equal(other + tdser, expected)
+
+    def test_td64arr_sub_dt64_array(self, box_with_array):
+        dti = pd.date_range("2016-01-01", periods=3)
+        tdi = TimedeltaIndex(["-1 Day"] * 3)
+        dtarr = dti.values
+        expected = DatetimeIndex(dtarr) - tdi
+
+        tdi = tm.box_expected(tdi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        msg = "cannot subtract a datelike from"
+        with pytest.raises(TypeError, match=msg):
+            tdi - dtarr
+
+        # TimedeltaIndex.__rsub__
+        result = dtarr - tdi
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_add_dt64_array(self, box_with_array):
+        dti = pd.date_range("2016-01-01", periods=3)
+        tdi = TimedeltaIndex(["-1 Day"] * 3)
+        dtarr = dti.values
+        expected = DatetimeIndex(dtarr) + tdi
+
+        tdi = tm.box_expected(tdi, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdi + dtarr
+        tm.assert_equal(result, expected)
+        result = dtarr + tdi
+        tm.assert_equal(result, expected)
+
+    # ------------------------------------------------------------------
+    # Invalid __add__/__sub__ operations
+
+    @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "h"])
+    @pytest.mark.parametrize("tdi_freq", [None, "h"])
+    def test_td64arr_sub_periodlike(
+        self, box_with_array, box_with_array2, tdi_freq, pi_freq
+    ):
+        # GH#20049 subtracting PeriodIndex should raise TypeError
+        tdi = TimedeltaIndex(["1 hours", "2 hours"], freq=tdi_freq)
+        dti = Timestamp("2018-03-07 17:16:40") + tdi
+        pi = dti.to_period(pi_freq)
+        per = pi[0]
+
+        tdi = tm.box_expected(tdi, box_with_array)
+        pi = tm.box_expected(pi, box_with_array2)
+        msg = "cannot subtract|unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            tdi - pi
+
+        # GH#13078 subtraction of Period scalar not supported
+        with pytest.raises(TypeError, match=msg):
+            tdi - per
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            # GH#12624 for str case
+            "a",
+            # GH#19123
+            1,
+            1.5,
+            np.array(2),
+        ],
+    )
+    def test_td64arr_addsub_numeric_scalar_invalid(self, box_with_array, other):
+        # vector-like others are tested in test_td64arr_add_sub_numeric_arr_invalid
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        tdarr = tm.box_expected(tdser, box_with_array)
+
+        assert_invalid_addsub_type(tdarr, other)
+
+    @pytest.mark.parametrize(
+        "vec",
+        [
+            np.array([1, 2, 3]),
+            Index([1, 2, 3]),
+            Series([1, 2, 3]),
+            DataFrame([[1, 2, 3]]),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_td64arr_addsub_numeric_arr_invalid(
+        self, box_with_array, vec, any_real_numpy_dtype
+    ):
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        tdarr = tm.box_expected(tdser, box_with_array)
+
+        vector = vec.astype(any_real_numpy_dtype)
+        assert_invalid_addsub_type(tdarr, vector)
+
+    def test_td64arr_add_sub_int(self, box_with_array, one):
+        # Variants of `one` for #19012, deprecated GH#22535
+        rng = timedelta_range("1 days 09:00:00", freq="h", periods=10)
+        tdarr = tm.box_expected(rng, box_with_array)
+
+        msg = "Addition/subtraction of integers"
+        assert_invalid_addsub_type(tdarr, one, msg)
+
+        # TODO: get inplace ops into assert_invalid_addsub_type
+        with pytest.raises(TypeError, match=msg):
+            tdarr += one
+        with pytest.raises(TypeError, match=msg):
+            tdarr -= one
+
+    def test_td64arr_add_sub_integer_array(self, box_with_array):
+        # GH#19959, deprecated GH#22535
+        # GH#22696 for DataFrame case, check that we don't dispatch to numpy
+        #  implementation, which treats int64 as m8[ns]
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = timedelta_range("1 days 09:00:00", freq="h", periods=3)
+        tdarr = tm.box_expected(rng, box)
+        other = tm.box_expected([4, 3, 2], xbox)
+
+        msg = "Addition/subtraction of integers and integer-arrays"
+        assert_invalid_addsub_type(tdarr, other, msg)
+
+    def test_td64arr_addsub_integer_array_no_freq(self, box_with_array):
+        # GH#19959
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        tdi = TimedeltaIndex(["1 Day", "NaT", "3 Hours"])
+        tdarr = tm.box_expected(tdi, box)
+        other = tm.box_expected([14, -1, 16], xbox)
+
+        msg = "Addition/subtraction of integers"
+        assert_invalid_addsub_type(tdarr, other, msg)
+
+    # ------------------------------------------------------------------
+    # Operations with timedelta-like others
+
+    def test_td64arr_add_sub_td64_array(self, box_with_array):
+        box = box_with_array
+        dti = pd.date_range("2016-01-01", periods=3)
+        tdi = dti - dti.shift(1)
+        tdarr = tdi.values
+
+        expected = 2 * tdi
+        tdi = tm.box_expected(tdi, box)
+        expected = tm.box_expected(expected, box)
+
+        result = tdi + tdarr
+        tm.assert_equal(result, expected)
+        result = tdarr + tdi
+        tm.assert_equal(result, expected)
+
+        expected_sub = 0 * tdi
+        result = tdi - tdarr
+        tm.assert_equal(result, expected_sub)
+        result = tdarr - tdi
+        tm.assert_equal(result, expected_sub)
+
+    def test_td64arr_add_sub_tdi(self, box_with_array, names):
+        # GH#17250 make sure result dtype is correct
+        # GH#19043 make sure names are propagated correctly
+        box = box_with_array
+        exname = get_expected_name(box, names)
+
+        tdi = TimedeltaIndex(["0 days", "1 day"], name=names[1])
+        tdi = np.array(tdi) if box in [tm.to_array, pd.array] else tdi
+        ser = Series([Timedelta(hours=3), Timedelta(hours=4)], name=names[0])
+        expected = Series([Timedelta(hours=3), Timedelta(days=1, hours=4)], name=exname)
+
+        ser = tm.box_expected(ser, box)
+        expected = tm.box_expected(expected, box)
+
+        result = tdi + ser
+        tm.assert_equal(result, expected)
+        assert_dtype(result, "timedelta64[ns]")
+
+        result = ser + tdi
+        tm.assert_equal(result, expected)
+        assert_dtype(result, "timedelta64[ns]")
+
+        expected = Series(
+            [Timedelta(hours=-3), Timedelta(days=1, hours=-4)], name=exname
+        )
+        expected = tm.box_expected(expected, box)
+
+        result = tdi - ser
+        tm.assert_equal(result, expected)
+        assert_dtype(result, "timedelta64[ns]")
+
+        result = ser - tdi
+        tm.assert_equal(result, -expected)
+        assert_dtype(result, "timedelta64[ns]")
+
+    @pytest.mark.parametrize("tdnat", [np.timedelta64("NaT"), NaT])
+    def test_td64arr_add_sub_td64_nat(self, box_with_array, tdnat):
+        # GH#18808, GH#23320 special handling for timedelta64("NaT")
+        box = box_with_array
+        tdi = TimedeltaIndex([NaT, Timedelta("1s")])
+        expected = TimedeltaIndex(["NaT"] * 2)
+
+        obj = tm.box_expected(tdi, box)
+        expected = tm.box_expected(expected, box)
+
+        result = obj + tdnat
+        tm.assert_equal(result, expected)
+        result = tdnat + obj
+        tm.assert_equal(result, expected)
+        result = obj - tdnat
+        tm.assert_equal(result, expected)
+        result = tdnat - obj
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_add_timedeltalike(self, two_hours, box_with_array):
+        # only test adding/sub offsets as + is now numeric
+        # GH#10699 for Tick cases
+        box = box_with_array
+        rng = timedelta_range("1 days", "10 days")
+        expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D")
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, box)
+
+        result = rng + two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours + rng
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_sub_timedeltalike(self, two_hours, box_with_array):
+        # only test adding/sub offsets as - is now numeric
+        # GH#10699 for Tick cases
+        box = box_with_array
+        rng = timedelta_range("1 days", "10 days")
+        expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00")
+
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, box)
+
+        result = rng - two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours - rng
+        tm.assert_equal(result, -expected)
+
+    # ------------------------------------------------------------------
+    # __add__/__sub__ with DateOffsets and arrays of DateOffsets
+
+    def test_td64arr_add_sub_offset_index(self, names, box_with_array):
+        # GH#18849, GH#19744
+        box = box_with_array
+        exname = get_expected_name(box, names)
+
+        tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"], name=names[0])
+        other = Index([offsets.Hour(n=1), offsets.Minute(n=-2)], name=names[1])
+        other = np.array(other) if box in [tm.to_array, pd.array] else other
+
+        expected = TimedeltaIndex(
+            [tdi[n] + other[n] for n in range(len(tdi))], freq="infer", name=exname
+        )
+        expected_sub = TimedeltaIndex(
+            [tdi[n] - other[n] for n in range(len(tdi))], freq="infer", name=exname
+        )
+
+        tdi = tm.box_expected(tdi, box)
+        expected = tm.box_expected(expected, box).astype(object, copy=False)
+        expected_sub = tm.box_expected(expected_sub, box).astype(object, copy=False)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = tdi + other
+        tm.assert_equal(res, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res2 = other + tdi
+        tm.assert_equal(res2, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res_sub = tdi - other
+        tm.assert_equal(res_sub, expected_sub)
+
+    def test_td64arr_add_sub_offset_array(self, box_with_array):
+        # GH#18849, GH#18824
+        box = box_with_array
+        tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"])
+        other = np.array([offsets.Hour(n=1), offsets.Minute(n=-2)])
+
+        expected = TimedeltaIndex(
+            [tdi[n] + other[n] for n in range(len(tdi))], freq="infer"
+        )
+        expected_sub = TimedeltaIndex(
+            [tdi[n] - other[n] for n in range(len(tdi))], freq="infer"
+        )
+
+        tdi = tm.box_expected(tdi, box)
+        expected = tm.box_expected(expected, box).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = tdi + other
+        tm.assert_equal(res, expected)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res2 = other + tdi
+        tm.assert_equal(res2, expected)
+
+        expected_sub = tm.box_expected(expected_sub, box_with_array).astype(object)
+        with tm.assert_produces_warning(PerformanceWarning):
+            res_sub = tdi - other
+        tm.assert_equal(res_sub, expected_sub)
+
+    def test_td64arr_with_offset_series(self, names, box_with_array):
+        # GH#18849
+        box = box_with_array
+        box2 = Series if box in [Index, tm.to_array, pd.array] else box
+        exname = get_expected_name(box, names)
+
+        tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"], name=names[0])
+        other = Series([offsets.Hour(n=1), offsets.Minute(n=-2)], name=names[1])
+
+        expected_add = Series(
+            [tdi[n] + other[n] for n in range(len(tdi))], name=exname, dtype=object
+        )
+        obj = tm.box_expected(tdi, box)
+        expected_add = tm.box_expected(expected_add, box2).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res = obj + other
+        tm.assert_equal(res, expected_add)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res2 = other + obj
+        tm.assert_equal(res2, expected_add)
+
+        expected_sub = Series(
+            [tdi[n] - other[n] for n in range(len(tdi))], name=exname, dtype=object
+        )
+        expected_sub = tm.box_expected(expected_sub, box2).astype(object)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            res3 = obj - other
+        tm.assert_equal(res3, expected_sub)
+
+    @pytest.mark.parametrize("obox", [np.array, Index, Series])
+    def test_td64arr_addsub_anchored_offset_arraylike(self, obox, box_with_array):
+        # GH#18824
+        tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"])
+        tdi = tm.box_expected(tdi, box_with_array)
+
+        anchored = obox([offsets.MonthEnd(), offsets.Day(n=2)])
+
+        # addition/subtraction ops with anchored offsets should issue
+        # a PerformanceWarning and _then_ raise a TypeError.
+        msg = "has incorrect type|cannot add the type MonthEnd"
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                tdi + anchored
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                anchored + tdi
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                tdi - anchored
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                anchored - tdi
+
+    # ------------------------------------------------------------------
+    # Unsorted
+
+    def test_td64arr_add_sub_object_array(self, box_with_array):
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        tdi = timedelta_range("1 day", periods=3, freq="D")
+        tdarr = tm.box_expected(tdi, box)
+
+        other = np.array([Timedelta(days=1), offsets.Day(2), Timestamp("2000-01-04")])
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = tdarr + other
+
+        expected = Index(
+            [Timedelta(days=2), Timedelta(days=4), Timestamp("2000-01-07")]
+        )
+        expected = tm.box_expected(expected, xbox).astype(object)
+        tm.assert_equal(result, expected)
+
+        msg = "unsupported operand type|cannot subtract a datelike"
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(PerformanceWarning):
+                tdarr - other
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            result = other - tdarr
+
+        expected = Index([Timedelta(0), Timedelta(0), Timestamp("2000-01-01")])
+        expected = tm.box_expected(expected, xbox).astype(object)
+        tm.assert_equal(result, expected)
+
+
+class TestTimedeltaArraylikeMulDivOps:
+    # Tests for timedelta64[ns]
+    # __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__
+
+    # ------------------------------------------------------------------
+    # Multiplication
+    # organized with scalar others first, then array-like
+
+    def test_td64arr_mul_int(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+
+        result = idx * 1
+        tm.assert_equal(result, idx)
+
+        result = 1 * idx
+        tm.assert_equal(result, idx)
+
+    def test_td64arr_mul_tdlike_scalar_raises(self, two_hours, box_with_array):
+        rng = timedelta_range("1 days", "10 days", name="foo")
+        rng = tm.box_expected(rng, box_with_array)
+        msg = "argument must be an integer|cannot use operands with types dtype"
+        with pytest.raises(TypeError, match=msg):
+            rng * two_hours
+
+    def test_tdi_mul_int_array_zerodim(self, box_with_array):
+        rng5 = np.arange(5, dtype="int64")
+        idx = TimedeltaIndex(rng5)
+        expected = TimedeltaIndex(rng5 * 5)
+
+        idx = tm.box_expected(idx, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = idx * np.array(5, dtype="int64")
+        tm.assert_equal(result, expected)
+
+    def test_tdi_mul_int_array(self, box_with_array):
+        rng5 = np.arange(5, dtype="int64")
+        idx = TimedeltaIndex(rng5)
+        expected = TimedeltaIndex(rng5**2)
+
+        idx = tm.box_expected(idx, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = idx * rng5
+        tm.assert_equal(result, expected)
+
+    def test_tdi_mul_int_series(self, box_with_array):
+        box = box_with_array
+        xbox = Series if box in [Index, tm.to_array, pd.array] else box
+
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        expected = TimedeltaIndex(np.arange(5, dtype="int64") ** 2)
+
+        idx = tm.box_expected(idx, box)
+        expected = tm.box_expected(expected, xbox)
+
+        result = idx * Series(np.arange(5, dtype="int64"))
+        tm.assert_equal(result, expected)
+
+    def test_tdi_mul_float_series(self, box_with_array):
+        box = box_with_array
+        xbox = Series if box in [Index, tm.to_array, pd.array] else box
+
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box)
+
+        rng5f = np.arange(5, dtype="float64")
+        expected = TimedeltaIndex(rng5f * (rng5f + 1.0))
+        expected = tm.box_expected(expected, xbox)
+
+        result = idx * Series(rng5f + 1.0)
+        tm.assert_equal(result, expected)
+
+    # TODO: Put Series/DataFrame in others?
+    @pytest.mark.parametrize(
+        "other",
+        [
+            np.arange(1, 11),
+            Index(np.arange(1, 11), np.int64),
+            Index(range(1, 11), np.uint64),
+            Index(range(1, 11), np.float64),
+            pd.RangeIndex(1, 11),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_tdi_rmul_arraylike(self, other, box_with_array):
+        box = box_with_array
+
+        tdi = TimedeltaIndex(["1 Day"] * 10)
+        expected = timedelta_range("1 days", "10 days")._with_freq(None)
+
+        tdi = tm.box_expected(tdi, box)
+        xbox = get_upcast_box(tdi, other)
+
+        expected = tm.box_expected(expected, xbox)
+
+        result = other * tdi
+        tm.assert_equal(result, expected)
+        commute = tdi * other
+        tm.assert_equal(commute, expected)
+
+    # ------------------------------------------------------------------
+    # __div__, __rdiv__
+
+    def test_td64arr_div_nat_invalid(self, box_with_array):
+        # don't allow division by NaT (maybe could in the future)
+        rng = timedelta_range("1 days", "10 days", name="foo")
+        rng = tm.box_expected(rng, box_with_array)
+
+        with pytest.raises(TypeError, match="unsupported operand type"):
+            rng / NaT
+        with pytest.raises(TypeError, match="Cannot divide NaTType by"):
+            NaT / rng
+
+        dt64nat = np.datetime64("NaT", "ns")
+        msg = "|".join(
+            [
+                # 'divide' on npdev as of 2021-12-18
+                "ufunc '(true_divide|divide)' cannot use operands",
+                "cannot perform __r?truediv__",
+                "Cannot divide datetime64 by TimedeltaArray",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            rng / dt64nat
+        with pytest.raises(TypeError, match=msg):
+            dt64nat / rng
+
+    def test_td64arr_div_td64nat(self, box_with_array):
+        # GH#23829
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = timedelta_range("1 days", "10 days")
+        rng = tm.box_expected(rng, box)
+
+        other = np.timedelta64("NaT")
+
+        expected = np.array([np.nan] * 10)
+        expected = tm.box_expected(expected, xbox)
+
+        result = rng / other
+        tm.assert_equal(result, expected)
+
+        result = other / rng
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_div_int(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+
+        result = idx / 1
+        tm.assert_equal(result, idx)
+
+        with pytest.raises(TypeError, match="Cannot divide"):
+            # GH#23829
+            1 / idx
+
+    def test_td64arr_div_tdlike_scalar(self, two_hours, box_with_array):
+        # GH#20088, GH#22163 ensure DataFrame returns correct dtype
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = timedelta_range("1 days", "10 days", name="foo")
+        expected = Index((np.arange(10) + 1) * 12, dtype=np.float64, name="foo")
+
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, xbox)
+
+        result = rng / two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours / rng
+        expected = 1 / expected
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("m", [1, 3, 10])
+    @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
+    def test_td64arr_div_td64_scalar(self, m, unit, box_with_array):
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        ser = Series([Timedelta(days=59)] * 3)
+        ser[2] = np.nan
+        flat = ser
+        ser = tm.box_expected(ser, box)
+
+        # op
+        expected = Series([x / np.timedelta64(m, unit) for x in flat])
+        expected = tm.box_expected(expected, xbox)
+        result = ser / np.timedelta64(m, unit)
+        tm.assert_equal(result, expected)
+
+        # reverse op
+        expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in flat])
+        expected = tm.box_expected(expected, xbox)
+        result = np.timedelta64(m, unit) / ser
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_div_tdlike_scalar_with_nat(self, two_hours, box_with_array):
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo")
+        expected = Index([12, np.nan, 24], dtype=np.float64, name="foo")
+
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, xbox)
+
+        result = rng / two_hours
+        tm.assert_equal(result, expected)
+
+        result = two_hours / rng
+        expected = 1 / expected
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_div_td64_ndarray(self, box_with_array):
+        # GH#22631
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        rng = TimedeltaIndex(["1 days", NaT, "2 days"])
+        expected = Index([12, np.nan, 24], dtype=np.float64)
+
+        rng = tm.box_expected(rng, box)
+        expected = tm.box_expected(expected, xbox)
+
+        other = np.array([2, 4, 2], dtype="m8[h]")
+        result = rng / other
+        tm.assert_equal(result, expected)
+
+        result = rng / tm.box_expected(other, box)
+        tm.assert_equal(result, expected)
+
+        result = rng / other.astype(object)
+        tm.assert_equal(result, expected.astype(object))
+
+        result = rng / list(other)
+        tm.assert_equal(result, expected)
+
+        # reversed op
+        expected = 1 / expected
+        result = other / rng
+        tm.assert_equal(result, expected)
+
+        result = tm.box_expected(other, box) / rng
+        tm.assert_equal(result, expected)
+
+        result = other.astype(object) / rng
+        tm.assert_equal(result, expected)
+
+        result = list(other) / rng
+        tm.assert_equal(result, expected)
+
+    def test_tdarr_div_length_mismatch(self, box_with_array):
+        rng = TimedeltaIndex(["1 days", NaT, "2 days"])
+        mismatched = [1, 2, 3, 4]
+
+        rng = tm.box_expected(rng, box_with_array)
+        msg = "Cannot divide vectors|Unable to coerce to Series"
+        for obj in [mismatched, mismatched[:2]]:
+            # one shorter, one longer
+            for other in [obj, np.array(obj), Index(obj)]:
+                with pytest.raises(ValueError, match=msg):
+                    rng / other
+                with pytest.raises(ValueError, match=msg):
+                    other / rng
+
+    def test_td64_div_object_mixed_result(self, box_with_array):
+        # Case where we having a NaT in the result inseat of timedelta64("NaT")
+        #  is misleading
+        orig = timedelta_range("1 Day", periods=3).insert(1, NaT)
+        tdi = tm.box_expected(orig, box_with_array, transpose=False)
+
+        other = np.array([orig[0], 1.5, 2.0, orig[2]], dtype=object)
+        other = tm.box_expected(other, box_with_array, transpose=False)
+
+        res = tdi / other
+
+        expected = Index([1.0, np.timedelta64("NaT", "ns"), orig[0], 1.5], dtype=object)
+        expected = tm.box_expected(expected, box_with_array, transpose=False)
+        if isinstance(expected, NumpyExtensionArray):
+            expected = expected.to_numpy()
+        tm.assert_equal(res, expected)
+        if box_with_array is DataFrame:
+            # We have a np.timedelta64(NaT), not pd.NaT
+            assert isinstance(res.iloc[1, 0], np.timedelta64)
+
+        res = tdi // other
+
+        expected = Index([1, np.timedelta64("NaT", "ns"), orig[0], 1], dtype=object)
+        expected = tm.box_expected(expected, box_with_array, transpose=False)
+        if isinstance(expected, NumpyExtensionArray):
+            expected = expected.to_numpy()
+        tm.assert_equal(res, expected)
+        if box_with_array is DataFrame:
+            # We have a np.timedelta64(NaT), not pd.NaT
+            assert isinstance(res.iloc[1, 0], np.timedelta64)
+
+    # ------------------------------------------------------------------
+    # __floordiv__, __rfloordiv__
+
+    def test_td64arr_floordiv_td64arr_with_nat(
+        self, box_with_array, using_array_manager
+    ):
+        # GH#35529
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+
+        left = Series([1000, 222330, 30], dtype="timedelta64[ns]")
+        right = Series([1000, 222330, None], dtype="timedelta64[ns]")
+
+        left = tm.box_expected(left, box)
+        right = tm.box_expected(right, box)
+
+        expected = np.array([1.0, 1.0, np.nan], dtype=np.float64)
+        expected = tm.box_expected(expected, xbox)
+        if box is DataFrame and using_array_manager:
+            # INFO(ArrayManager) floordiv returns integer, and ArrayManager
+            # performs ops column-wise and thus preserves int64 dtype for
+            # columns without missing values
+            expected[[0, 1]] = expected[[0, 1]].astype("int64")
+
+        with tm.maybe_produces_warning(
+            RuntimeWarning, box is pd.array, check_stacklevel=False
+        ):
+            result = left // right
+
+        tm.assert_equal(result, expected)
+
+        # case that goes through __rfloordiv__ with arraylike
+        with tm.maybe_produces_warning(
+            RuntimeWarning, box is pd.array, check_stacklevel=False
+        ):
+            result = np.asarray(left) // right
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning")
+    def test_td64arr_floordiv_tdscalar(self, box_with_array, scalar_td):
+        # GH#18831, GH#19125
+        box = box_with_array
+        xbox = np.ndarray if box is pd.array else box
+        td = Timedelta("5m3s")  # i.e. (scalar_td - 1sec) / 2
+
+        td1 = Series([td, td, NaT], dtype="m8[ns]")
+        td1 = tm.box_expected(td1, box, transpose=False)
+
+        expected = Series([0, 0, np.nan])
+        expected = tm.box_expected(expected, xbox, transpose=False)
+
+        result = td1 // scalar_td
+        tm.assert_equal(result, expected)
+
+        # Reversed op
+        expected = Series([2, 2, np.nan])
+        expected = tm.box_expected(expected, xbox, transpose=False)
+
+        result = scalar_td // td1
+        tm.assert_equal(result, expected)
+
+        # same thing buts let's be explicit about calling __rfloordiv__
+        result = td1.__rfloordiv__(scalar_td)
+        tm.assert_equal(result, expected)
+
+    def test_td64arr_floordiv_int(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+        result = idx // 1
+        tm.assert_equal(result, idx)
+
+        pattern = "floor_divide cannot use operands|Cannot divide int by Timedelta*"
+        with pytest.raises(TypeError, match=pattern):
+            1 // idx
+
+    # ------------------------------------------------------------------
+    # mod, divmod
+    # TODO: operations with timedelta-like arrays, numeric arrays,
+    #  reversed ops
+
+    def test_td64arr_mod_tdscalar(self, box_with_array, three_days):
+        tdi = timedelta_range("1 Day", "9 days")
+        tdarr = tm.box_expected(tdi, box_with_array)
+
+        expected = TimedeltaIndex(["1 Day", "2 Days", "0 Days"] * 3)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdarr % three_days
+        tm.assert_equal(result, expected)
+
+        warn = None
+        if box_with_array is DataFrame and isinstance(three_days, pd.DateOffset):
+            warn = PerformanceWarning
+            # TODO: making expected be object here a result of DataFrame.__divmod__
+            #  being defined in a naive way that does not dispatch to the underlying
+            #  array's __divmod__
+            expected = expected.astype(object)
+
+        with tm.assert_produces_warning(warn):
+            result = divmod(tdarr, three_days)
+
+        tm.assert_equal(result[1], expected)
+        tm.assert_equal(result[0], tdarr // three_days)
+
+    def test_td64arr_mod_int(self, box_with_array):
+        tdi = timedelta_range("1 ns", "10 ns", periods=10)
+        tdarr = tm.box_expected(tdi, box_with_array)
+
+        expected = TimedeltaIndex(["1 ns", "0 ns"] * 5)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdarr % 2
+        tm.assert_equal(result, expected)
+
+        msg = "Cannot divide int by"
+        with pytest.raises(TypeError, match=msg):
+            2 % tdarr
+
+        result = divmod(tdarr, 2)
+        tm.assert_equal(result[1], expected)
+        tm.assert_equal(result[0], tdarr // 2)
+
+    def test_td64arr_rmod_tdscalar(self, box_with_array, three_days):
+        tdi = timedelta_range("1 Day", "9 days")
+        tdarr = tm.box_expected(tdi, box_with_array)
+
+        expected = ["0 Days", "1 Day", "0 Days"] + ["3 Days"] * 6
+        expected = TimedeltaIndex(expected)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = three_days % tdarr
+        tm.assert_equal(result, expected)
+
+        result = divmod(three_days, tdarr)
+        tm.assert_equal(result[1], expected)
+        tm.assert_equal(result[0], three_days // tdarr)
+
+    # ------------------------------------------------------------------
+    # Operations with invalid others
+
+    def test_td64arr_mul_tdscalar_invalid(self, box_with_array, scalar_td):
+        td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
+        td1.iloc[2] = np.nan
+
+        td1 = tm.box_expected(td1, box_with_array)
+
+        # check that we are getting a TypeError
+        # with 'operate' (from core/ops.py) for the ops that are not
+        # defined
+        pattern = "operate|unsupported|cannot|not supported"
+        with pytest.raises(TypeError, match=pattern):
+            td1 * scalar_td
+        with pytest.raises(TypeError, match=pattern):
+            scalar_td * td1
+
+    def test_td64arr_mul_too_short_raises(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+        msg = "|".join(
+            [
+                "cannot use operands with types dtype",
+                "Cannot multiply with unequal lengths",
+                "Unable to coerce to Series",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            # length check before dtype check
+            idx * idx[:3]
+        with pytest.raises(ValueError, match=msg):
+            idx * np.array([1, 2])
+
+    def test_td64arr_mul_td64arr_raises(self, box_with_array):
+        idx = TimedeltaIndex(np.arange(5, dtype="int64"))
+        idx = tm.box_expected(idx, box_with_array)
+        msg = "cannot use operands with types dtype"
+        with pytest.raises(TypeError, match=msg):
+            idx * idx
+
+    # ------------------------------------------------------------------
+    # Operations with numeric others
+
+    def test_td64arr_mul_numeric_scalar(self, box_with_array, one):
+        # GH#4521
+        # divide/multiply by integers
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        expected = Series(["-59 Days", "-59 Days", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdser * (-one)
+        tm.assert_equal(result, expected)
+        result = (-one) * tdser
+        tm.assert_equal(result, expected)
+
+        expected = Series(["118 Days", "118 Days", "NaT"], dtype="timedelta64[ns]")
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdser * (2 * one)
+        tm.assert_equal(result, expected)
+        result = (2 * one) * tdser
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("two", [2, 2.0, np.array(2), np.array(2.0)])
+    def test_td64arr_div_numeric_scalar(self, box_with_array, two):
+        # GH#4521
+        # divide/multiply by integers
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        expected = Series(["29.5D", "29.5D", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdser / two
+        tm.assert_equal(result, expected)
+
+        with pytest.raises(TypeError, match="Cannot divide"):
+            two / tdser
+
+    @pytest.mark.parametrize("two", [2, 2.0, np.array(2), np.array(2.0)])
+    def test_td64arr_floordiv_numeric_scalar(self, box_with_array, two):
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        expected = Series(["29.5D", "29.5D", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        expected = tm.box_expected(expected, box_with_array)
+
+        result = tdser // two
+        tm.assert_equal(result, expected)
+
+        with pytest.raises(TypeError, match="Cannot divide"):
+            two // tdser
+
+    @pytest.mark.parametrize(
+        "vector",
+        [np.array([20, 30, 40]), Index([20, 30, 40]), Series([20, 30, 40])],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_td64arr_rmul_numeric_array(
+        self,
+        box_with_array,
+        vector,
+        any_real_numpy_dtype,
+    ):
+        # GH#4521
+        # divide/multiply by integers
+
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        vector = vector.astype(any_real_numpy_dtype)
+
+        expected = Series(["1180 Days", "1770 Days", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        xbox = get_upcast_box(tdser, vector)
+
+        expected = tm.box_expected(expected, xbox)
+
+        result = tdser * vector
+        tm.assert_equal(result, expected)
+
+        result = vector * tdser
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "vector",
+        [np.array([20, 30, 40]), Index([20, 30, 40]), Series([20, 30, 40])],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_td64arr_div_numeric_array(
+        self, box_with_array, vector, any_real_numpy_dtype
+    ):
+        # GH#4521
+        # divide/multiply by integers
+
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
+        vector = vector.astype(any_real_numpy_dtype)
+
+        expected = Series(["2.95D", "1D 23h 12m", "NaT"], dtype="timedelta64[ns]")
+
+        tdser = tm.box_expected(tdser, box_with_array)
+        xbox = get_upcast_box(tdser, vector)
+        expected = tm.box_expected(expected, xbox)
+
+        result = tdser / vector
+        tm.assert_equal(result, expected)
+
+        pattern = "|".join(
+            [
+                "true_divide'? cannot use operands",
+                "cannot perform __div__",
+                "cannot perform __truediv__",
+                "unsupported operand",
+                "Cannot divide",
+                "ufunc 'divide' cannot use operands with types",
+            ]
+        )
+        with pytest.raises(TypeError, match=pattern):
+            vector / tdser
+
+        result = tdser / vector.astype(object)
+        if box_with_array is DataFrame:
+            expected = [tdser.iloc[0, n] / vector[n] for n in range(len(vector))]
+            expected = tm.box_expected(expected, xbox).astype(object)
+            # We specifically expect timedelta64("NaT") here, not pd.NA
+            msg = "The 'downcast' keyword in fillna"
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                expected[2] = expected[2].fillna(
+                    np.timedelta64("NaT", "ns"), downcast=False
+                )
+        else:
+            expected = [tdser[n] / vector[n] for n in range(len(tdser))]
+            expected = [
+                x if x is not NaT else np.timedelta64("NaT", "ns") for x in expected
+            ]
+            if xbox is tm.to_array:
+                expected = tm.to_array(expected).astype(object)
+            else:
+                expected = xbox(expected, dtype=object)
+
+        tm.assert_equal(result, expected)
+
+        with pytest.raises(TypeError, match=pattern):
+            vector.astype(object) / tdser
+
+    def test_td64arr_mul_int_series(self, box_with_array, names):
+        # GH#19042 test for correct name attachment
+        box = box_with_array
+        exname = get_expected_name(box, names)
+
+        tdi = TimedeltaIndex(
+            ["0days", "1day", "2days", "3days", "4days"], name=names[0]
+        )
+        # TODO: Should we be parametrizing over types for `ser` too?
+        ser = Series([0, 1, 2, 3, 4], dtype=np.int64, name=names[1])
+
+        expected = Series(
+            ["0days", "1day", "4days", "9days", "16days"],
+            dtype="timedelta64[ns]",
+            name=exname,
+        )
+
+        tdi = tm.box_expected(tdi, box)
+        xbox = get_upcast_box(tdi, ser)
+
+        expected = tm.box_expected(expected, xbox)
+
+        result = ser * tdi
+        tm.assert_equal(result, expected)
+
+        result = tdi * ser
+        tm.assert_equal(result, expected)
+
+    # TODO: Should we be parametrizing over types for `ser` too?
+    def test_float_series_rdiv_td64arr(self, box_with_array, names):
+        # GH#19042 test for correct name attachment
+        box = box_with_array
+        tdi = TimedeltaIndex(
+            ["0days", "1day", "2days", "3days", "4days"], name=names[0]
+        )
+        ser = Series([1.5, 3, 4.5, 6, 7.5], dtype=np.float64, name=names[1])
+
+        xname = names[2] if box not in [tm.to_array, pd.array] else names[1]
+        expected = Series(
+            [tdi[n] / ser[n] for n in range(len(ser))],
+            dtype="timedelta64[ns]",
+            name=xname,
+        )
+
+        tdi = tm.box_expected(tdi, box)
+        xbox = get_upcast_box(tdi, ser)
+        expected = tm.box_expected(expected, xbox)
+
+        result = ser.__rtruediv__(tdi)
+        if box is DataFrame:
+            assert result is NotImplemented
+        else:
+            tm.assert_equal(result, expected)
+
+    def test_td64arr_all_nat_div_object_dtype_numeric(self, box_with_array):
+        # GH#39750 make sure we infer the result as td64
+        tdi = TimedeltaIndex([NaT, NaT])
+
+        left = tm.box_expected(tdi, box_with_array)
+        right = np.array([2, 2.0], dtype=object)
+
+        tdnat = np.timedelta64("NaT", "ns")
+        expected = Index([tdnat] * 2, dtype=object)
+        if box_with_array is not Index:
+            expected = tm.box_expected(expected, box_with_array).astype(object)
+            if box_with_array in [Series, DataFrame]:
+                msg = "The 'downcast' keyword in fillna is deprecated"
+                with tm.assert_produces_warning(FutureWarning, match=msg):
+                    expected = expected.fillna(tdnat, downcast=False)  # GH#18463
+
+        result = left / right
+        tm.assert_equal(result, expected)
+
+        result = left // right
+        tm.assert_equal(result, expected)
+
+
+class TestTimedelta64ArrayLikeArithmetic:
+    # Arithmetic tests for timedelta64[ns] vectors fully parametrized over
+    #  DataFrame/Series/TimedeltaIndex/TimedeltaArray.  Ideally all arithmetic
+    #  tests will eventually end up here.
+
+    def test_td64arr_pow_invalid(self, scalar_td, box_with_array):
+        td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
+        td1.iloc[2] = np.nan
+
+        td1 = tm.box_expected(td1, box_with_array)
+
+        # check that we are getting a TypeError
+        # with 'operate' (from core/ops.py) for the ops that are not
+        # defined
+        pattern = "operate|unsupported|cannot|not supported"
+        with pytest.raises(TypeError, match=pattern):
+            scalar_td**td1
+
+        with pytest.raises(TypeError, match=pattern):
+            td1**scalar_td
+
+
+def test_add_timestamp_to_timedelta():
+    # GH: 35897
+    timestamp = Timestamp("2021-01-01")
+    result = timestamp + timedelta_range("0s", "1s", periods=31)
+    expected = DatetimeIndex(
+        [
+            timestamp
+            + (
+                pd.to_timedelta("0.033333333s") * i
+                + pd.to_timedelta("0.000000001s") * divmod(i, 3)[0]
+            )
+            for i in range(31)
+        ]
+    )
+    tm.assert_index_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/io/excel/test_odf.py

@@ -0,0 +1,77 @@
+import functools
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+import pandas._testing as tm
+
+pytest.importorskip("odf")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture(autouse=True)
+def cd_and_set_engine(monkeypatch, datapath):
+    func = functools.partial(pd.read_excel, engine="odf")
+    monkeypatch.setattr(pd, "read_excel", func)
+    monkeypatch.chdir(datapath("io", "data", "excel"))
+
+
+def test_read_invalid_types_raises():
+    # the invalid_value_type.ods required manually editing
+    # of the included content.xml file
+    with pytest.raises(ValueError, match="Unrecognized type awesome_new_type"):
+        pd.read_excel("invalid_value_type.ods")
+
+
+def test_read_writer_table():
+    # Also test reading tables from an text OpenDocument file
+    # (.odt)
+    index = pd.Index(["Row 1", "Row 2", "Row 3"], name="Header")
+    expected = pd.DataFrame(
+        [[1, np.nan, 7], [2, np.nan, 8], [3, np.nan, 9]],
+        index=index,
+        columns=["Column 1", "Unnamed: 2", "Column 3"],
+    )
+
+    result = pd.read_excel("writertable.odt", sheet_name="Table1", index_col=0)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_newlines_between_xml_elements_table():
+    # GH#45598
+    expected = pd.DataFrame(
+        [[1.0, 4.0, 7], [np.nan, np.nan, 8], [3.0, 6.0, 9]],
+        columns=["Column 1", "Column 2", "Column 3"],
+    )
+
+    result = pd.read_excel("test_newlines.ods")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_unempty_cells():
+    expected = pd.DataFrame(
+        [1, np.nan, 3, np.nan, 5],
+        columns=["Column 1"],
+    )
+
+    result = pd.read_excel("test_unempty_cells.ods")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_cell_annotation():
+    expected = pd.DataFrame(
+        ["test", np.nan, "test 3"],
+        columns=["Column 1"],
+    )
+
+    result = pd.read_excel("test_cell_annotation.ods")
+
+    tm.assert_frame_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/io/excel/test_odswriter.py

@@ -0,0 +1,106 @@
+from datetime import (
+    date,
+    datetime,
+)
+import re
+
+import pytest
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+import pandas._testing as tm
+
+from pandas.io.excel import ExcelWriter
+
+odf = pytest.importorskip("odf")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture
+def ext():
+    return ".ods"
+
+
+def test_write_append_mode_raises(ext):
+    msg = "Append mode is not supported with odf!"
+
+    with tm.ensure_clean(ext) as f:
+        with pytest.raises(ValueError, match=msg):
+            ExcelWriter(f, engine="odf", mode="a")
+
+
+@pytest.mark.parametrize("engine_kwargs", [None, {"kwarg": 1}])
+def test_engine_kwargs(ext, engine_kwargs):
+    # GH 42286
+    # GH 43445
+    # test for error: OpenDocumentSpreadsheet does not accept any arguments
+    with tm.ensure_clean(ext) as f:
+        if engine_kwargs is not None:
+            error = re.escape(
+                "OpenDocumentSpreadsheet() got an unexpected keyword argument 'kwarg'"
+            )
+            with pytest.raises(
+                TypeError,
+                match=error,
+            ):
+                ExcelWriter(f, engine="odf", engine_kwargs=engine_kwargs)
+        else:
+            with ExcelWriter(f, engine="odf", engine_kwargs=engine_kwargs) as _:
+                pass
+
+
+def test_book_and_sheets_consistent(ext):
+    # GH#45687 - Ensure sheets is updated if user modifies book
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f) as writer:
+            assert writer.sheets == {}
+            table = odf.table.Table(name="test_name")
+            writer.book.spreadsheet.addElement(table)
+            assert writer.sheets == {"test_name": table}
+
+
+@pytest.mark.parametrize(
+    ["value", "cell_value_type", "cell_value_attribute", "cell_value"],
+    argvalues=[
+        (True, "boolean", "boolean-value", "true"),
+        ("test string", "string", "string-value", "test string"),
+        (1, "float", "value", "1"),
+        (1.5, "float", "value", "1.5"),
+        (
+            datetime(2010, 10, 10, 10, 10, 10),
+            "date",
+            "date-value",
+            "2010-10-10T10:10:10",
+        ),
+        (date(2010, 10, 10), "date", "date-value", "2010-10-10"),
+    ],
+)
+def test_cell_value_type(ext, value, cell_value_type, cell_value_attribute, cell_value):
+    # GH#54994 ODS: cell attributes should follow specification
+    # http://docs.oasis-open.org/office/v1.2/os/OpenDocument-v1.2-os-part1.html#refTable13
+    from odf.namespaces import OFFICENS
+    from odf.table import (
+        TableCell,
+        TableRow,
+    )
+
+    table_cell_name = TableCell().qname
+
+    with tm.ensure_clean(ext) as f:
+        pd.DataFrame([[value]]).to_excel(f, header=False, index=False)
+
+        with pd.ExcelFile(f) as wb:
+            sheet = wb._reader.get_sheet_by_index(0)
+            sheet_rows = sheet.getElementsByType(TableRow)
+            sheet_cells = [
+                x
+                for x in sheet_rows[0].childNodes
+                if hasattr(x, "qname") and x.qname == table_cell_name
+            ]
+
+            cell = sheet_cells[0]
+            assert cell.attributes.get((OFFICENS, "value-type")) == cell_value_type
+            assert cell.attributes.get((OFFICENS, cell_value_attribute)) == cell_value

env-llmeval/lib/python3.10/site-packages/pandas/tests/io/excel/test_openpyxl.py

@@ -0,0 +1,432 @@
+import contextlib
+from pathlib import Path
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+from pandas import DataFrame
+import pandas._testing as tm
+
+from pandas.io.excel import (
+    ExcelWriter,
+    _OpenpyxlWriter,
+)
+from pandas.io.excel._openpyxl import OpenpyxlReader
+
+openpyxl = pytest.importorskip("openpyxl")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture
+def ext():
+    return ".xlsx"
+
+
+def test_to_excel_styleconverter():
+    from openpyxl import styles
+
+    hstyle = {
+        "font": {"color": "00FF0000", "bold": True},
+        "borders": {"top": "thin", "right": "thin", "bottom": "thin", "left": "thin"},
+        "alignment": {"horizontal": "center", "vertical": "top"},
+        "fill": {"patternType": "solid", "fgColor": {"rgb": "006666FF", "tint": 0.3}},
+        "number_format": {"format_code": "0.00"},
+        "protection": {"locked": True, "hidden": False},
+    }
+
+    font_color = styles.Color("00FF0000")
+    font = styles.Font(bold=True, color=font_color)
+    side = styles.Side(style=styles.borders.BORDER_THIN)
+    border = styles.Border(top=side, right=side, bottom=side, left=side)
+    alignment = styles.Alignment(horizontal="center", vertical="top")
+    fill_color = styles.Color(rgb="006666FF", tint=0.3)
+    fill = styles.PatternFill(patternType="solid", fgColor=fill_color)
+
+    number_format = "0.00"
+
+    protection = styles.Protection(locked=True, hidden=False)
+
+    kw = _OpenpyxlWriter._convert_to_style_kwargs(hstyle)
+    assert kw["font"] == font
+    assert kw["border"] == border
+    assert kw["alignment"] == alignment
+    assert kw["fill"] == fill
+    assert kw["number_format"] == number_format
+    assert kw["protection"] == protection
+
+
+def test_write_cells_merge_styled(ext):
+    from pandas.io.formats.excel import ExcelCell
+
+    sheet_name = "merge_styled"
+
+    sty_b1 = {"font": {"color": "00FF0000"}}
+    sty_a2 = {"font": {"color": "0000FF00"}}
+
+    initial_cells = [
+        ExcelCell(col=1, row=0, val=42, style=sty_b1),
+        ExcelCell(col=0, row=1, val=99, style=sty_a2),
+    ]
+
+    sty_merged = {"font": {"color": "000000FF", "bold": True}}
+    sty_kwargs = _OpenpyxlWriter._convert_to_style_kwargs(sty_merged)
+    openpyxl_sty_merged = sty_kwargs["font"]
+    merge_cells = [
+        ExcelCell(
+            col=0, row=0, val="pandas", mergestart=1, mergeend=1, style=sty_merged
+        )
+    ]
+
+    with tm.ensure_clean(ext) as path:
+        with _OpenpyxlWriter(path) as writer:
+            writer._write_cells(initial_cells, sheet_name=sheet_name)
+            writer._write_cells(merge_cells, sheet_name=sheet_name)
+
+            wks = writer.sheets[sheet_name]
+        xcell_b1 = wks["B1"]
+        xcell_a2 = wks["A2"]
+        assert xcell_b1.font == openpyxl_sty_merged
+        assert xcell_a2.font == openpyxl_sty_merged
+
+
+@pytest.mark.parametrize("iso_dates", [True, False])
+def test_engine_kwargs_write(ext, iso_dates):
+    # GH 42286 GH 43445
+    engine_kwargs = {"iso_dates": iso_dates}
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f, engine="openpyxl", engine_kwargs=engine_kwargs) as writer:
+            assert writer.book.iso_dates == iso_dates
+            # ExcelWriter won't allow us to close without writing something
+            DataFrame().to_excel(writer)
+
+
+def test_engine_kwargs_append_invalid(ext):
+    # GH 43445
+    # test whether an invalid engine kwargs actually raises
+    with tm.ensure_clean(ext) as f:
+        DataFrame(["hello", "world"]).to_excel(f)
+        with pytest.raises(
+            TypeError,
+            match=re.escape(
+                "load_workbook() got an unexpected keyword argument 'apple_banana'"
+            ),
+        ):
+            with ExcelWriter(
+                f, engine="openpyxl", mode="a", engine_kwargs={"apple_banana": "fruit"}
+            ) as writer:
+                # ExcelWriter needs us to write something to close properly
+                DataFrame(["good"]).to_excel(writer, sheet_name="Sheet2")
+
+
+@pytest.mark.parametrize("data_only, expected", [(True, 0), (False, "=1+1")])
+def test_engine_kwargs_append_data_only(ext, data_only, expected):
+    # GH 43445
+    # tests whether the data_only engine_kwarg actually works well for
+    # openpyxl's load_workbook
+    with tm.ensure_clean(ext) as f:
+        DataFrame(["=1+1"]).to_excel(f)
+        with ExcelWriter(
+            f, engine="openpyxl", mode="a", engine_kwargs={"data_only": data_only}
+        ) as writer:
+            assert writer.sheets["Sheet1"]["B2"].value == expected
+            # ExcelWriter needs us to writer something to close properly?
+            DataFrame().to_excel(writer, sheet_name="Sheet2")
+
+        # ensure that data_only also works for reading
+        #  and that formulas/values roundtrip
+        assert (
+            pd.read_excel(
+                f,
+                sheet_name="Sheet1",
+                engine="openpyxl",
+                engine_kwargs={"data_only": data_only},
+            ).iloc[0, 1]
+            == expected
+        )
+
+
+@pytest.mark.parametrize("kwarg_name", ["read_only", "data_only"])
+@pytest.mark.parametrize("kwarg_value", [True, False])
+def test_engine_kwargs_append_reader(datapath, ext, kwarg_name, kwarg_value):
+    # GH 55027
+    # test that `read_only` and `data_only` can be passed to
+    #  `openpyxl.reader.excel.load_workbook` via `engine_kwargs`
+    filename = datapath("io", "data", "excel", "test1" + ext)
+    with contextlib.closing(
+        OpenpyxlReader(filename, engine_kwargs={kwarg_name: kwarg_value})
+    ) as reader:
+        assert getattr(reader.book, kwarg_name) == kwarg_value
+
+
+@pytest.mark.parametrize(
+    "mode,expected", [("w", ["baz"]), ("a", ["foo", "bar", "baz"])]
+)
+def test_write_append_mode(ext, mode, expected):
+    df = DataFrame([1], columns=["baz"])
+
+    with tm.ensure_clean(ext) as f:
+        wb = openpyxl.Workbook()
+        wb.worksheets[0].title = "foo"
+        wb.worksheets[0]["A1"].value = "foo"
+        wb.create_sheet("bar")
+        wb.worksheets[1]["A1"].value = "bar"
+        wb.save(f)
+
+        with ExcelWriter(f, engine="openpyxl", mode=mode) as writer:
+            df.to_excel(writer, sheet_name="baz", index=False)
+
+        with contextlib.closing(openpyxl.load_workbook(f)) as wb2:
+            result = [sheet.title for sheet in wb2.worksheets]
+            assert result == expected
+
+            for index, cell_value in enumerate(expected):
+                assert wb2.worksheets[index]["A1"].value == cell_value
+
+
+@pytest.mark.parametrize(
+    "if_sheet_exists,num_sheets,expected",
+    [
+        ("new", 2, ["apple", "banana"]),
+        ("replace", 1, ["pear"]),
+        ("overlay", 1, ["pear", "banana"]),
+    ],
+)
+def test_if_sheet_exists_append_modes(ext, if_sheet_exists, num_sheets, expected):
+    # GH 40230
+    df1 = DataFrame({"fruit": ["apple", "banana"]})
+    df2 = DataFrame({"fruit": ["pear"]})
+
+    with tm.ensure_clean(ext) as f:
+        df1.to_excel(f, engine="openpyxl", sheet_name="foo", index=False)
+        with ExcelWriter(
+            f, engine="openpyxl", mode="a", if_sheet_exists=if_sheet_exists
+        ) as writer:
+            df2.to_excel(writer, sheet_name="foo", index=False)
+
+        with contextlib.closing(openpyxl.load_workbook(f)) as wb:
+            assert len(wb.sheetnames) == num_sheets
+            assert wb.sheetnames[0] == "foo"
+            result = pd.read_excel(wb, "foo", engine="openpyxl")
+            assert list(result["fruit"]) == expected
+            if len(wb.sheetnames) == 2:
+                result = pd.read_excel(wb, wb.sheetnames[1], engine="openpyxl")
+                tm.assert_frame_equal(result, df2)
+
+
+@pytest.mark.parametrize(
+    "startrow, startcol, greeting, goodbye",
+    [
+        (0, 0, ["poop", "world"], ["goodbye", "people"]),
+        (0, 1, ["hello", "world"], ["poop", "people"]),
+        (1, 0, ["hello", "poop"], ["goodbye", "people"]),
+        (1, 1, ["hello", "world"], ["goodbye", "poop"]),
+    ],
+)
+def test_append_overlay_startrow_startcol(ext, startrow, startcol, greeting, goodbye):
+    df1 = DataFrame({"greeting": ["hello", "world"], "goodbye": ["goodbye", "people"]})
+    df2 = DataFrame(["poop"])
+
+    with tm.ensure_clean(ext) as f:
+        df1.to_excel(f, engine="openpyxl", sheet_name="poo", index=False)
+        with ExcelWriter(
+            f, engine="openpyxl", mode="a", if_sheet_exists="overlay"
+        ) as writer:
+            # use startrow+1 because we don't have a header
+            df2.to_excel(
+                writer,
+                index=False,
+                header=False,
+                startrow=startrow + 1,
+                startcol=startcol,
+                sheet_name="poo",
+            )
+
+        result = pd.read_excel(f, sheet_name="poo", engine="openpyxl")
+        expected = DataFrame({"greeting": greeting, "goodbye": goodbye})
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "if_sheet_exists,msg",
+    [
+        (
+            "invalid",
+            "'invalid' is not valid for if_sheet_exists. Valid options "
+            "are 'error', 'new', 'replace' and 'overlay'.",
+        ),
+        (
+            "error",
+            "Sheet 'foo' already exists and if_sheet_exists is set to 'error'.",
+        ),
+        (
+            None,
+            "Sheet 'foo' already exists and if_sheet_exists is set to 'error'.",
+        ),
+    ],
+)
+def test_if_sheet_exists_raises(ext, if_sheet_exists, msg):
+    # GH 40230
+    df = DataFrame({"fruit": ["pear"]})
+    with tm.ensure_clean(ext) as f:
+        with pytest.raises(ValueError, match=re.escape(msg)):
+            df.to_excel(f, sheet_name="foo", engine="openpyxl")
+            with ExcelWriter(
+                f, engine="openpyxl", mode="a", if_sheet_exists=if_sheet_exists
+            ) as writer:
+                df.to_excel(writer, sheet_name="foo")
+
+
+def test_to_excel_with_openpyxl_engine(ext):
+    # GH 29854
+    with tm.ensure_clean(ext) as filename:
+        df1 = DataFrame({"A": np.linspace(1, 10, 10)})
+        df2 = DataFrame({"B": np.linspace(1, 20, 10)})
+        df = pd.concat([df1, df2], axis=1)
+        styled = df.style.map(
+            lambda val: f"color: {'red' if val < 0 else 'black'}"
+        ).highlight_max()
+
+        styled.to_excel(filename, engine="openpyxl")
+
+
+@pytest.mark.parametrize("read_only", [True, False])
+def test_read_workbook(datapath, ext, read_only):
+    # GH 39528
+    filename = datapath("io", "data", "excel", "test1" + ext)
+    with contextlib.closing(
+        openpyxl.load_workbook(filename, read_only=read_only)
+    ) as wb:
+        result = pd.read_excel(wb, engine="openpyxl")
+    expected = pd.read_excel(filename)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "header, expected_data",
+    [
+        (
+            0,
+            {
+                "Title": [np.nan, "A", 1, 2, 3],
+                "Unnamed: 1": [np.nan, "B", 4, 5, 6],
+                "Unnamed: 2": [np.nan, "C", 7, 8, 9],
+            },
+        ),
+        (2, {"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}),
+    ],
+)
+@pytest.mark.parametrize(
+    "filename", ["dimension_missing", "dimension_small", "dimension_large"]
+)
+# When read_only is None, use read_excel instead of a workbook
+@pytest.mark.parametrize("read_only", [True, False, None])
+def test_read_with_bad_dimension(
+    datapath, ext, header, expected_data, filename, read_only
+):
+    # GH 38956, 39001 - no/incorrect dimension information
+    path = datapath("io", "data", "excel", f"{filename}{ext}")
+    if read_only is None:
+        result = pd.read_excel(path, header=header)
+    else:
+        with contextlib.closing(
+            openpyxl.load_workbook(path, read_only=read_only)
+        ) as wb:
+            result = pd.read_excel(wb, engine="openpyxl", header=header)
+    expected = DataFrame(expected_data)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_append_mode_file(ext):
+    # GH 39576
+    df = DataFrame()
+
+    with tm.ensure_clean(ext) as f:
+        df.to_excel(f, engine="openpyxl")
+
+        with ExcelWriter(
+            f, mode="a", engine="openpyxl", if_sheet_exists="new"
+        ) as writer:
+            df.to_excel(writer)
+
+        # make sure that zip files are not concatenated by making sure that
+        # "docProps/app.xml" only occurs twice in the file
+        data = Path(f).read_bytes()
+        first = data.find(b"docProps/app.xml")
+        second = data.find(b"docProps/app.xml", first + 1)
+        third = data.find(b"docProps/app.xml", second + 1)
+        assert second != -1 and third == -1
+
+
+# When read_only is None, use read_excel instead of a workbook
+@pytest.mark.parametrize("read_only", [True, False, None])
+def test_read_with_empty_trailing_rows(datapath, ext, read_only):
+    # GH 39181
+    path = datapath("io", "data", "excel", f"empty_trailing_rows{ext}")
+    if read_only is None:
+        result = pd.read_excel(path)
+    else:
+        with contextlib.closing(
+            openpyxl.load_workbook(path, read_only=read_only)
+        ) as wb:
+            result = pd.read_excel(wb, engine="openpyxl")
+    expected = DataFrame(
+        {
+            "Title": [np.nan, "A", 1, 2, 3],
+            "Unnamed: 1": [np.nan, "B", 4, 5, 6],
+            "Unnamed: 2": [np.nan, "C", 7, 8, 9],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+# When read_only is None, use read_excel instead of a workbook
+@pytest.mark.parametrize("read_only", [True, False, None])
+def test_read_empty_with_blank_row(datapath, ext, read_only):
+    # GH 39547 - empty excel file with a row that has no data
+    path = datapath("io", "data", "excel", f"empty_with_blank_row{ext}")
+    if read_only is None:
+        result = pd.read_excel(path)
+    else:
+        with contextlib.closing(
+            openpyxl.load_workbook(path, read_only=read_only)
+        ) as wb:
+            result = pd.read_excel(wb, engine="openpyxl")
+    expected = DataFrame()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_book_and_sheets_consistent(ext):
+    # GH#45687 - Ensure sheets is updated if user modifies book
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f, engine="openpyxl") as writer:
+            assert writer.sheets == {}
+            sheet = writer.book.create_sheet("test_name", 0)
+            assert writer.sheets == {"test_name": sheet}
+
+
+def test_ints_spelled_with_decimals(datapath, ext):
+    # GH 46988 - openpyxl returns this sheet with floats
+    path = datapath("io", "data", "excel", f"ints_spelled_with_decimals{ext}")
+    result = pd.read_excel(path)
+    expected = DataFrame(range(2, 12), columns=[1])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_multiindex_header_no_index_names(datapath, ext):
+    # GH#47487
+    path = datapath("io", "data", "excel", f"multiindex_no_index_names{ext}")
+    result = pd.read_excel(path, index_col=[0, 1, 2], header=[0, 1, 2])
+    expected = DataFrame(
+        [[np.nan, "x", "x", "x"], ["x", np.nan, np.nan, np.nan]],
+        columns=pd.MultiIndex.from_tuples(
+            [("X", "Y", "A1"), ("X", "Y", "A2"), ("XX", "YY", "B1"), ("XX", "YY", "B2")]
+        ),
+        index=pd.MultiIndex.from_tuples([("A", "AA", "AAA"), ("A", "BB", "BBB")]),
+    )
+    tm.assert_frame_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/io/excel/test_readers.py

@@ -0,0 +1,1751 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    time,
+)
+from functools import partial
+from io import BytesIO
+import os
+from pathlib import Path
+import platform
+import re
+from urllib.error import URLError
+from zipfile import BadZipFile
+
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas.compat import is_platform_windows
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    read_csv,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    ArrowStringArray,
+    StringArray,
+)
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+read_ext_params = [".xls", ".xlsx", ".xlsm", ".xlsb", ".ods"]
+engine_params = [
+    # Add any engines to test here
+    # When defusedxml is installed it triggers deprecation warnings for
+    # xlrd and openpyxl, so catch those here
+    pytest.param(
+        "xlrd",
+        marks=[
+            td.skip_if_no("xlrd"),
+        ],
+    ),
+    pytest.param(
+        "openpyxl",
+        marks=[
+            td.skip_if_no("openpyxl"),
+        ],
+    ),
+    pytest.param(
+        None,
+        marks=[
+            td.skip_if_no("xlrd"),
+        ],
+    ),
+    pytest.param("pyxlsb", marks=td.skip_if_no("pyxlsb")),
+    pytest.param("odf", marks=td.skip_if_no("odf")),
+    pytest.param("calamine", marks=td.skip_if_no("python_calamine")),
+]
+
+
+def _is_valid_engine_ext_pair(engine, read_ext: str) -> bool:
+    """
+    Filter out invalid (engine, ext) pairs instead of skipping, as that
+    produces 500+ pytest.skips.
+    """
+    engine = engine.values[0]
+    if engine == "openpyxl" and read_ext == ".xls":
+        return False
+    if engine == "odf" and read_ext != ".ods":
+        return False
+    if read_ext == ".ods" and engine not in {"odf", "calamine"}:
+        return False
+    if engine == "pyxlsb" and read_ext != ".xlsb":
+        return False
+    if read_ext == ".xlsb" and engine not in {"pyxlsb", "calamine"}:
+        return False
+    if engine == "xlrd" and read_ext != ".xls":
+        return False
+    return True
+
+
+def _transfer_marks(engine, read_ext):
+    """
+    engine gives us a pytest.param object with some marks, read_ext is just
+    a string.  We need to generate a new pytest.param inheriting the marks.
+    """
+    values = engine.values + (read_ext,)
+    new_param = pytest.param(values, marks=engine.marks)
+    return new_param
+
+
+@pytest.fixture(
+    params=[
+        _transfer_marks(eng, ext)
+        for eng in engine_params
+        for ext in read_ext_params
+        if _is_valid_engine_ext_pair(eng, ext)
+    ],
+    ids=str,
+)
+def engine_and_read_ext(request):
+    """
+    Fixture for Excel reader engine and read_ext, only including valid pairs.
+    """
+    return request.param
+
+
+@pytest.fixture
+def engine(engine_and_read_ext):
+    engine, read_ext = engine_and_read_ext
+    return engine
+
+
+@pytest.fixture
+def read_ext(engine_and_read_ext):
+    engine, read_ext = engine_and_read_ext
+    return read_ext
+
+
+@pytest.fixture
+def df_ref(datapath):
+    """
+    Obtain the reference data from read_csv with the Python engine.
+    """
+    filepath = datapath("io", "data", "csv", "test1.csv")
+    df_ref = read_csv(filepath, index_col=0, parse_dates=True, engine="python")
+    return df_ref
+
+
+def get_exp_unit(read_ext: str, engine: str | None) -> str:
+    return "ns"
+
+
+def adjust_expected(expected: DataFrame, read_ext: str, engine: str) -> None:
+    expected.index.name = None
+    unit = get_exp_unit(read_ext, engine)
+    # error: "Index" has no attribute "as_unit"
+    expected.index = expected.index.as_unit(unit)  # type: ignore[attr-defined]
+
+
+def xfail_datetimes_with_pyxlsb(engine, request):
+    if engine == "pyxlsb":
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="Sheets containing datetimes not supported by pyxlsb"
+            )
+        )
+
+
+class TestReaders:
+    @pytest.fixture(autouse=True)
+    def cd_and_set_engine(self, engine, datapath, monkeypatch):
+        """
+        Change directory and set engine for read_excel calls.
+        """
+        func = partial(pd.read_excel, engine=engine)
+        monkeypatch.chdir(datapath("io", "data", "excel"))
+        monkeypatch.setattr(pd, "read_excel", func)
+
+    def test_engine_used(self, read_ext, engine, monkeypatch):
+        # GH 38884
+        def parser(self, *args, **kwargs):
+            return self.engine
+
+        monkeypatch.setattr(pd.ExcelFile, "parse", parser)
+
+        expected_defaults = {
+            "xlsx": "openpyxl",
+            "xlsm": "openpyxl",
+            "xlsb": "pyxlsb",
+            "xls": "xlrd",
+            "ods": "odf",
+        }
+
+        with open("test1" + read_ext, "rb") as f:
+            result = pd.read_excel(f)
+
+        if engine is not None:
+            expected = engine
+        else:
+            expected = expected_defaults[read_ext[1:]]
+        assert result == expected
+
+    def test_engine_kwargs(self, read_ext, engine):
+        # GH#52214
+        expected_defaults = {
+            "xlsx": {"foo": "abcd"},
+            "xlsm": {"foo": 123},
+            "xlsb": {"foo": "True"},
+            "xls": {"foo": True},
+            "ods": {"foo": "abcd"},
+        }
+
+        if engine in {"xlrd", "pyxlsb"}:
+            msg = re.escape(r"open_workbook() got an unexpected keyword argument 'foo'")
+        elif engine == "odf":
+            msg = re.escape(r"load() got an unexpected keyword argument 'foo'")
+        else:
+            msg = re.escape(r"load_workbook() got an unexpected keyword argument 'foo'")
+
+        if engine is not None:
+            with pytest.raises(TypeError, match=msg):
+                pd.read_excel(
+                    "test1" + read_ext,
+                    sheet_name="Sheet1",
+                    index_col=0,
+                    engine_kwargs=expected_defaults[read_ext[1:]],
+                )
+
+    def test_usecols_int(self, read_ext):
+        # usecols as int
+        msg = "Passing an integer for `usecols`"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel(
+                "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=3
+            )
+
+        # usecols as int
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel(
+                "test1" + read_ext,
+                sheet_name="Sheet2",
+                skiprows=[1],
+                index_col=0,
+                usecols=3,
+            )
+
+    def test_usecols_list(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref[["B", "C"]]
+        adjust_expected(expected, read_ext, engine)
+
+        df1 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=[0, 2, 3]
+        )
+        df2 = pd.read_excel(
+            "test1" + read_ext,
+            sheet_name="Sheet2",
+            skiprows=[1],
+            index_col=0,
+            usecols=[0, 2, 3],
+        )
+
+        # TODO add index to xls file)
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+    def test_usecols_str(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref[["A", "B", "C"]]
+        adjust_expected(expected, read_ext, engine)
+
+        df2 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A:D"
+        )
+        df3 = pd.read_excel(
+            "test1" + read_ext,
+            sheet_name="Sheet2",
+            skiprows=[1],
+            index_col=0,
+            usecols="A:D",
+        )
+
+        # TODO add index to xls, read xls ignores index name ?
+        tm.assert_frame_equal(df2, expected)
+        tm.assert_frame_equal(df3, expected)
+
+        expected = df_ref[["B", "C"]]
+        adjust_expected(expected, read_ext, engine)
+
+        df2 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,C,D"
+        )
+        df3 = pd.read_excel(
+            "test1" + read_ext,
+            sheet_name="Sheet2",
+            skiprows=[1],
+            index_col=0,
+            usecols="A,C,D",
+        )
+        # TODO add index to xls file
+        tm.assert_frame_equal(df2, expected)
+        tm.assert_frame_equal(df3, expected)
+
+        df2 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,C:D"
+        )
+        df3 = pd.read_excel(
+            "test1" + read_ext,
+            sheet_name="Sheet2",
+            skiprows=[1],
+            index_col=0,
+            usecols="A,C:D",
+        )
+        tm.assert_frame_equal(df2, expected)
+        tm.assert_frame_equal(df3, expected)
+
+    @pytest.mark.parametrize(
+        "usecols", [[0, 1, 3], [0, 3, 1], [1, 0, 3], [1, 3, 0], [3, 0, 1], [3, 1, 0]]
+    )
+    def test_usecols_diff_positional_int_columns_order(
+        self, request, engine, read_ext, usecols, df_ref
+    ):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref[["A", "C"]]
+        adjust_expected(expected, read_ext, engine)
+
+        result = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=usecols
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("usecols", [["B", "D"], ["D", "B"]])
+    def test_usecols_diff_positional_str_columns_order(self, read_ext, usecols, df_ref):
+        expected = df_ref[["B", "D"]]
+        expected.index = range(len(expected))
+
+        result = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", usecols=usecols)
+        tm.assert_frame_equal(result, expected)
+
+    def test_read_excel_without_slicing(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        result = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", index_col=0)
+        tm.assert_frame_equal(result, expected)
+
+    def test_usecols_excel_range_str(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref[["C", "D"]]
+        adjust_expected(expected, read_ext, engine)
+
+        result = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,D:E"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_usecols_excel_range_str_invalid(self, read_ext):
+        msg = "Invalid column name: E1"
+
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("test1" + read_ext, sheet_name="Sheet1", usecols="D:E1")
+
+    def test_index_col_label_error(self, read_ext):
+        msg = "list indices must be integers.*, not str"
+
+        with pytest.raises(TypeError, match=msg):
+            pd.read_excel(
+                "test1" + read_ext,
+                sheet_name="Sheet1",
+                index_col=["A"],
+                usecols=["A", "C"],
+            )
+
+    def test_index_col_str(self, read_ext):
+        # see gh-52716
+        result = pd.read_excel("test1" + read_ext, sheet_name="Sheet3", index_col="A")
+        expected = DataFrame(
+            columns=["B", "C", "D", "E", "F"], index=Index([], name="A")
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_index_col_empty(self, read_ext):
+        # see gh-9208
+        result = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet3", index_col=["A", "B", "C"]
+        )
+        expected = DataFrame(
+            columns=["D", "E", "F"],
+            index=MultiIndex(levels=[[]] * 3, codes=[[]] * 3, names=["A", "B", "C"]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("index_col", [None, 2])
+    def test_index_col_with_unnamed(self, read_ext, index_col):
+        # see gh-18792
+        result = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet4", index_col=index_col
+        )
+        expected = DataFrame(
+            [["i1", "a", "x"], ["i2", "b", "y"]], columns=["Unnamed: 0", "col1", "col2"]
+        )
+        if index_col:
+            expected = expected.set_index(expected.columns[index_col])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_usecols_pass_non_existent_column(self, read_ext):
+        msg = (
+            "Usecols do not match columns, "
+            "columns expected but not found: "
+            r"\['E'\]"
+        )
+
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("test1" + read_ext, usecols=["E"])
+
+    def test_usecols_wrong_type(self, read_ext):
+        msg = (
+            "'usecols' must either be list-like of "
+            "all strings, all unicode, all integers or a callable."
+        )
+
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("test1" + read_ext, usecols=["E1", 0])
+
+    def test_excel_stop_iterator(self, read_ext):
+        parsed = pd.read_excel("test2" + read_ext, sheet_name="Sheet1")
+        expected = DataFrame([["aaaa", "bbbbb"]], columns=["Test", "Test1"])
+        tm.assert_frame_equal(parsed, expected)
+
+    def test_excel_cell_error_na(self, request, engine, read_ext):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        # https://github.com/tafia/calamine/issues/355
+        if engine == "calamine" and read_ext == ".ods":
+            request.applymarker(
+                pytest.mark.xfail(reason="Calamine can't extract error from ods files")
+            )
+
+        parsed = pd.read_excel("test3" + read_ext, sheet_name="Sheet1")
+        expected = DataFrame([[np.nan]], columns=["Test"])
+        tm.assert_frame_equal(parsed, expected)
+
+    def test_excel_table(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        df1 = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", index_col=0)
+        df2 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet2", skiprows=[1], index_col=0
+        )
+        # TODO add index to file
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+        df3 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, skipfooter=1
+        )
+        tm.assert_frame_equal(df3, df1.iloc[:-1])
+
+    def test_reader_special_dtypes(self, request, engine, read_ext):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        unit = get_exp_unit(read_ext, engine)
+        expected = DataFrame.from_dict(
+            {
+                "IntCol": [1, 2, -3, 4, 0],
+                "FloatCol": [1.25, 2.25, 1.83, 1.92, 0.0000000005],
+                "BoolCol": [True, False, True, True, False],
+                "StrCol": [1, 2, 3, 4, 5],
+                "Str2Col": ["a", 3, "c", "d", "e"],
+                "DateCol": Index(
+                    [
+                        datetime(2013, 10, 30),
+                        datetime(2013, 10, 31),
+                        datetime(1905, 1, 1),
+                        datetime(2013, 12, 14),
+                        datetime(2015, 3, 14),
+                    ],
+                    dtype=f"M8[{unit}]",
+                ),
+            },
+        )
+        basename = "test_types"
+
+        # should read in correctly and infer types
+        actual = pd.read_excel(basename + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, expected)
+
+        # if not coercing number, then int comes in as float
+        float_expected = expected.copy()
+        float_expected.loc[float_expected.index[1], "Str2Col"] = 3.0
+        actual = pd.read_excel(basename + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, float_expected)
+
+        # check setting Index (assuming xls and xlsx are the same here)
+        for icol, name in enumerate(expected.columns):
+            actual = pd.read_excel(
+                basename + read_ext, sheet_name="Sheet1", index_col=icol
+            )
+            exp = expected.set_index(name)
+            tm.assert_frame_equal(actual, exp)
+
+        expected["StrCol"] = expected["StrCol"].apply(str)
+        actual = pd.read_excel(
+            basename + read_ext, sheet_name="Sheet1", converters={"StrCol": str}
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    # GH8212 - support for converters and missing values
+    def test_reader_converters(self, read_ext):
+        basename = "test_converters"
+
+        expected = DataFrame.from_dict(
+            {
+                "IntCol": [1, 2, -3, -1000, 0],
+                "FloatCol": [12.5, np.nan, 18.3, 19.2, 0.000000005],
+                "BoolCol": ["Found", "Found", "Found", "Not found", "Found"],
+                "StrCol": ["1", np.nan, "3", "4", "5"],
+            }
+        )
+
+        converters = {
+            "IntCol": lambda x: int(x) if x != "" else -1000,
+            "FloatCol": lambda x: 10 * x if x else np.nan,
+            2: lambda x: "Found" if x != "" else "Not found",
+            3: lambda x: str(x) if x else "",
+        }
+
+        # should read in correctly and set types of single cells (not array
+        # dtypes)
+        actual = pd.read_excel(
+            basename + read_ext, sheet_name="Sheet1", converters=converters
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    def test_reader_dtype(self, read_ext):
+        # GH 8212
+        basename = "testdtype"
+        actual = pd.read_excel(basename + read_ext)
+
+        expected = DataFrame(
+            {
+                "a": [1, 2, 3, 4],
+                "b": [2.5, 3.5, 4.5, 5.5],
+                "c": [1, 2, 3, 4],
+                "d": [1.0, 2.0, np.nan, 4.0],
+            }
+        )
+
+        tm.assert_frame_equal(actual, expected)
+
+        actual = pd.read_excel(
+            basename + read_ext, dtype={"a": "float64", "b": "float32", "c": str}
+        )
+
+        expected["a"] = expected["a"].astype("float64")
+        expected["b"] = expected["b"].astype("float32")
+        expected["c"] = Series(["001", "002", "003", "004"], dtype=object)
+        tm.assert_frame_equal(actual, expected)
+
+        msg = "Unable to convert column d to type int64"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel(basename + read_ext, dtype={"d": "int64"})
+
+    @pytest.mark.parametrize(
+        "dtype,expected",
+        [
+            (
+                None,
+                DataFrame(
+                    {
+                        "a": [1, 2, 3, 4],
+                        "b": [2.5, 3.5, 4.5, 5.5],
+                        "c": [1, 2, 3, 4],
+                        "d": [1.0, 2.0, np.nan, 4.0],
+                    }
+                ),
+            ),
+            (
+                {"a": "float64", "b": "float32", "c": str, "d": str},
+                DataFrame(
+                    {
+                        "a": Series([1, 2, 3, 4], dtype="float64"),
+                        "b": Series([2.5, 3.5, 4.5, 5.5], dtype="float32"),
+                        "c": Series(["001", "002", "003", "004"], dtype=object),
+                        "d": Series(["1", "2", np.nan, "4"], dtype=object),
+                    }
+                ),
+            ),
+        ],
+    )
+    def test_reader_dtype_str(self, read_ext, dtype, expected):
+        # see gh-20377
+        basename = "testdtype"
+
+        actual = pd.read_excel(basename + read_ext, dtype=dtype)
+        tm.assert_frame_equal(actual, expected)
+
+    def test_dtype_backend(self, read_ext, dtype_backend, engine):
+        # GH#36712
+        if read_ext in (".xlsb", ".xls"):
+            pytest.skip(f"No engine for filetype: '{read_ext}'")
+
+        df = DataFrame(
+            {
+                "a": Series([1, 3], dtype="Int64"),
+                "b": Series([2.5, 4.5], dtype="Float64"),
+                "c": Series([True, False], dtype="boolean"),
+                "d": Series(["a", "b"], dtype="string"),
+                "e": Series([pd.NA, 6], dtype="Int64"),
+                "f": Series([pd.NA, 7.5], dtype="Float64"),
+                "g": Series([pd.NA, True], dtype="boolean"),
+                "h": Series([pd.NA, "a"], dtype="string"),
+                "i": Series([pd.Timestamp("2019-12-31")] * 2),
+                "j": Series([pd.NA, pd.NA], dtype="Int64"),
+            }
+        )
+        with tm.ensure_clean(read_ext) as file_path:
+            df.to_excel(file_path, sheet_name="test", index=False)
+            result = pd.read_excel(
+                file_path, sheet_name="test", dtype_backend=dtype_backend
+            )
+        if dtype_backend == "pyarrow":
+            import pyarrow as pa
+
+            from pandas.arrays import ArrowExtensionArray
+
+            expected = DataFrame(
+                {
+                    col: ArrowExtensionArray(pa.array(df[col], from_pandas=True))
+                    for col in df.columns
+                }
+            )
+            # pyarrow by default infers timestamp resolution as us, not ns
+            expected["i"] = ArrowExtensionArray(
+                expected["i"].array._pa_array.cast(pa.timestamp(unit="us"))
+            )
+            # pyarrow supports a null type, so don't have to default to Int64
+            expected["j"] = ArrowExtensionArray(pa.array([None, None]))
+        else:
+            expected = df
+            unit = get_exp_unit(read_ext, engine)
+            expected["i"] = expected["i"].astype(f"M8[{unit}]")
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_dtype_backend_and_dtype(self, read_ext):
+        # GH#36712
+        if read_ext in (".xlsb", ".xls"):
+            pytest.skip(f"No engine for filetype: '{read_ext}'")
+
+        df = DataFrame({"a": [np.nan, 1.0], "b": [2.5, np.nan]})
+        with tm.ensure_clean(read_ext) as file_path:
+            df.to_excel(file_path, sheet_name="test", index=False)
+            result = pd.read_excel(
+                file_path,
+                sheet_name="test",
+                dtype_backend="numpy_nullable",
+                dtype="float64",
+            )
+        tm.assert_frame_equal(result, df)
+
+    @pytest.mark.xfail(
+        using_pyarrow_string_dtype(), reason="infer_string takes precedence"
+    )
+    def test_dtype_backend_string(self, read_ext, string_storage):
+        # GH#36712
+        if read_ext in (".xlsb", ".xls"):
+            pytest.skip(f"No engine for filetype: '{read_ext}'")
+
+        pa = pytest.importorskip("pyarrow")
+
+        with pd.option_context("mode.string_storage", string_storage):
+            df = DataFrame(
+                {
+                    "a": np.array(["a", "b"], dtype=np.object_),
+                    "b": np.array(["x", pd.NA], dtype=np.object_),
+                }
+            )
+            with tm.ensure_clean(read_ext) as file_path:
+                df.to_excel(file_path, sheet_name="test", index=False)
+                result = pd.read_excel(
+                    file_path, sheet_name="test", dtype_backend="numpy_nullable"
+                )
+
+            if string_storage == "python":
+                expected = DataFrame(
+                    {
+                        "a": StringArray(np.array(["a", "b"], dtype=np.object_)),
+                        "b": StringArray(np.array(["x", pd.NA], dtype=np.object_)),
+                    }
+                )
+            else:
+                expected = DataFrame(
+                    {
+                        "a": ArrowStringArray(pa.array(["a", "b"])),
+                        "b": ArrowStringArray(pa.array(["x", None])),
+                    }
+                )
+            tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtypes, exp_value", [({}, 1), ({"a.1": "int64"}, 1)])
+    def test_dtype_mangle_dup_cols(self, read_ext, dtypes, exp_value):
+        # GH#35211
+        basename = "df_mangle_dup_col_dtypes"
+        dtype_dict = {"a": object, **dtypes}
+        dtype_dict_copy = dtype_dict.copy()
+        # GH#42462
+        result = pd.read_excel(basename + read_ext, dtype=dtype_dict)
+        expected = DataFrame(
+            {
+                "a": Series([1], dtype=object),
+                "a.1": Series([exp_value], dtype=object if not dtypes else None),
+            }
+        )
+        assert dtype_dict == dtype_dict_copy, "dtype dict changed"
+        tm.assert_frame_equal(result, expected)
+
+    def test_reader_spaces(self, read_ext):
+        # see gh-32207
+        basename = "test_spaces"
+
+        actual = pd.read_excel(basename + read_ext)
+        expected = DataFrame(
+            {
+                "testcol": [
+                    "this is great",
+                    "4    spaces",
+                    "1 trailing ",
+                    " 1 leading",
+                    "2  spaces  multiple  times",
+                ]
+            }
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    # gh-36122, gh-35802
+    @pytest.mark.parametrize(
+        "basename,expected",
+        [
+            ("gh-35802", DataFrame({"COLUMN": ["Test (1)"]})),
+            ("gh-36122", DataFrame(columns=["got 2nd sa"])),
+        ],
+    )
+    def test_read_excel_ods_nested_xml(self, engine, read_ext, basename, expected):
+        # see gh-35802
+        if engine != "odf":
+            pytest.skip(f"Skipped for engine: {engine}")
+
+        actual = pd.read_excel(basename + read_ext)
+        tm.assert_frame_equal(actual, expected)
+
+    def test_reading_all_sheets(self, read_ext):
+        # Test reading all sheet names by setting sheet_name to None,
+        # Ensure a dict is returned.
+        # See PR #9450
+        basename = "test_multisheet"
+        dfs = pd.read_excel(basename + read_ext, sheet_name=None)
+        # ensure this is not alphabetical to test order preservation
+        expected_keys = ["Charlie", "Alpha", "Beta"]
+        tm.assert_contains_all(expected_keys, dfs.keys())
+        # Issue 9930
+        # Ensure sheet order is preserved
+        assert expected_keys == list(dfs.keys())
+
+    def test_reading_multiple_specific_sheets(self, read_ext):
+        # Test reading specific sheet names by specifying a mixed list
+        # of integers and strings, and confirm that duplicated sheet
+        # references (positions/names) are removed properly.
+        # Ensure a dict is returned
+        # See PR #9450
+        basename = "test_multisheet"
+        # Explicitly request duplicates. Only the set should be returned.
+        expected_keys = [2, "Charlie", "Charlie"]
+        dfs = pd.read_excel(basename + read_ext, sheet_name=expected_keys)
+        expected_keys = list(set(expected_keys))
+        tm.assert_contains_all(expected_keys, dfs.keys())
+        assert len(expected_keys) == len(dfs.keys())
+
+    def test_reading_all_sheets_with_blank(self, read_ext):
+        # Test reading all sheet names by setting sheet_name to None,
+        # In the case where some sheets are blank.
+        # Issue #11711
+        basename = "blank_with_header"
+        dfs = pd.read_excel(basename + read_ext, sheet_name=None)
+        expected_keys = ["Sheet1", "Sheet2", "Sheet3"]
+        tm.assert_contains_all(expected_keys, dfs.keys())
+
+    # GH6403
+    def test_read_excel_blank(self, read_ext):
+        actual = pd.read_excel("blank" + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, DataFrame())
+
+    def test_read_excel_blank_with_header(self, read_ext):
+        expected = DataFrame(columns=["col_1", "col_2"])
+        actual = pd.read_excel("blank_with_header" + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_exception_message_includes_sheet_name(self, read_ext):
+        # GH 48706
+        with pytest.raises(ValueError, match=r" \(sheet: Sheet1\)$"):
+            pd.read_excel("blank_with_header" + read_ext, header=[1], sheet_name=None)
+        with pytest.raises(ZeroDivisionError, match=r" \(sheet: Sheet1\)$"):
+            pd.read_excel("test1" + read_ext, usecols=lambda x: 1 / 0, sheet_name=None)
+
+    @pytest.mark.filterwarnings("ignore:Cell A4 is marked:UserWarning:openpyxl")
+    def test_date_conversion_overflow(self, request, engine, read_ext):
+        # GH 10001 : pandas.ExcelFile ignore parse_dates=False
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = DataFrame(
+            [
+                [pd.Timestamp("2016-03-12"), "Marc Johnson"],
+                [pd.Timestamp("2016-03-16"), "Jack Black"],
+                [1e20, "Timothy Brown"],
+            ],
+            columns=["DateColWithBigInt", "StringCol"],
+        )
+
+        if engine == "openpyxl":
+            request.applymarker(
+                pytest.mark.xfail(reason="Maybe not supported by openpyxl")
+            )
+
+        if engine is None and read_ext in (".xlsx", ".xlsm"):
+            # GH 35029
+            request.applymarker(
+                pytest.mark.xfail(reason="Defaults to openpyxl, maybe not supported")
+            )
+
+        result = pd.read_excel("testdateoverflow" + read_ext)
+        tm.assert_frame_equal(result, expected)
+
+    def test_sheet_name(self, request, read_ext, engine, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        filename = "test1"
+        sheet_name = "Sheet1"
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        df1 = pd.read_excel(
+            filename + read_ext, sheet_name=sheet_name, index_col=0
+        )  # doc
+        df2 = pd.read_excel(filename + read_ext, index_col=0, sheet_name=sheet_name)
+
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+    def test_excel_read_buffer(self, read_ext):
+        pth = "test1" + read_ext
+        expected = pd.read_excel(pth, sheet_name="Sheet1", index_col=0)
+        with open(pth, "rb") as f:
+            actual = pd.read_excel(f, sheet_name="Sheet1", index_col=0)
+            tm.assert_frame_equal(expected, actual)
+
+    def test_bad_engine_raises(self):
+        bad_engine = "foo"
+        with pytest.raises(ValueError, match="Unknown engine: foo"):
+            pd.read_excel("", engine=bad_engine)
+
+    @pytest.mark.parametrize(
+        "sheet_name",
+        [3, [0, 3], [3, 0], "Sheet4", ["Sheet1", "Sheet4"], ["Sheet4", "Sheet1"]],
+    )
+    def test_bad_sheetname_raises(self, read_ext, sheet_name):
+        # GH 39250
+        msg = "Worksheet index 3 is invalid|Worksheet named 'Sheet4' not found"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("blank" + read_ext, sheet_name=sheet_name)
+
+    def test_missing_file_raises(self, read_ext):
+        bad_file = f"foo{read_ext}"
+        # CI tests with other languages, translates to "No such file or directory"
+        match = "|".join(
+            [
+                "(No such file or directory",
+                "没有那个文件或目录",
+                "File o directory non esistente)",
+            ]
+        )
+        with pytest.raises(FileNotFoundError, match=match):
+            pd.read_excel(bad_file)
+
+    def test_corrupt_bytes_raises(self, engine):
+        bad_stream = b"foo"
+        if engine is None:
+            error = ValueError
+            msg = (
+                "Excel file format cannot be determined, you must "
+                "specify an engine manually."
+            )
+        elif engine == "xlrd":
+            from xlrd import XLRDError
+
+            error = XLRDError
+            msg = (
+                "Unsupported format, or corrupt file: Expected BOF "
+                "record; found b'foo'"
+            )
+        elif engine == "calamine":
+            from python_calamine import CalamineError
+
+            error = CalamineError
+            msg = "Cannot detect file format"
+        else:
+            error = BadZipFile
+            msg = "File is not a zip file"
+        with pytest.raises(error, match=msg):
+            pd.read_excel(BytesIO(bad_stream))
+
+    @pytest.mark.network
+    @pytest.mark.single_cpu
+    def test_read_from_http_url(self, httpserver, read_ext):
+        with open("test1" + read_ext, "rb") as f:
+            httpserver.serve_content(content=f.read())
+        url_table = pd.read_excel(httpserver.url)
+        local_table = pd.read_excel("test1" + read_ext)
+        tm.assert_frame_equal(url_table, local_table)
+
+    @td.skip_if_not_us_locale
+    @pytest.mark.single_cpu
+    def test_read_from_s3_url(self, read_ext, s3_public_bucket, s3so):
+        # Bucket created in tests/io/conftest.py
+        with open("test1" + read_ext, "rb") as f:
+            s3_public_bucket.put_object(Key="test1" + read_ext, Body=f)
+
+        url = f"s3://{s3_public_bucket.name}/test1" + read_ext
+
+        url_table = pd.read_excel(url, storage_options=s3so)
+        local_table = pd.read_excel("test1" + read_ext)
+        tm.assert_frame_equal(url_table, local_table)
+
+    @pytest.mark.single_cpu
+    def test_read_from_s3_object(self, read_ext, s3_public_bucket, s3so):
+        # GH 38788
+        # Bucket created in tests/io/conftest.py
+        with open("test1" + read_ext, "rb") as f:
+            s3_public_bucket.put_object(Key="test1" + read_ext, Body=f)
+
+        import s3fs
+
+        s3 = s3fs.S3FileSystem(**s3so)
+
+        with s3.open(f"s3://{s3_public_bucket.name}/test1" + read_ext) as f:
+            url_table = pd.read_excel(f)
+
+        local_table = pd.read_excel("test1" + read_ext)
+        tm.assert_frame_equal(url_table, local_table)
+
+    @pytest.mark.slow
+    def test_read_from_file_url(self, read_ext, datapath):
+        # FILE
+        localtable = os.path.join(datapath("io", "data", "excel"), "test1" + read_ext)
+        local_table = pd.read_excel(localtable)
+
+        try:
+            url_table = pd.read_excel("file://localhost/" + localtable)
+        except URLError:
+            # fails on some systems
+            platform_info = " ".join(platform.uname()).strip()
+            pytest.skip(f"failing on {platform_info}")
+
+        tm.assert_frame_equal(url_table, local_table)
+
+    def test_read_from_pathlib_path(self, read_ext):
+        # GH12655
+        str_path = "test1" + read_ext
+        expected = pd.read_excel(str_path, sheet_name="Sheet1", index_col=0)
+
+        path_obj = Path("test1" + read_ext)
+        actual = pd.read_excel(path_obj, sheet_name="Sheet1", index_col=0)
+
+        tm.assert_frame_equal(expected, actual)
+
+    @td.skip_if_no("py.path")
+    def test_read_from_py_localpath(self, read_ext):
+        # GH12655
+        from py.path import local as LocalPath
+
+        str_path = os.path.join("test1" + read_ext)
+        expected = pd.read_excel(str_path, sheet_name="Sheet1", index_col=0)
+
+        path_obj = LocalPath().join("test1" + read_ext)
+        actual = pd.read_excel(path_obj, sheet_name="Sheet1", index_col=0)
+
+        tm.assert_frame_equal(expected, actual)
+
+    def test_close_from_py_localpath(self, read_ext):
+        # GH31467
+        str_path = os.path.join("test1" + read_ext)
+        with open(str_path, "rb") as f:
+            x = pd.read_excel(f, sheet_name="Sheet1", index_col=0)
+            del x
+            # should not throw an exception because the passed file was closed
+            f.read()
+
+    def test_reader_seconds(self, request, engine, read_ext):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        # GH 55045
+        if engine == "calamine" and read_ext == ".ods":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="ODS file contains bad datetime (seconds as text)"
+                )
+            )
+
+        # Test reading times with and without milliseconds. GH5945.
+        expected = DataFrame.from_dict(
+            {
+                "Time": [
+                    time(1, 2, 3),
+                    time(2, 45, 56, 100000),
+                    time(4, 29, 49, 200000),
+                    time(6, 13, 42, 300000),
+                    time(7, 57, 35, 400000),
+                    time(9, 41, 28, 500000),
+                    time(11, 25, 21, 600000),
+                    time(13, 9, 14, 700000),
+                    time(14, 53, 7, 800000),
+                    time(16, 37, 0, 900000),
+                    time(18, 20, 54),
+                ]
+            }
+        )
+
+        actual = pd.read_excel("times_1900" + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, expected)
+
+        actual = pd.read_excel("times_1904" + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_multiindex(self, request, engine, read_ext):
+        # see gh-4679
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        unit = get_exp_unit(read_ext, engine)
+
+        mi = MultiIndex.from_product([["foo", "bar"], ["a", "b"]])
+        mi_file = "testmultiindex" + read_ext
+
+        # "mi_column" sheet
+        expected = DataFrame(
+            [
+                [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=mi,
+        )
+        expected[mi[2]] = expected[mi[2]].astype(f"M8[{unit}]")
+
+        actual = pd.read_excel(
+            mi_file, sheet_name="mi_column", header=[0, 1], index_col=0
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # "mi_index" sheet
+        expected.index = mi
+        expected.columns = ["a", "b", "c", "d"]
+
+        actual = pd.read_excel(mi_file, sheet_name="mi_index", index_col=[0, 1])
+        tm.assert_frame_equal(actual, expected)
+
+        # "both" sheet
+        expected.columns = mi
+
+        actual = pd.read_excel(
+            mi_file, sheet_name="both", index_col=[0, 1], header=[0, 1]
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # "mi_index_name" sheet
+        expected.columns = ["a", "b", "c", "d"]
+        expected.index = mi.set_names(["ilvl1", "ilvl2"])
+
+        actual = pd.read_excel(mi_file, sheet_name="mi_index_name", index_col=[0, 1])
+        tm.assert_frame_equal(actual, expected)
+
+        # "mi_column_name" sheet
+        expected.index = list(range(4))
+        expected.columns = mi.set_names(["c1", "c2"])
+        actual = pd.read_excel(
+            mi_file, sheet_name="mi_column_name", header=[0, 1], index_col=0
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # see gh-11317
+        # "name_with_int" sheet
+        expected.columns = mi.set_levels([1, 2], level=1).set_names(["c1", "c2"])
+
+        actual = pd.read_excel(
+            mi_file, sheet_name="name_with_int", index_col=0, header=[0, 1]
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # "both_name" sheet
+        expected.columns = mi.set_names(["c1", "c2"])
+        expected.index = mi.set_names(["ilvl1", "ilvl2"])
+
+        actual = pd.read_excel(
+            mi_file, sheet_name="both_name", index_col=[0, 1], header=[0, 1]
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # "both_skiprows" sheet
+        actual = pd.read_excel(
+            mi_file,
+            sheet_name="both_name_skiprows",
+            index_col=[0, 1],
+            header=[0, 1],
+            skiprows=2,
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    @pytest.mark.parametrize(
+        "sheet_name,idx_lvl2",
+        [
+            ("both_name_blank_after_mi_name", [np.nan, "b", "a", "b"]),
+            ("both_name_multiple_blanks", [np.nan] * 4),
+        ],
+    )
+    def test_read_excel_multiindex_blank_after_name(
+        self, request, engine, read_ext, sheet_name, idx_lvl2
+    ):
+        # GH34673
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        mi_file = "testmultiindex" + read_ext
+        mi = MultiIndex.from_product([["foo", "bar"], ["a", "b"]], names=["c1", "c2"])
+
+        unit = get_exp_unit(read_ext, engine)
+
+        expected = DataFrame(
+            [
+                [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=mi,
+            index=MultiIndex.from_arrays(
+                (["foo", "foo", "bar", "bar"], idx_lvl2),
+                names=["ilvl1", "ilvl2"],
+            ),
+        )
+        expected[mi[2]] = expected[mi[2]].astype(f"M8[{unit}]")
+        result = pd.read_excel(
+            mi_file,
+            sheet_name=sheet_name,
+            index_col=[0, 1],
+            header=[0, 1],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_read_excel_multiindex_header_only(self, read_ext):
+        # see gh-11733.
+        #
+        # Don't try to parse a header name if there isn't one.
+        mi_file = "testmultiindex" + read_ext
+        result = pd.read_excel(mi_file, sheet_name="index_col_none", header=[0, 1])
+
+        exp_columns = MultiIndex.from_product([("A", "B"), ("key", "val")])
+        expected = DataFrame([[1, 2, 3, 4]] * 2, columns=exp_columns)
+        tm.assert_frame_equal(result, expected)
+
+    def test_excel_old_index_format(self, read_ext):
+        # see gh-4679
+        filename = "test_index_name_pre17" + read_ext
+
+        # We detect headers to determine if index names exist, so
+        # that "index" name in the "names" version of the data will
+        # now be interpreted as rows that include null data.
+        data = np.array(
+            [
+                [np.nan, np.nan, np.nan, np.nan, np.nan],
+                ["R0C0", "R0C1", "R0C2", "R0C3", "R0C4"],
+                ["R1C0", "R1C1", "R1C2", "R1C3", "R1C4"],
+                ["R2C0", "R2C1", "R2C2", "R2C3", "R2C4"],
+                ["R3C0", "R3C1", "R3C2", "R3C3", "R3C4"],
+                ["R4C0", "R4C1", "R4C2", "R4C3", "R4C4"],
+            ],
+            dtype=object,
+        )
+        columns = ["C_l0_g0", "C_l0_g1", "C_l0_g2", "C_l0_g3", "C_l0_g4"]
+        mi = MultiIndex(
+            levels=[
+                ["R0", "R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"],
+                ["R1", "R_l1_g0", "R_l1_g1", "R_l1_g2", "R_l1_g3", "R_l1_g4"],
+            ],
+            codes=[[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]],
+            names=[None, None],
+        )
+        si = Index(
+            ["R0", "R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], name=None
+        )
+
+        expected = DataFrame(data, index=si, columns=columns)
+
+        actual = pd.read_excel(filename, sheet_name="single_names", index_col=0)
+        tm.assert_frame_equal(actual, expected)
+
+        expected.index = mi
+
+        actual = pd.read_excel(filename, sheet_name="multi_names", index_col=[0, 1])
+        tm.assert_frame_equal(actual, expected)
+
+        # The analogous versions of the "names" version data
+        # where there are explicitly no names for the indices.
+        data = np.array(
+            [
+                ["R0C0", "R0C1", "R0C2", "R0C3", "R0C4"],
+                ["R1C0", "R1C1", "R1C2", "R1C3", "R1C4"],
+                ["R2C0", "R2C1", "R2C2", "R2C3", "R2C4"],
+                ["R3C0", "R3C1", "R3C2", "R3C3", "R3C4"],
+                ["R4C0", "R4C1", "R4C2", "R4C3", "R4C4"],
+            ]
+        )
+        columns = ["C_l0_g0", "C_l0_g1", "C_l0_g2", "C_l0_g3", "C_l0_g4"]
+        mi = MultiIndex(
+            levels=[
+                ["R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"],
+                ["R_l1_g0", "R_l1_g1", "R_l1_g2", "R_l1_g3", "R_l1_g4"],
+            ],
+            codes=[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]],
+            names=[None, None],
+        )
+        si = Index(["R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], name=None)
+
+        expected = DataFrame(data, index=si, columns=columns)
+
+        actual = pd.read_excel(filename, sheet_name="single_no_names", index_col=0)
+        tm.assert_frame_equal(actual, expected)
+
+        expected.index = mi
+
+        actual = pd.read_excel(filename, sheet_name="multi_no_names", index_col=[0, 1])
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_bool_header_arg(self, read_ext):
+        # GH 6114
+        msg = "Passing a bool to header is invalid"
+        for arg in [True, False]:
+            with pytest.raises(TypeError, match=msg):
+                pd.read_excel("test1" + read_ext, header=arg)
+
+    def test_read_excel_skiprows(self, request, engine, read_ext):
+        # GH 4903
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        unit = get_exp_unit(read_ext, engine)
+
+        actual = pd.read_excel(
+            "testskiprows" + read_ext, sheet_name="skiprows_list", skiprows=[0, 2]
+        )
+        expected = DataFrame(
+            [
+                [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=["a", "b", "c", "d"],
+        )
+        expected["c"] = expected["c"].astype(f"M8[{unit}]")
+        tm.assert_frame_equal(actual, expected)
+
+        actual = pd.read_excel(
+            "testskiprows" + read_ext,
+            sheet_name="skiprows_list",
+            skiprows=np.array([0, 2]),
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # GH36435
+        actual = pd.read_excel(
+            "testskiprows" + read_ext,
+            sheet_name="skiprows_list",
+            skiprows=lambda x: x in [0, 2],
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        actual = pd.read_excel(
+            "testskiprows" + read_ext,
+            sheet_name="skiprows_list",
+            skiprows=3,
+            names=["a", "b", "c", "d"],
+        )
+        expected = DataFrame(
+            [
+                # [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=["a", "b", "c", "d"],
+        )
+        expected["c"] = expected["c"].astype(f"M8[{unit}]")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_skiprows_callable_not_in(self, request, engine, read_ext):
+        # GH 4903
+        xfail_datetimes_with_pyxlsb(engine, request)
+        unit = get_exp_unit(read_ext, engine)
+
+        actual = pd.read_excel(
+            "testskiprows" + read_ext,
+            sheet_name="skiprows_list",
+            skiprows=lambda x: x not in [1, 3, 5],
+        )
+        expected = DataFrame(
+            [
+                [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                # [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                # [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=["a", "b", "c", "d"],
+        )
+        expected["c"] = expected["c"].astype(f"M8[{unit}]")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_nrows(self, read_ext):
+        # GH 16645
+        num_rows_to_pull = 5
+        actual = pd.read_excel("test1" + read_ext, nrows=num_rows_to_pull)
+        expected = pd.read_excel("test1" + read_ext)
+        expected = expected[:num_rows_to_pull]
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_nrows_greater_than_nrows_in_file(self, read_ext):
+        # GH 16645
+        expected = pd.read_excel("test1" + read_ext)
+        num_records_in_file = len(expected)
+        num_rows_to_pull = num_records_in_file + 10
+        actual = pd.read_excel("test1" + read_ext, nrows=num_rows_to_pull)
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_nrows_non_integer_parameter(self, read_ext):
+        # GH 16645
+        msg = "'nrows' must be an integer >=0"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("test1" + read_ext, nrows="5")
+
+    @pytest.mark.parametrize(
+        "filename,sheet_name,header,index_col,skiprows",
+        [
+            ("testmultiindex", "mi_column", [0, 1], 0, None),
+            ("testmultiindex", "mi_index", None, [0, 1], None),
+            ("testmultiindex", "both", [0, 1], [0, 1], None),
+            ("testmultiindex", "mi_column_name", [0, 1], 0, None),
+            ("testskiprows", "skiprows_list", None, None, [0, 2]),
+            ("testskiprows", "skiprows_list", None, None, lambda x: x in (0, 2)),
+        ],
+    )
+    def test_read_excel_nrows_params(
+        self, read_ext, filename, sheet_name, header, index_col, skiprows
+    ):
+        """
+        For various parameters, we should get the same result whether we
+        limit the rows during load (nrows=3) or after (df.iloc[:3]).
+        """
+        # GH 46894
+        expected = pd.read_excel(
+            filename + read_ext,
+            sheet_name=sheet_name,
+            header=header,
+            index_col=index_col,
+            skiprows=skiprows,
+        ).iloc[:3]
+        actual = pd.read_excel(
+            filename + read_ext,
+            sheet_name=sheet_name,
+            header=header,
+            index_col=index_col,
+            skiprows=skiprows,
+            nrows=3,
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    def test_deprecated_kwargs(self, read_ext):
+        with pytest.raises(TypeError, match="but 3 positional arguments"):
+            pd.read_excel("test1" + read_ext, "Sheet1", 0)
+
+    def test_no_header_with_list_index_col(self, read_ext):
+        # GH 31783
+        file_name = "testmultiindex" + read_ext
+        data = [("B", "B"), ("key", "val"), (3, 4), (3, 4)]
+        idx = MultiIndex.from_tuples(
+            [("A", "A"), ("key", "val"), (1, 2), (1, 2)], names=(0, 1)
+        )
+        expected = DataFrame(data, index=idx, columns=(2, 3))
+        result = pd.read_excel(
+            file_name, sheet_name="index_col_none", index_col=[0, 1], header=None
+        )
+        tm.assert_frame_equal(expected, result)
+
+    def test_one_col_noskip_blank_line(self, read_ext):
+        # GH 39808
+        file_name = "one_col_blank_line" + read_ext
+        data = [0.5, np.nan, 1, 2]
+        expected = DataFrame(data, columns=["numbers"])
+        result = pd.read_excel(file_name)
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiheader_two_blank_lines(self, read_ext):
+        # GH 40442
+        file_name = "testmultiindex" + read_ext
+        columns = MultiIndex.from_tuples([("a", "A"), ("b", "B")])
+        data = [[np.nan, np.nan], [np.nan, np.nan], [1, 3], [2, 4]]
+        expected = DataFrame(data, columns=columns)
+        result = pd.read_excel(
+            file_name, sheet_name="mi_column_empty_rows", header=[0, 1]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_trailing_blanks(self, read_ext):
+        """
+        Sheets can contain blank cells with no data. Some of our readers
+        were including those cells, creating many empty rows and columns
+        """
+        file_name = "trailing_blanks" + read_ext
+        result = pd.read_excel(file_name)
+        assert result.shape == (3, 3)
+
+    def test_ignore_chartsheets_by_str(self, request, engine, read_ext):
+        # GH 41448
+        if read_ext == ".ods":
+            pytest.skip("chartsheets do not exist in the ODF format")
+        if engine == "pyxlsb":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="pyxlsb can't distinguish chartsheets from worksheets"
+                )
+            )
+        with pytest.raises(ValueError, match="Worksheet named 'Chart1' not found"):
+            pd.read_excel("chartsheet" + read_ext, sheet_name="Chart1")
+
+    def test_ignore_chartsheets_by_int(self, request, engine, read_ext):
+        # GH 41448
+        if read_ext == ".ods":
+            pytest.skip("chartsheets do not exist in the ODF format")
+        if engine == "pyxlsb":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="pyxlsb can't distinguish chartsheets from worksheets"
+                )
+            )
+        with pytest.raises(
+            ValueError, match="Worksheet index 1 is invalid, 1 worksheets found"
+        ):
+            pd.read_excel("chartsheet" + read_ext, sheet_name=1)
+
+    def test_euro_decimal_format(self, read_ext):
+        # copied from read_csv
+        result = pd.read_excel("test_decimal" + read_ext, decimal=",", skiprows=1)
+        expected = DataFrame(
+            [
+                [1, 1521.1541, 187101.9543, "ABC", "poi", 4.738797819],
+                [2, 121.12, 14897.76, "DEF", "uyt", 0.377320872],
+                [3, 878.158, 108013.434, "GHI", "rez", 2.735694704],
+            ],
+            columns=["Id", "Number1", "Number2", "Text1", "Text2", "Number3"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+class TestExcelFileRead:
+    def test_deprecate_bytes_input(self, engine, read_ext):
+        # GH 53830
+        msg = (
+            "Passing bytes to 'read_excel' is deprecated and "
+            "will be removed in a future version. To read from a "
+            "byte string, wrap it in a `BytesIO` object."
+        )
+
+        with tm.assert_produces_warning(
+            FutureWarning, match=msg, raise_on_extra_warnings=False
+        ):
+            with open("test1" + read_ext, "rb") as f:
+                pd.read_excel(f.read(), engine=engine)
+
+    @pytest.fixture(autouse=True)
+    def cd_and_set_engine(self, engine, datapath, monkeypatch):
+        """
+        Change directory and set engine for ExcelFile objects.
+        """
+        func = partial(pd.ExcelFile, engine=engine)
+        monkeypatch.chdir(datapath("io", "data", "excel"))
+        monkeypatch.setattr(pd, "ExcelFile", func)
+
+    def test_engine_used(self, read_ext, engine):
+        expected_defaults = {
+            "xlsx": "openpyxl",
+            "xlsm": "openpyxl",
+            "xlsb": "pyxlsb",
+            "xls": "xlrd",
+            "ods": "odf",
+        }
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            result = excel.engine
+
+        if engine is not None:
+            expected = engine
+        else:
+            expected = expected_defaults[read_ext[1:]]
+        assert result == expected
+
+    def test_excel_passes_na(self, read_ext):
+        with pd.ExcelFile("test4" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel, sheet_name="Sheet1", keep_default_na=False, na_values=["apple"]
+            )
+        expected = DataFrame(
+            [["NA"], [1], ["NA"], [np.nan], ["rabbit"]], columns=["Test"]
+        )
+        tm.assert_frame_equal(parsed, expected)
+
+        with pd.ExcelFile("test4" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel, sheet_name="Sheet1", keep_default_na=True, na_values=["apple"]
+            )
+        expected = DataFrame(
+            [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]], columns=["Test"]
+        )
+        tm.assert_frame_equal(parsed, expected)
+
+        # 13967
+        with pd.ExcelFile("test5" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel, sheet_name="Sheet1", keep_default_na=False, na_values=["apple"]
+            )
+        expected = DataFrame(
+            [["1.#QNAN"], [1], ["nan"], [np.nan], ["rabbit"]], columns=["Test"]
+        )
+        tm.assert_frame_equal(parsed, expected)
+
+        with pd.ExcelFile("test5" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel, sheet_name="Sheet1", keep_default_na=True, na_values=["apple"]
+            )
+        expected = DataFrame(
+            [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]], columns=["Test"]
+        )
+        tm.assert_frame_equal(parsed, expected)
+
+    @pytest.mark.parametrize("na_filter", [None, True, False])
+    def test_excel_passes_na_filter(self, read_ext, na_filter):
+        # gh-25453
+        kwargs = {}
+
+        if na_filter is not None:
+            kwargs["na_filter"] = na_filter
+
+        with pd.ExcelFile("test5" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel,
+                sheet_name="Sheet1",
+                keep_default_na=True,
+                na_values=["apple"],
+                **kwargs,
+            )
+
+        if na_filter is False:
+            expected = [["1.#QNAN"], [1], ["nan"], ["apple"], ["rabbit"]]
+        else:
+            expected = [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]]
+
+        expected = DataFrame(expected, columns=["Test"])
+        tm.assert_frame_equal(parsed, expected)
+
+    def test_excel_table_sheet_by_index(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            df1 = pd.read_excel(excel, sheet_name=0, index_col=0)
+            df2 = pd.read_excel(excel, sheet_name=1, skiprows=[1], index_col=0)
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            df1 = excel.parse(0, index_col=0)
+            df2 = excel.parse(1, skiprows=[1], index_col=0)
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            df3 = pd.read_excel(excel, sheet_name=0, index_col=0, skipfooter=1)
+        tm.assert_frame_equal(df3, df1.iloc[:-1])
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            df3 = excel.parse(0, index_col=0, skipfooter=1)
+
+        tm.assert_frame_equal(df3, df1.iloc[:-1])
+
+    def test_sheet_name(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        filename = "test1"
+        sheet_name = "Sheet1"
+
+        with pd.ExcelFile(filename + read_ext) as excel:
+            df1_parse = excel.parse(sheet_name=sheet_name, index_col=0)  # doc
+
+        with pd.ExcelFile(filename + read_ext) as excel:
+            df2_parse = excel.parse(index_col=0, sheet_name=sheet_name)
+
+        tm.assert_frame_equal(df1_parse, expected)
+        tm.assert_frame_equal(df2_parse, expected)
+
+    @pytest.mark.parametrize(
+        "sheet_name",
+        [3, [0, 3], [3, 0], "Sheet4", ["Sheet1", "Sheet4"], ["Sheet4", "Sheet1"]],
+    )
+    def test_bad_sheetname_raises(self, read_ext, sheet_name):
+        # GH 39250
+        msg = "Worksheet index 3 is invalid|Worksheet named 'Sheet4' not found"
+        with pytest.raises(ValueError, match=msg):
+            with pd.ExcelFile("blank" + read_ext) as excel:
+                excel.parse(sheet_name=sheet_name)
+
+    def test_excel_read_buffer(self, engine, read_ext):
+        pth = "test1" + read_ext
+        expected = pd.read_excel(pth, sheet_name="Sheet1", index_col=0, engine=engine)
+
+        with open(pth, "rb") as f:
+            with pd.ExcelFile(f) as xls:
+                actual = pd.read_excel(xls, sheet_name="Sheet1", index_col=0)
+
+        tm.assert_frame_equal(expected, actual)
+
+    def test_reader_closes_file(self, engine, read_ext):
+        with open("test1" + read_ext, "rb") as f:
+            with pd.ExcelFile(f) as xlsx:
+                # parses okay
+                pd.read_excel(xlsx, sheet_name="Sheet1", index_col=0, engine=engine)
+
+        assert f.closed
+
+    def test_conflicting_excel_engines(self, read_ext):
+        # GH 26566
+        msg = "Engine should not be specified when passing an ExcelFile"
+
+        with pd.ExcelFile("test1" + read_ext) as xl:
+            with pytest.raises(ValueError, match=msg):
+                pd.read_excel(xl, engine="foo")
+
+    def test_excel_read_binary(self, engine, read_ext):
+        # GH 15914
+        expected = pd.read_excel("test1" + read_ext, engine=engine)
+
+        with open("test1" + read_ext, "rb") as f:
+            data = f.read()
+
+        actual = pd.read_excel(BytesIO(data), engine=engine)
+        tm.assert_frame_equal(expected, actual)
+
+    def test_excel_read_binary_via_read_excel(self, read_ext, engine):
+        # GH 38424
+        with open("test1" + read_ext, "rb") as f:
+            result = pd.read_excel(f, engine=engine)
+        expected = pd.read_excel("test1" + read_ext, engine=engine)
+        tm.assert_frame_equal(result, expected)
+
+    def test_read_excel_header_index_out_of_range(self, engine):
+        # GH#43143
+        with open("df_header_oob.xlsx", "rb") as f:
+            with pytest.raises(ValueError, match="exceeds maximum"):
+                pd.read_excel(f, header=[0, 1])
+
+    @pytest.mark.parametrize("filename", ["df_empty.xlsx", "df_equals.xlsx"])
+    def test_header_with_index_col(self, filename):
+        # GH 33476
+        idx = Index(["Z"], name="I2")
+        cols = MultiIndex.from_tuples([("A", "B"), ("A", "B.1")], names=["I11", "I12"])
+        expected = DataFrame([[1, 3]], index=idx, columns=cols, dtype="int64")
+        result = pd.read_excel(
+            filename, sheet_name="Sheet1", index_col=0, header=[0, 1]
+        )
+        tm.assert_frame_equal(expected, result)
+
+    def test_read_datetime_multiindex(self, request, engine, read_ext):
+        # GH 34748
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        f = "test_datetime_mi" + read_ext
+        with pd.ExcelFile(f) as excel:
+            actual = pd.read_excel(excel, header=[0, 1], index_col=0, engine=engine)
+
+        unit = get_exp_unit(read_ext, engine)
+        dti = pd.DatetimeIndex(["2020-02-29", "2020-03-01"], dtype=f"M8[{unit}]")
+        expected_column_index = MultiIndex.from_arrays(
+            [dti[:1], dti[1:]],
+            names=[
+                dti[0].to_pydatetime(),
+                dti[1].to_pydatetime(),
+            ],
+        )
+        expected = DataFrame([], index=[], columns=expected_column_index)
+
+        tm.assert_frame_equal(expected, actual)
+
+    def test_engine_invalid_option(self, read_ext):
+        # read_ext includes the '.' hence the weird formatting
+        with pytest.raises(ValueError, match="Value must be one of *"):
+            with pd.option_context(f"io.excel{read_ext}.reader", "abc"):
+                pass
+
+    def test_ignore_chartsheets(self, request, engine, read_ext):
+        # GH 41448
+        if read_ext == ".ods":
+            pytest.skip("chartsheets do not exist in the ODF format")
+        if engine == "pyxlsb":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="pyxlsb can't distinguish chartsheets from worksheets"
+                )
+            )
+        with pd.ExcelFile("chartsheet" + read_ext) as excel:
+            assert excel.sheet_names == ["Sheet1"]
+
+    def test_corrupt_files_closed(self, engine, read_ext):
+        # GH41778
+        errors = (BadZipFile,)
+        if engine is None:
+            pytest.skip(f"Invalid test for engine={engine}")
+        elif engine == "xlrd":
+            import xlrd
+
+            errors = (BadZipFile, xlrd.biffh.XLRDError)
+        elif engine == "calamine":
+            from python_calamine import CalamineError
+
+            errors = (CalamineError,)
+
+        with tm.ensure_clean(f"corrupt{read_ext}") as file:
+            Path(file).write_text("corrupt", encoding="utf-8")
+            with tm.assert_produces_warning(False):
+                try:
+                    pd.ExcelFile(file, engine=engine)
+                except errors:
+                    pass

env-llmeval/lib/python3.10/site-packages/pandas/tests/io/excel/test_style.py

@@ -0,0 +1,298 @@
+import contextlib
+import time
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    read_excel,
+)
+import pandas._testing as tm
+
+from pandas.io.excel import ExcelWriter
+from pandas.io.formats.excel import ExcelFormatter
+
+pytest.importorskip("jinja2")
+# jinja2 is currently required for Styler.__init__(). Technically Styler.to_excel
+# could compute styles and render to excel without jinja2, since there is no
+# 'template' file, but this needs the import error to delayed until render time.
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+def assert_equal_cell_styles(cell1, cell2):
+    # TODO: should find a better way to check equality
+    assert cell1.alignment.__dict__ == cell2.alignment.__dict__
+    assert cell1.border.__dict__ == cell2.border.__dict__
+    assert cell1.fill.__dict__ == cell2.fill.__dict__
+    assert cell1.font.__dict__ == cell2.font.__dict__
+    assert cell1.number_format == cell2.number_format
+    assert cell1.protection.__dict__ == cell2.protection.__dict__
+
+
+@pytest.mark.parametrize(
+    "engine",
+    ["xlsxwriter", "openpyxl"],
+)
+def test_styler_to_excel_unstyled(engine):
+    # compare DataFrame.to_excel and Styler.to_excel when no styles applied
+    pytest.importorskip(engine)
+    df = DataFrame(np.random.default_rng(2).standard_normal((2, 2)))
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine=engine) as writer:
+            df.to_excel(writer, sheet_name="dataframe")
+            df.style.to_excel(writer, sheet_name="unstyled")
+
+        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            for col1, col2 in zip(wb["dataframe"].columns, wb["unstyled"].columns):
+                assert len(col1) == len(col2)
+                for cell1, cell2 in zip(col1, col2):
+                    assert cell1.value == cell2.value
+                    assert_equal_cell_styles(cell1, cell2)
+
+
+shared_style_params = [
+    (
+        "background-color: #111222",
+        ["fill", "fgColor", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    (
+        "color: #111222",
+        ["font", "color", "value"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("font-family: Arial;", ["font", "name"], "arial"),
+    ("font-weight: bold;", ["font", "b"], True),
+    ("font-style: italic;", ["font", "i"], True),
+    ("text-decoration: underline;", ["font", "u"], "single"),
+    ("number-format: $??,???.00;", ["number_format"], "$??,???.00"),
+    ("text-align: left;", ["alignment", "horizontal"], "left"),
+    (
+        "vertical-align: bottom;",
+        ["alignment", "vertical"],
+        {"xlsxwriter": None, "openpyxl": "bottom"},  # xlsxwriter Fails
+    ),
+    ("vertical-align: middle;", ["alignment", "vertical"], "center"),
+    # Border widths
+    ("border-left: 2pt solid red", ["border", "left", "style"], "medium"),
+    ("border-left: 1pt dotted red", ["border", "left", "style"], "dotted"),
+    ("border-left: 2pt dotted red", ["border", "left", "style"], "mediumDashDotDot"),
+    ("border-left: 1pt dashed red", ["border", "left", "style"], "dashed"),
+    ("border-left: 2pt dashed red", ["border", "left", "style"], "mediumDashed"),
+    ("border-left: 1pt solid red", ["border", "left", "style"], "thin"),
+    ("border-left: 3pt solid red", ["border", "left", "style"], "thick"),
+    # Border expansion
+    (
+        "border-left: 2pt solid #111222",
+        ["border", "left", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("border: 1pt solid red", ["border", "top", "style"], "thin"),
+    (
+        "border: 1pt solid #111222",
+        ["border", "top", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("border: 1pt solid red", ["border", "right", "style"], "thin"),
+    (
+        "border: 1pt solid #111222",
+        ["border", "right", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("border: 1pt solid red", ["border", "bottom", "style"], "thin"),
+    (
+        "border: 1pt solid #111222",
+        ["border", "bottom", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("border: 1pt solid red", ["border", "left", "style"], "thin"),
+    (
+        "border: 1pt solid #111222",
+        ["border", "left", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    # Border styles
+    (
+        "border-left-style: hair; border-left-color: black",
+        ["border", "left", "style"],
+        "hair",
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "engine",
+    ["xlsxwriter", "openpyxl"],
+)
+@pytest.mark.parametrize("css, attrs, expected", shared_style_params)
+def test_styler_to_excel_basic(engine, css, attrs, expected):
+    pytest.importorskip(engine)
+    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
+    styler = df.style.map(lambda x: css)
+
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine=engine) as writer:
+            df.to_excel(writer, sheet_name="dataframe")
+            styler.to_excel(writer, sheet_name="styled")
+
+        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            # test unstyled data cell does not have expected styles
+            # test styled cell has expected styles
+            u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2)
+        for attr in attrs:
+            u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr)
+
+        if isinstance(expected, dict):
+            assert u_cell is None or u_cell != expected[engine]
+            assert s_cell == expected[engine]
+        else:
+            assert u_cell is None or u_cell != expected
+            assert s_cell == expected
+
+
+@pytest.mark.parametrize(
+    "engine",
+    ["xlsxwriter", "openpyxl"],
+)
+@pytest.mark.parametrize("css, attrs, expected", shared_style_params)
+def test_styler_to_excel_basic_indexes(engine, css, attrs, expected):
+    pytest.importorskip(engine)
+    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
+
+    styler = df.style
+    styler.map_index(lambda x: css, axis=0)
+    styler.map_index(lambda x: css, axis=1)
+
+    null_styler = df.style
+    null_styler.map(lambda x: "null: css;")
+    null_styler.map_index(lambda x: "null: css;", axis=0)
+    null_styler.map_index(lambda x: "null: css;", axis=1)
+
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine=engine) as writer:
+            null_styler.to_excel(writer, sheet_name="null_styled")
+            styler.to_excel(writer, sheet_name="styled")
+
+        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            # test null styled index cells does not have expected styles
+            # test styled cell has expected styles
+            ui_cell, si_cell = wb["null_styled"].cell(2, 1), wb["styled"].cell(2, 1)
+            uc_cell, sc_cell = wb["null_styled"].cell(1, 2), wb["styled"].cell(1, 2)
+        for attr in attrs:
+            ui_cell, si_cell = getattr(ui_cell, attr, None), getattr(si_cell, attr)
+            uc_cell, sc_cell = getattr(uc_cell, attr, None), getattr(sc_cell, attr)
+
+        if isinstance(expected, dict):
+            assert ui_cell is None or ui_cell != expected[engine]
+            assert si_cell == expected[engine]
+            assert uc_cell is None or uc_cell != expected[engine]
+            assert sc_cell == expected[engine]
+        else:
+            assert ui_cell is None or ui_cell != expected
+            assert si_cell == expected
+            assert uc_cell is None or uc_cell != expected
+            assert sc_cell == expected
+
+
+# From https://openpyxl.readthedocs.io/en/stable/api/openpyxl.styles.borders.html
+# Note: Leaving behavior of "width"-type styles undefined; user should use border-width
+# instead
+excel_border_styles = [
+    # "thin",
+    "dashed",
+    "mediumDashDot",
+    "dashDotDot",
+    "hair",
+    "dotted",
+    "mediumDashDotDot",
+    # "medium",
+    "double",
+    "dashDot",
+    "slantDashDot",
+    # "thick",
+    "mediumDashed",
+]
+
+
+@pytest.mark.parametrize(
+    "engine",
+    ["xlsxwriter", "openpyxl"],
+)
+@pytest.mark.parametrize("border_style", excel_border_styles)
+def test_styler_to_excel_border_style(engine, border_style):
+    css = f"border-left: {border_style} black thin"
+    attrs = ["border", "left", "style"]
+    expected = border_style
+
+    pytest.importorskip(engine)
+    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
+    styler = df.style.map(lambda x: css)
+
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine=engine) as writer:
+            df.to_excel(writer, sheet_name="dataframe")
+            styler.to_excel(writer, sheet_name="styled")
+
+        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            # test unstyled data cell does not have expected styles
+            # test styled cell has expected styles
+            u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2)
+        for attr in attrs:
+            u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr)
+
+        if isinstance(expected, dict):
+            assert u_cell is None or u_cell != expected[engine]
+            assert s_cell == expected[engine]
+        else:
+            assert u_cell is None or u_cell != expected
+            assert s_cell == expected
+
+
+def test_styler_custom_converter():
+    openpyxl = pytest.importorskip("openpyxl")
+
+    def custom_converter(css):
+        return {"font": {"color": {"rgb": "111222"}}}
+
+    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
+    styler = df.style.map(lambda x: "color: #888999")
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine="openpyxl") as writer:
+            ExcelFormatter(styler, style_converter=custom_converter).write(
+                writer, sheet_name="custom"
+            )
+
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            assert wb["custom"].cell(2, 2).font.color.value == "00111222"
+
+
+@pytest.mark.single_cpu
+@td.skip_if_not_us_locale
+def test_styler_to_s3(s3_public_bucket, s3so):
+    # GH#46381
+
+    mock_bucket_name, target_file = s3_public_bucket.name, "test.xlsx"
+    df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]})
+    styler = df.style.set_sticky(axis="index")
+    styler.to_excel(f"s3://{mock_bucket_name}/{target_file}", storage_options=s3so)
+    timeout = 5
+    while True:
+        if target_file in (obj.key for obj in s3_public_bucket.objects.all()):
+            break
+        time.sleep(0.1)
+        timeout -= 0.1
+        assert timeout > 0, "Timed out waiting for file to appear on moto"
+        result = read_excel(
+            f"s3://{mock_bucket_name}/{target_file}", index_col=0, storage_options=s3so
+        )
+        tm.assert_frame_equal(result, df)

env-llmeval/lib/python3.10/site-packages/pandas/tests/io/excel/test_writers.py

@@ -0,0 +1,1511 @@
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+from functools import partial
+from io import BytesIO
+import os
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+from pandas.compat._constants import PY310
+from pandas.compat._optional import import_optional_dependency
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    date_range,
+    option_context,
+)
+import pandas._testing as tm
+
+from pandas.io.excel import (
+    ExcelFile,
+    ExcelWriter,
+    _OpenpyxlWriter,
+    _XlsxWriter,
+    register_writer,
+)
+from pandas.io.excel._util import _writers
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+def get_exp_unit(path: str) -> str:
+    return "ns"
+
+
+@pytest.fixture
+def frame(float_frame):
+    """
+    Returns the first ten items in fixture "float_frame".
+    """
+    return float_frame[:10]
+
+
+@pytest.fixture(params=[True, False])
+def merge_cells(request):
+    return request.param
+
+
+@pytest.fixture
+def path(ext):
+    """
+    Fixture to open file for use in each test case.
+    """
+    with tm.ensure_clean(ext) as file_path:
+        yield file_path
+
+
+@pytest.fixture
+def set_engine(engine, ext):
+    """
+    Fixture to set engine for use in each test case.
+
+    Rather than requiring `engine=...` to be provided explicitly as an
+    argument in each test, this fixture sets a global option to dictate
+    which engine should be used to write Excel files. After executing
+    the test it rolls back said change to the global option.
+    """
+    option_name = f"io.excel.{ext.strip('.')}.writer"
+    with option_context(option_name, engine):
+        yield
+
+
+@pytest.mark.parametrize(
+    "ext",
+    [
+        pytest.param(".xlsx", marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")]),
+        pytest.param(".xlsm", marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")]),
+        pytest.param(
+            ".xlsx", marks=[td.skip_if_no("xlsxwriter"), td.skip_if_no("xlrd")]
+        ),
+        pytest.param(".ods", marks=td.skip_if_no("odf")),
+    ],
+)
+class TestRoundTrip:
+    @pytest.mark.parametrize(
+        "header,expected",
+        [(None, DataFrame([np.nan] * 4)), (0, DataFrame({"Unnamed: 0": [np.nan] * 3}))],
+    )
+    def test_read_one_empty_col_no_header(self, ext, header, expected):
+        # xref gh-12292
+        filename = "no_header"
+        df = DataFrame([["", 1, 100], ["", 2, 200], ["", 3, 300], ["", 4, 400]])
+
+        with tm.ensure_clean(ext) as path:
+            df.to_excel(path, sheet_name=filename, index=False, header=False)
+            result = pd.read_excel(
+                path, sheet_name=filename, usecols=[0], header=header
+            )
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "header,expected",
+        [(None, DataFrame([0] + [np.nan] * 4)), (0, DataFrame([np.nan] * 4))],
+    )
+    def test_read_one_empty_col_with_header(self, ext, header, expected):
+        filename = "with_header"
+        df = DataFrame([["", 1, 100], ["", 2, 200], ["", 3, 300], ["", 4, 400]])
+
+        with tm.ensure_clean(ext) as path:
+            df.to_excel(path, sheet_name="with_header", index=False, header=True)
+            result = pd.read_excel(
+                path, sheet_name=filename, usecols=[0], header=header
+            )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_set_column_names_in_parameter(self, ext):
+        # GH 12870 : pass down column names associated with
+        # keyword argument names
+        refdf = DataFrame([[1, "foo"], [2, "bar"], [3, "baz"]], columns=["a", "b"])
+
+        with tm.ensure_clean(ext) as pth:
+            with ExcelWriter(pth) as writer:
+                refdf.to_excel(
+                    writer, sheet_name="Data_no_head", header=False, index=False
+                )
+                refdf.to_excel(writer, sheet_name="Data_with_head", index=False)
+
+            refdf.columns = ["A", "B"]
+
+            with ExcelFile(pth) as reader:
+                xlsdf_no_head = pd.read_excel(
+                    reader, sheet_name="Data_no_head", header=None, names=["A", "B"]
+                )
+                xlsdf_with_head = pd.read_excel(
+                    reader,
+                    sheet_name="Data_with_head",
+                    index_col=None,
+                    names=["A", "B"],
+                )
+
+            tm.assert_frame_equal(xlsdf_no_head, refdf)
+            tm.assert_frame_equal(xlsdf_with_head, refdf)
+
+    def test_creating_and_reading_multiple_sheets(self, ext):
+        # see gh-9450
+        #
+        # Test reading multiple sheets, from a runtime
+        # created Excel file with multiple sheets.
+        def tdf(col_sheet_name):
+            d, i = [11, 22, 33], [1, 2, 3]
+            return DataFrame(d, i, columns=[col_sheet_name])
+
+        sheets = ["AAA", "BBB", "CCC"]
+
+        dfs = [tdf(s) for s in sheets]
+        dfs = dict(zip(sheets, dfs))
+
+        with tm.ensure_clean(ext) as pth:
+            with ExcelWriter(pth) as ew:
+                for sheetname, df in dfs.items():
+                    df.to_excel(ew, sheet_name=sheetname)
+
+            dfs_returned = pd.read_excel(pth, sheet_name=sheets, index_col=0)
+
+            for s in sheets:
+                tm.assert_frame_equal(dfs[s], dfs_returned[s])
+
+    def test_read_excel_multiindex_empty_level(self, ext):
+        # see gh-12453
+        with tm.ensure_clean(ext) as path:
+            df = DataFrame(
+                {
+                    ("One", "x"): {0: 1},
+                    ("Two", "X"): {0: 3},
+                    ("Two", "Y"): {0: 7},
+                    ("Zero", ""): {0: 0},
+                }
+            )
+
+            expected = DataFrame(
+                {
+                    ("One", "x"): {0: 1},
+                    ("Two", "X"): {0: 3},
+                    ("Two", "Y"): {0: 7},
+                    ("Zero", "Unnamed: 4_level_1"): {0: 0},
+                }
+            )
+
+            df.to_excel(path)
+            actual = pd.read_excel(path, header=[0, 1], index_col=0)
+            tm.assert_frame_equal(actual, expected)
+
+            df = DataFrame(
+                {
+                    ("Beg", ""): {0: 0},
+                    ("Middle", "x"): {0: 1},
+                    ("Tail", "X"): {0: 3},
+                    ("Tail", "Y"): {0: 7},
+                }
+            )
+
+            expected = DataFrame(
+                {
+                    ("Beg", "Unnamed: 1_level_1"): {0: 0},
+                    ("Middle", "x"): {0: 1},
+                    ("Tail", "X"): {0: 3},
+                    ("Tail", "Y"): {0: 7},
+                }
+            )
+
+            df.to_excel(path)
+            actual = pd.read_excel(path, header=[0, 1], index_col=0)
+            tm.assert_frame_equal(actual, expected)
+
+    @pytest.mark.parametrize("c_idx_names", ["a", None])
+    @pytest.mark.parametrize("r_idx_names", ["b", None])
+    @pytest.mark.parametrize("c_idx_levels", [1, 3])
+    @pytest.mark.parametrize("r_idx_levels", [1, 3])
+    def test_excel_multindex_roundtrip(
+        self, ext, c_idx_names, r_idx_names, c_idx_levels, r_idx_levels, request
+    ):
+        # see gh-4679
+        with tm.ensure_clean(ext) as pth:
+            # Empty name case current read in as
+            # unnamed levels, not Nones.
+            check_names = bool(r_idx_names) or r_idx_levels <= 1
+
+            if c_idx_levels == 1:
+                columns = Index(list("abcde"))
+            else:
+                columns = MultiIndex.from_arrays(
+                    [range(5) for _ in range(c_idx_levels)],
+                    names=[f"{c_idx_names}-{i}" for i in range(c_idx_levels)],
+                )
+            if r_idx_levels == 1:
+                index = Index(list("ghijk"))
+            else:
+                index = MultiIndex.from_arrays(
+                    [range(5) for _ in range(r_idx_levels)],
+                    names=[f"{r_idx_names}-{i}" for i in range(r_idx_levels)],
+                )
+            df = DataFrame(
+                1.1 * np.ones((5, 5)),
+                columns=columns,
+                index=index,
+            )
+            df.to_excel(pth)
+
+            act = pd.read_excel(
+                pth,
+                index_col=list(range(r_idx_levels)),
+                header=list(range(c_idx_levels)),
+            )
+            tm.assert_frame_equal(df, act, check_names=check_names)
+
+            df.iloc[0, :] = np.nan
+            df.to_excel(pth)
+
+            act = pd.read_excel(
+                pth,
+                index_col=list(range(r_idx_levels)),
+                header=list(range(c_idx_levels)),
+            )
+            tm.assert_frame_equal(df, act, check_names=check_names)
+
+            df.iloc[-1, :] = np.nan
+            df.to_excel(pth)
+            act = pd.read_excel(
+                pth,
+                index_col=list(range(r_idx_levels)),
+                header=list(range(c_idx_levels)),
+            )
+            tm.assert_frame_equal(df, act, check_names=check_names)
+
+    def test_read_excel_parse_dates(self, ext):
+        # see gh-11544, gh-12051
+        df = DataFrame(
+            {"col": [1, 2, 3], "date_strings": date_range("2012-01-01", periods=3)}
+        )
+        df2 = df.copy()
+        df2["date_strings"] = df2["date_strings"].dt.strftime("%m/%d/%Y")
+
+        with tm.ensure_clean(ext) as pth:
+            df2.to_excel(pth)
+
+            res = pd.read_excel(pth, index_col=0)
+            tm.assert_frame_equal(df2, res)
+
+            res = pd.read_excel(pth, parse_dates=["date_strings"], index_col=0)
+            tm.assert_frame_equal(df, res)
+
+            date_parser = lambda x: datetime.strptime(x, "%m/%d/%Y")
+            with tm.assert_produces_warning(
+                FutureWarning,
+                match="use 'date_format' instead",
+                raise_on_extra_warnings=False,
+            ):
+                res = pd.read_excel(
+                    pth,
+                    parse_dates=["date_strings"],
+                    date_parser=date_parser,
+                    index_col=0,
+                )
+            tm.assert_frame_equal(df, res)
+            res = pd.read_excel(
+                pth, parse_dates=["date_strings"], date_format="%m/%d/%Y", index_col=0
+            )
+            tm.assert_frame_equal(df, res)
+
+    def test_multiindex_interval_datetimes(self, ext):
+        # GH 30986
+        midx = MultiIndex.from_arrays(
+            [
+                range(4),
+                pd.interval_range(
+                    start=pd.Timestamp("2020-01-01"), periods=4, freq="6ME"
+                ),
+            ]
+        )
+        df = DataFrame(range(4), index=midx)
+        with tm.ensure_clean(ext) as pth:
+            df.to_excel(pth)
+            result = pd.read_excel(pth, index_col=[0, 1])
+        expected = DataFrame(
+            range(4),
+            MultiIndex.from_arrays(
+                [
+                    range(4),
+                    [
+                        "(2020-01-31 00:00:00, 2020-07-31 00:00:00]",
+                        "(2020-07-31 00:00:00, 2021-01-31 00:00:00]",
+                        "(2021-01-31 00:00:00, 2021-07-31 00:00:00]",
+                        "(2021-07-31 00:00:00, 2022-01-31 00:00:00]",
+                    ],
+                ]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "engine,ext",
+    [
+        pytest.param(
+            "openpyxl",
+            ".xlsx",
+            marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")],
+        ),
+        pytest.param(
+            "openpyxl",
+            ".xlsm",
+            marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")],
+        ),
+        pytest.param(
+            "xlsxwriter",
+            ".xlsx",
+            marks=[td.skip_if_no("xlsxwriter"), td.skip_if_no("xlrd")],
+        ),
+        pytest.param("odf", ".ods", marks=td.skip_if_no("odf")),
+    ],
+)
+@pytest.mark.usefixtures("set_engine")
+class TestExcelWriter:
+    def test_excel_sheet_size(self, path):
+        # GH 26080
+        breaking_row_count = 2**20 + 1
+        breaking_col_count = 2**14 + 1
+        # purposely using two arrays to prevent memory issues while testing
+        row_arr = np.zeros(shape=(breaking_row_count, 1))
+        col_arr = np.zeros(shape=(1, breaking_col_count))
+        row_df = DataFrame(row_arr)
+        col_df = DataFrame(col_arr)
+
+        msg = "sheet is too large"
+        with pytest.raises(ValueError, match=msg):
+            row_df.to_excel(path)
+
+        with pytest.raises(ValueError, match=msg):
+            col_df.to_excel(path)
+
+    def test_excel_sheet_by_name_raise(self, path):
+        gt = DataFrame(np.random.default_rng(2).standard_normal((10, 2)))
+        gt.to_excel(path)
+
+        with ExcelFile(path) as xl:
+            df = pd.read_excel(xl, sheet_name=0, index_col=0)
+
+        tm.assert_frame_equal(gt, df)
+
+        msg = "Worksheet named '0' not found"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel(xl, "0")
+
+    def test_excel_writer_context_manager(self, frame, path):
+        with ExcelWriter(path) as writer:
+            frame.to_excel(writer, sheet_name="Data1")
+            frame2 = frame.copy()
+            frame2.columns = frame.columns[::-1]
+            frame2.to_excel(writer, sheet_name="Data2")
+
+        with ExcelFile(path) as reader:
+            found_df = pd.read_excel(reader, sheet_name="Data1", index_col=0)
+            found_df2 = pd.read_excel(reader, sheet_name="Data2", index_col=0)
+
+            tm.assert_frame_equal(found_df, frame)
+            tm.assert_frame_equal(found_df2, frame2)
+
+    def test_roundtrip(self, frame, path):
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+        # test roundtrip
+        frame.to_excel(path, sheet_name="test1")
+        recons = pd.read_excel(path, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(frame, recons)
+
+        frame.to_excel(path, sheet_name="test1", index=False)
+        recons = pd.read_excel(path, sheet_name="test1", index_col=None)
+        recons.index = frame.index
+        tm.assert_frame_equal(frame, recons)
+
+        frame.to_excel(path, sheet_name="test1", na_rep="NA")
+        recons = pd.read_excel(path, sheet_name="test1", index_col=0, na_values=["NA"])
+        tm.assert_frame_equal(frame, recons)
+
+        # GH 3611
+        frame.to_excel(path, sheet_name="test1", na_rep="88")
+        recons = pd.read_excel(path, sheet_name="test1", index_col=0, na_values=["88"])
+        tm.assert_frame_equal(frame, recons)
+
+        frame.to_excel(path, sheet_name="test1", na_rep="88")
+        recons = pd.read_excel(
+            path, sheet_name="test1", index_col=0, na_values=[88, 88.0]
+        )
+        tm.assert_frame_equal(frame, recons)
+
+        # GH 6573
+        frame.to_excel(path, sheet_name="Sheet1")
+        recons = pd.read_excel(path, index_col=0)
+        tm.assert_frame_equal(frame, recons)
+
+        frame.to_excel(path, sheet_name="0")
+        recons = pd.read_excel(path, index_col=0)
+        tm.assert_frame_equal(frame, recons)
+
+        # GH 8825 Pandas Series should provide to_excel method
+        s = frame["A"]
+        s.to_excel(path)
+        recons = pd.read_excel(path, index_col=0)
+        tm.assert_frame_equal(s.to_frame(), recons)
+
+    def test_mixed(self, frame, path):
+        mixed_frame = frame.copy()
+        mixed_frame["foo"] = "bar"
+
+        mixed_frame.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(mixed_frame, recons)
+
+    def test_ts_frame(self, path):
+        unit = get_exp_unit(path)
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+
+        # freq doesn't round-trip
+        index = pd.DatetimeIndex(np.asarray(df.index), freq=None)
+        df.index = index
+
+        expected = df[:]
+        expected.index = expected.index.as_unit(unit)
+
+        df.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_basics_with_nan(self, frame, path):
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+    @pytest.mark.parametrize("np_type", [np.int8, np.int16, np.int32, np.int64])
+    def test_int_types(self, np_type, path):
+        # Test np.int values read come back as int
+        # (rather than float which is Excel's format).
+        df = DataFrame(
+            np.random.default_rng(2).integers(-10, 10, size=(10, 2)), dtype=np_type
+        )
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        int_frame = df.astype(np.int64)
+        tm.assert_frame_equal(int_frame, recons)
+
+        recons2 = pd.read_excel(path, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(int_frame, recons2)
+
+    @pytest.mark.parametrize("np_type", [np.float16, np.float32, np.float64])
+    def test_float_types(self, np_type, path):
+        # Test np.float values read come back as float.
+        df = DataFrame(np.random.default_rng(2).random(10), dtype=np_type)
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np_type
+            )
+
+        tm.assert_frame_equal(df, recons)
+
+    def test_bool_types(self, path):
+        # Test np.bool_ values read come back as float.
+        df = DataFrame([1, 0, True, False], dtype=np.bool_)
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np.bool_
+            )
+
+        tm.assert_frame_equal(df, recons)
+
+    def test_inf_roundtrip(self, path):
+        df = DataFrame([(1, np.inf), (2, 3), (5, -np.inf)])
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        tm.assert_frame_equal(df, recons)
+
+    def test_sheets(self, frame, path):
+        # freq doesn't round-trip
+        unit = get_exp_unit(path)
+        tsframe = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+        index = pd.DatetimeIndex(np.asarray(tsframe.index), freq=None)
+        tsframe.index = index
+
+        expected = tsframe[:]
+        expected.index = expected.index.as_unit(unit)
+
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+        # Test writing to separate sheets
+        with ExcelWriter(path) as writer:
+            frame.to_excel(writer, sheet_name="test1")
+            tsframe.to_excel(writer, sheet_name="test2")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+            tm.assert_frame_equal(frame, recons)
+            recons = pd.read_excel(reader, sheet_name="test2", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+        assert 2 == len(reader.sheet_names)
+        assert "test1" == reader.sheet_names[0]
+        assert "test2" == reader.sheet_names[1]
+
+    def test_colaliases(self, frame, path):
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+        # column aliases
+        col_aliases = Index(["AA", "X", "Y", "Z"])
+        frame.to_excel(path, sheet_name="test1", header=col_aliases)
+        with ExcelFile(path) as reader:
+            rs = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        xp = frame.copy()
+        xp.columns = col_aliases
+        tm.assert_frame_equal(xp, rs)
+
+    def test_roundtrip_indexlabels(self, merge_cells, frame, path):
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+        # test index_label
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0
+        df.to_excel(
+            path, sheet_name="test1", index_label=["test"], merge_cells=merge_cells
+        )
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np.int64
+            )
+        df.index.names = ["test"]
+        assert df.index.names == recons.index.names
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0
+        df.to_excel(
+            path,
+            sheet_name="test1",
+            index_label=["test", "dummy", "dummy2"],
+            merge_cells=merge_cells,
+        )
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np.int64
+            )
+        df.index.names = ["test"]
+        assert df.index.names == recons.index.names
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0
+        df.to_excel(
+            path, sheet_name="test1", index_label="test", merge_cells=merge_cells
+        )
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np.int64
+            )
+        df.index.names = ["test"]
+        tm.assert_frame_equal(df, recons.astype(bool))
+
+        frame.to_excel(
+            path,
+            sheet_name="test1",
+            columns=["A", "B", "C", "D"],
+            index=False,
+            merge_cells=merge_cells,
+        )
+        # take 'A' and 'B' as indexes (same row as cols 'C', 'D')
+        df = frame.copy()
+        df = df.set_index(["A", "B"])
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1])
+        tm.assert_frame_equal(df, recons)
+
+    def test_excel_roundtrip_indexname(self, merge_cells, path):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+        df.index.name = "foo"
+
+        df.to_excel(path, merge_cells=merge_cells)
+
+        with ExcelFile(path) as xf:
+            result = pd.read_excel(xf, sheet_name=xf.sheet_names[0], index_col=0)
+
+        tm.assert_frame_equal(result, df)
+        assert result.index.name == "foo"
+
+    def test_excel_roundtrip_datetime(self, merge_cells, path):
+        # datetime.date, not sure what to test here exactly
+        unit = get_exp_unit(path)
+
+        # freq does not round-trip
+        tsframe = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+        index = pd.DatetimeIndex(np.asarray(tsframe.index), freq=None)
+        tsframe.index = index
+
+        tsf = tsframe.copy()
+
+        tsf.index = [x.date() for x in tsframe.index]
+        tsf.to_excel(path, sheet_name="test1", merge_cells=merge_cells)
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        expected = tsframe[:]
+        expected.index = expected.index.as_unit(unit)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_excel_date_datetime_format(self, ext, path):
+        # see gh-4133
+        #
+        # Excel output format strings
+        unit = get_exp_unit(path)
+
+        df = DataFrame(
+            [
+                [date(2014, 1, 31), date(1999, 9, 24)],
+                [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)],
+            ],
+            index=["DATE", "DATETIME"],
+            columns=["X", "Y"],
+        )
+        df_expected = DataFrame(
+            [
+                [datetime(2014, 1, 31), datetime(1999, 9, 24)],
+                [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)],
+            ],
+            index=["DATE", "DATETIME"],
+            columns=["X", "Y"],
+        )
+        df_expected = df_expected.astype(f"M8[{unit}]")
+
+        with tm.ensure_clean(ext) as filename2:
+            with ExcelWriter(path) as writer1:
+                df.to_excel(writer1, sheet_name="test1")
+
+            with ExcelWriter(
+                filename2,
+                date_format="DD.MM.YYYY",
+                datetime_format="DD.MM.YYYY HH-MM-SS",
+            ) as writer2:
+                df.to_excel(writer2, sheet_name="test1")
+
+            with ExcelFile(path) as reader1:
+                rs1 = pd.read_excel(reader1, sheet_name="test1", index_col=0)
+
+            with ExcelFile(filename2) as reader2:
+                rs2 = pd.read_excel(reader2, sheet_name="test1", index_col=0)
+
+        tm.assert_frame_equal(rs1, rs2)
+
+        # Since the reader returns a datetime object for dates,
+        # we need to use df_expected to check the result.
+        tm.assert_frame_equal(rs2, df_expected)
+
+    def test_to_excel_interval_no_labels(self, path, using_infer_string):
+        # see gh-19242
+        #
+        # Test writing Interval without labels.
+        df = DataFrame(
+            np.random.default_rng(2).integers(-10, 10, size=(20, 1)), dtype=np.int64
+        )
+        expected = df.copy()
+
+        df["new"] = pd.cut(df[0], 10)
+        expected["new"] = pd.cut(expected[0], 10).astype(
+            str if not using_infer_string else "string[pyarrow_numpy]"
+        )
+
+        df.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_to_excel_interval_labels(self, path):
+        # see gh-19242
+        #
+        # Test writing Interval with labels.
+        df = DataFrame(
+            np.random.default_rng(2).integers(-10, 10, size=(20, 1)), dtype=np.int64
+        )
+        expected = df.copy()
+        intervals = pd.cut(
+            df[0], 10, labels=["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"]
+        )
+        df["new"] = intervals
+        expected["new"] = pd.Series(list(intervals))
+
+        df.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_to_excel_timedelta(self, path):
+        # see gh-19242, gh-9155
+        #
+        # Test writing timedelta to xls.
+        df = DataFrame(
+            np.random.default_rng(2).integers(-10, 10, size=(20, 1)),
+            columns=["A"],
+            dtype=np.int64,
+        )
+        expected = df.copy()
+
+        df["new"] = df["A"].apply(lambda x: timedelta(seconds=x))
+        expected["new"] = expected["A"].apply(
+            lambda x: timedelta(seconds=x).total_seconds() / 86400
+        )
+
+        df.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_to_excel_periodindex(self, path):
+        # xp has a PeriodIndex
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+        xp = df.resample("ME").mean().to_period("M")
+
+        xp.to_excel(path, sheet_name="sht1")
+
+        with ExcelFile(path) as reader:
+            rs = pd.read_excel(reader, sheet_name="sht1", index_col=0)
+        tm.assert_frame_equal(xp, rs.to_period("M"))
+
+    def test_to_excel_multiindex(self, merge_cells, frame, path):
+        arrays = np.arange(len(frame.index) * 2, dtype=np.int64).reshape(2, -1)
+        new_index = MultiIndex.from_arrays(arrays, names=["first", "second"])
+        frame.index = new_index
+
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+
+        # round trip
+        frame.to_excel(path, sheet_name="test1", merge_cells=merge_cells)
+        with ExcelFile(path) as reader:
+            df = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1])
+        tm.assert_frame_equal(frame, df)
+
+    # GH13511
+    def test_to_excel_multiindex_nan_label(self, merge_cells, path):
+        df = DataFrame(
+            {
+                "A": [None, 2, 3],
+                "B": [10, 20, 30],
+                "C": np.random.default_rng(2).random(3),
+            }
+        )
+        df = df.set_index(["A", "B"])
+
+        df.to_excel(path, merge_cells=merge_cells)
+        df1 = pd.read_excel(path, index_col=[0, 1])
+        tm.assert_frame_equal(df, df1)
+
+    # Test for Issue 11328. If column indices are integers, make
+    # sure they are handled correctly for either setting of
+    # merge_cells
+    def test_to_excel_multiindex_cols(self, merge_cells, frame, path):
+        arrays = np.arange(len(frame.index) * 2, dtype=np.int64).reshape(2, -1)
+        new_index = MultiIndex.from_arrays(arrays, names=["first", "second"])
+        frame.index = new_index
+
+        new_cols_index = MultiIndex.from_tuples([(40, 1), (40, 2), (50, 1), (50, 2)])
+        frame.columns = new_cols_index
+        header = [0, 1]
+        if not merge_cells:
+            header = 0
+
+        # round trip
+        frame.to_excel(path, sheet_name="test1", merge_cells=merge_cells)
+        with ExcelFile(path) as reader:
+            df = pd.read_excel(
+                reader, sheet_name="test1", header=header, index_col=[0, 1]
+            )
+        if not merge_cells:
+            fm = frame.columns._format_multi(sparsify=False, include_names=False)
+            frame.columns = [".".join(map(str, q)) for q in zip(*fm)]
+        tm.assert_frame_equal(frame, df)
+
+    def test_to_excel_multiindex_dates(self, merge_cells, path):
+        # try multiindex with dates
+        unit = get_exp_unit(path)
+        tsframe = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+        tsframe.index = MultiIndex.from_arrays(
+            [
+                tsframe.index.as_unit(unit),
+                np.arange(len(tsframe.index), dtype=np.int64),
+            ],
+            names=["time", "foo"],
+        )
+
+        tsframe.to_excel(path, sheet_name="test1", merge_cells=merge_cells)
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1])
+
+        tm.assert_frame_equal(tsframe, recons)
+        assert recons.index.names == ("time", "foo")
+
+    def test_to_excel_multiindex_no_write_index(self, path):
+        # Test writing and re-reading a MI without the index. GH 5616.
+
+        # Initial non-MI frame.
+        frame1 = DataFrame({"a": [10, 20], "b": [30, 40], "c": [50, 60]})
+
+        # Add a MI.
+        frame2 = frame1.copy()
+        multi_index = MultiIndex.from_tuples([(70, 80), (90, 100)])
+        frame2.index = multi_index
+
+        # Write out to Excel without the index.
+        frame2.to_excel(path, sheet_name="test1", index=False)
+
+        # Read it back in.
+        with ExcelFile(path) as reader:
+            frame3 = pd.read_excel(reader, sheet_name="test1")
+
+        # Test that it is the same as the initial frame.
+        tm.assert_frame_equal(frame1, frame3)
+
+    def test_to_excel_empty_multiindex(self, path):
+        # GH 19543.
+        expected = DataFrame([], columns=[0, 1, 2])
+
+        df = DataFrame([], index=MultiIndex.from_tuples([], names=[0, 1]), columns=[2])
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            result = pd.read_excel(reader, sheet_name="test1")
+        tm.assert_frame_equal(
+            result, expected, check_index_type=False, check_dtype=False
+        )
+
+    def test_to_excel_float_format(self, path):
+        df = DataFrame(
+            [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
+            index=["A", "B"],
+            columns=["X", "Y", "Z"],
+        )
+        df.to_excel(path, sheet_name="test1", float_format="%.2f")
+
+        with ExcelFile(path) as reader:
+            result = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        expected = DataFrame(
+            [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]],
+            index=["A", "B"],
+            columns=["X", "Y", "Z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_to_excel_output_encoding(self, ext):
+        # Avoid mixed inferred_type.
+        df = DataFrame(
+            [["\u0192", "\u0193", "\u0194"], ["\u0195", "\u0196", "\u0197"]],
+            index=["A\u0192", "B"],
+            columns=["X\u0193", "Y", "Z"],
+        )
+
+        with tm.ensure_clean("__tmp_to_excel_float_format__." + ext) as filename:
+            df.to_excel(filename, sheet_name="TestSheet")
+            result = pd.read_excel(filename, sheet_name="TestSheet", index_col=0)
+            tm.assert_frame_equal(result, df)
+
+    def test_to_excel_unicode_filename(self, ext):
+        with tm.ensure_clean("\u0192u." + ext) as filename:
+            try:
+                with open(filename, "wb"):
+                    pass
+            except UnicodeEncodeError:
+                pytest.skip("No unicode file names on this system")
+
+            df = DataFrame(
+                [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
+                index=["A", "B"],
+                columns=["X", "Y", "Z"],
+            )
+            df.to_excel(filename, sheet_name="test1", float_format="%.2f")
+
+            with ExcelFile(filename) as reader:
+                result = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        expected = DataFrame(
+            [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]],
+            index=["A", "B"],
+            columns=["X", "Y", "Z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("use_headers", [True, False])
+    @pytest.mark.parametrize("r_idx_nlevels", [1, 2, 3])
+    @pytest.mark.parametrize("c_idx_nlevels", [1, 2, 3])
+    def test_excel_010_hemstring(
+        self, merge_cells, c_idx_nlevels, r_idx_nlevels, use_headers, path
+    ):
+        def roundtrip(data, header=True, parser_hdr=0, index=True):
+            data.to_excel(path, header=header, merge_cells=merge_cells, index=index)
+
+            with ExcelFile(path) as xf:
+                return pd.read_excel(
+                    xf, sheet_name=xf.sheet_names[0], header=parser_hdr
+                )
+
+        # Basic test.
+        parser_header = 0 if use_headers else None
+        res = roundtrip(DataFrame([0]), use_headers, parser_header)
+
+        assert res.shape == (1, 2)
+        assert res.iloc[0, 0] is not np.nan
+
+        # More complex tests with multi-index.
+        nrows = 5
+        ncols = 3
+
+        # ensure limited functionality in 0.10
+        # override of gh-2370 until sorted out in 0.11
+
+        if c_idx_nlevels == 1:
+            columns = Index([f"a-{i}" for i in range(ncols)], dtype=object)
+        else:
+            columns = MultiIndex.from_arrays(
+                [range(ncols) for _ in range(c_idx_nlevels)],
+                names=[f"i-{i}" for i in range(c_idx_nlevels)],
+            )
+        if r_idx_nlevels == 1:
+            index = Index([f"b-{i}" for i in range(nrows)], dtype=object)
+        else:
+            index = MultiIndex.from_arrays(
+                [range(nrows) for _ in range(r_idx_nlevels)],
+                names=[f"j-{i}" for i in range(r_idx_nlevels)],
+            )
+
+        df = DataFrame(
+            np.ones((nrows, ncols)),
+            columns=columns,
+            index=index,
+        )
+
+        # This if will be removed once multi-column Excel writing
+        # is implemented. For now fixing gh-9794.
+        if c_idx_nlevels > 1:
+            msg = (
+                "Writing to Excel with MultiIndex columns and no index "
+                "\\('index'=False\\) is not yet implemented."
+            )
+            with pytest.raises(NotImplementedError, match=msg):
+                roundtrip(df, use_headers, index=False)
+        else:
+            res = roundtrip(df, use_headers)
+
+            if use_headers:
+                assert res.shape == (nrows, ncols + r_idx_nlevels)
+            else:
+                # First row taken as columns.
+                assert res.shape == (nrows - 1, ncols + r_idx_nlevels)
+
+            # No NaNs.
+            for r in range(len(res.index)):
+                for c in range(len(res.columns)):
+                    assert res.iloc[r, c] is not np.nan
+
+    def test_duplicated_columns(self, path):
+        # see gh-5235
+        df = DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3]], columns=["A", "B", "B"])
+        df.to_excel(path, sheet_name="test1")
+        expected = DataFrame(
+            [[1, 2, 3], [1, 2, 3], [1, 2, 3]], columns=["A", "B", "B.1"]
+        )
+
+        # By default, we mangle.
+        result = pd.read_excel(path, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(result, expected)
+
+        # see gh-11007, gh-10970
+        df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "A", "B"])
+        df.to_excel(path, sheet_name="test1")
+
+        result = pd.read_excel(path, sheet_name="test1", index_col=0)
+        expected = DataFrame(
+            [[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "A.1", "B.1"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # see gh-10982
+        df.to_excel(path, sheet_name="test1", index=False, header=False)
+        result = pd.read_excel(path, sheet_name="test1", header=None)
+
+        expected = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]])
+        tm.assert_frame_equal(result, expected)
+
+    def test_swapped_columns(self, path):
+        # Test for issue #5427.
+        write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2]})
+        write_frame.to_excel(path, sheet_name="test1", columns=["B", "A"])
+
+        read_frame = pd.read_excel(path, sheet_name="test1", header=0)
+
+        tm.assert_series_equal(write_frame["A"], read_frame["A"])
+        tm.assert_series_equal(write_frame["B"], read_frame["B"])
+
+    def test_invalid_columns(self, path):
+        # see gh-10982
+        write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2]})
+
+        with pytest.raises(KeyError, match="Not all names specified"):
+            write_frame.to_excel(path, sheet_name="test1", columns=["B", "C"])
+
+        with pytest.raises(
+            KeyError, match="'passes columns are not ALL present dataframe'"
+        ):
+            write_frame.to_excel(path, sheet_name="test1", columns=["C", "D"])
+
+    @pytest.mark.parametrize(
+        "to_excel_index,read_excel_index_col",
+        [
+            (True, 0),  # Include index in write to file
+            (False, None),  # Dont include index in write to file
+        ],
+    )
+    def test_write_subset_columns(self, path, to_excel_index, read_excel_index_col):
+        # GH 31677
+        write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2], "C": [3, 3, 3]})
+        write_frame.to_excel(
+            path, sheet_name="col_subset_bug", columns=["A", "B"], index=to_excel_index
+        )
+
+        expected = write_frame[["A", "B"]]
+        read_frame = pd.read_excel(
+            path, sheet_name="col_subset_bug", index_col=read_excel_index_col
+        )
+
+        tm.assert_frame_equal(expected, read_frame)
+
+    def test_comment_arg(self, path):
+        # see gh-18735
+        #
+        # Test the comment argument functionality to pd.read_excel.
+
+        # Create file to read in.
+        df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]})
+        df.to_excel(path, sheet_name="test_c")
+
+        # Read file without comment arg.
+        result1 = pd.read_excel(path, sheet_name="test_c", index_col=0)
+
+        result1.iloc[1, 0] = None
+        result1.iloc[1, 1] = None
+        result1.iloc[2, 1] = None
+
+        result2 = pd.read_excel(path, sheet_name="test_c", comment="#", index_col=0)
+        tm.assert_frame_equal(result1, result2)
+
+    def test_comment_default(self, path):
+        # Re issue #18735
+        # Test the comment argument default to pd.read_excel
+
+        # Create file to read in
+        df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]})
+        df.to_excel(path, sheet_name="test_c")
+
+        # Read file with default and explicit comment=None
+        result1 = pd.read_excel(path, sheet_name="test_c")
+        result2 = pd.read_excel(path, sheet_name="test_c", comment=None)
+        tm.assert_frame_equal(result1, result2)
+
+    def test_comment_used(self, path):
+        # see gh-18735
+        #
+        # Test the comment argument is working as expected when used.
+
+        # Create file to read in.
+        df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]})
+        df.to_excel(path, sheet_name="test_c")
+
+        # Test read_frame_comment against manually produced expected output.
+        expected = DataFrame({"A": ["one", None, "one"], "B": ["two", None, None]})
+        result = pd.read_excel(path, sheet_name="test_c", comment="#", index_col=0)
+        tm.assert_frame_equal(result, expected)
+
+    def test_comment_empty_line(self, path):
+        # Re issue #18735
+        # Test that pd.read_excel ignores commented lines at the end of file
+
+        df = DataFrame({"a": ["1", "#2"], "b": ["2", "3"]})
+        df.to_excel(path, index=False)
+
+        # Test that all-comment lines at EoF are ignored
+        expected = DataFrame({"a": [1], "b": [2]})
+        result = pd.read_excel(path, comment="#")
+        tm.assert_frame_equal(result, expected)
+
+    def test_datetimes(self, path):
+        # Test writing and reading datetimes. For issue #9139. (xref #9185)
+        unit = get_exp_unit(path)
+        datetimes = [
+            datetime(2013, 1, 13, 1, 2, 3),
+            datetime(2013, 1, 13, 2, 45, 56),
+            datetime(2013, 1, 13, 4, 29, 49),
+            datetime(2013, 1, 13, 6, 13, 42),
+            datetime(2013, 1, 13, 7, 57, 35),
+            datetime(2013, 1, 13, 9, 41, 28),
+            datetime(2013, 1, 13, 11, 25, 21),
+            datetime(2013, 1, 13, 13, 9, 14),
+            datetime(2013, 1, 13, 14, 53, 7),
+            datetime(2013, 1, 13, 16, 37, 0),
+            datetime(2013, 1, 13, 18, 20, 52),
+        ]
+
+        write_frame = DataFrame({"A": datetimes})
+        write_frame.to_excel(path, sheet_name="Sheet1")
+        read_frame = pd.read_excel(path, sheet_name="Sheet1", header=0)
+
+        expected = write_frame.astype(f"M8[{unit}]")
+        tm.assert_series_equal(expected["A"], read_frame["A"])
+
+    def test_bytes_io(self, engine):
+        # see gh-7074
+        with BytesIO() as bio:
+            df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)))
+
+            # Pass engine explicitly, as there is no file path to infer from.
+            with ExcelWriter(bio, engine=engine) as writer:
+                df.to_excel(writer)
+
+            bio.seek(0)
+            reread_df = pd.read_excel(bio, index_col=0)
+            tm.assert_frame_equal(df, reread_df)
+
+    def test_engine_kwargs(self, engine, path):
+        # GH#52368
+        df = DataFrame([{"A": 1, "B": 2}, {"A": 3, "B": 4}])
+
+        msgs = {
+            "odf": r"OpenDocumentSpreadsheet() got an unexpected keyword "
+            r"argument 'foo'",
+            "openpyxl": r"__init__() got an unexpected keyword argument 'foo'",
+            "xlsxwriter": r"__init__() got an unexpected keyword argument 'foo'",
+        }
+
+        if PY310:
+            msgs[
+                "openpyxl"
+            ] = "Workbook.__init__() got an unexpected keyword argument 'foo'"
+            msgs[
+                "xlsxwriter"
+            ] = "Workbook.__init__() got an unexpected keyword argument 'foo'"
+
+        # Handle change in error message for openpyxl (write and append mode)
+        if engine == "openpyxl" and not os.path.exists(path):
+            msgs[
+                "openpyxl"
+            ] = r"load_workbook() got an unexpected keyword argument 'foo'"
+
+        with pytest.raises(TypeError, match=re.escape(msgs[engine])):
+            df.to_excel(
+                path,
+                engine=engine,
+                engine_kwargs={"foo": "bar"},
+            )
+
+    def test_write_lists_dict(self, path):
+        # see gh-8188.
+        df = DataFrame(
+            {
+                "mixed": ["a", ["b", "c"], {"d": "e", "f": 2}],
+                "numeric": [1, 2, 3.0],
+                "str": ["apple", "banana", "cherry"],
+            }
+        )
+        df.to_excel(path, sheet_name="Sheet1")
+        read = pd.read_excel(path, sheet_name="Sheet1", header=0, index_col=0)
+
+        expected = df.copy()
+        expected.mixed = expected.mixed.apply(str)
+        expected.numeric = expected.numeric.astype("int64")
+
+        tm.assert_frame_equal(read, expected)
+
+    def test_render_as_column_name(self, path):
+        # see gh-34331
+        df = DataFrame({"render": [1, 2], "data": [3, 4]})
+        df.to_excel(path, sheet_name="Sheet1")
+        read = pd.read_excel(path, "Sheet1", index_col=0)
+        expected = df
+        tm.assert_frame_equal(read, expected)
+
+    def test_true_and_false_value_options(self, path):
+        # see gh-13347
+        df = DataFrame([["foo", "bar"]], columns=["col1", "col2"], dtype=object)
+        with option_context("future.no_silent_downcasting", True):
+            expected = df.replace({"foo": True, "bar": False}).astype("bool")
+
+        df.to_excel(path)
+        read_frame = pd.read_excel(
+            path, true_values=["foo"], false_values=["bar"], index_col=0
+        )
+        tm.assert_frame_equal(read_frame, expected)
+
+    def test_freeze_panes(self, path):
+        # see gh-15160
+        expected = DataFrame([[1, 2], [3, 4]], columns=["col1", "col2"])
+        expected.to_excel(path, sheet_name="Sheet1", freeze_panes=(1, 1))
+
+        result = pd.read_excel(path, index_col=0)
+        tm.assert_frame_equal(result, expected)
+
+    def test_path_path_lib(self, engine, ext):
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD")),
+            index=Index([f"i-{i}" for i in range(30)], dtype=object),
+        )
+        writer = partial(df.to_excel, engine=engine)
+
+        reader = partial(pd.read_excel, index_col=0)
+        result = tm.round_trip_pathlib(writer, reader, path=f"foo{ext}")
+        tm.assert_frame_equal(result, df)
+
+    def test_path_local_path(self, engine, ext):
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD")),
+            index=Index([f"i-{i}" for i in range(30)]),
+        )
+        writer = partial(df.to_excel, engine=engine)
+
+        reader = partial(pd.read_excel, index_col=0)
+        result = tm.round_trip_localpath(writer, reader, path=f"foo{ext}")
+        tm.assert_frame_equal(result, df)
+
+    def test_merged_cell_custom_objects(self, path):
+        # see GH-27006
+        mi = MultiIndex.from_tuples(
+            [
+                (pd.Period("2018"), pd.Period("2018Q1")),
+                (pd.Period("2018"), pd.Period("2018Q2")),
+            ]
+        )
+        expected = DataFrame(np.ones((2, 2), dtype="int64"), columns=mi)
+        expected.to_excel(path)
+        result = pd.read_excel(path, header=[0, 1], index_col=0)
+        # need to convert PeriodIndexes to standard Indexes for assert equal
+        expected.columns = expected.columns.set_levels(
+            [[str(i) for i in mi.levels[0]], [str(i) for i in mi.levels[1]]],
+            level=[0, 1],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_raise_when_saving_timezones(self, dtype, tz_aware_fixture, path):
+        # GH 27008, GH 7056
+        tz = tz_aware_fixture
+        data = pd.Timestamp("2019", tz=tz)
+        df = DataFrame([data], dtype=dtype)
+        with pytest.raises(ValueError, match="Excel does not support"):
+            df.to_excel(path)
+
+        data = data.to_pydatetime()
+        df = DataFrame([data], dtype=dtype)
+        with pytest.raises(ValueError, match="Excel does not support"):
+            df.to_excel(path)
+
+    def test_excel_duplicate_columns_with_names(self, path):
+        # GH#39695
+        df = DataFrame({"A": [0, 1], "B": [10, 11]})
+        df.to_excel(path, columns=["A", "B", "A"], index=False)
+
+        result = pd.read_excel(path)
+        expected = DataFrame([[0, 10, 0], [1, 11, 1]], columns=["A", "B", "A.1"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_if_sheet_exists_raises(self, ext):
+        # GH 40230
+        msg = "if_sheet_exists is only valid in append mode (mode='a')"
+
+        with tm.ensure_clean(ext) as f:
+            with pytest.raises(ValueError, match=re.escape(msg)):
+                ExcelWriter(f, if_sheet_exists="replace")
+
+    def test_excel_writer_empty_frame(self, engine, ext):
+        # GH#45793
+        with tm.ensure_clean(ext) as path:
+            with ExcelWriter(path, engine=engine) as writer:
+                DataFrame().to_excel(writer)
+            result = pd.read_excel(path)
+            expected = DataFrame()
+            tm.assert_frame_equal(result, expected)
+
+    def test_to_excel_empty_frame(self, engine, ext):
+        # GH#45793
+        with tm.ensure_clean(ext) as path:
+            DataFrame().to_excel(path, engine=engine)
+            result = pd.read_excel(path)
+            expected = DataFrame()
+            tm.assert_frame_equal(result, expected)
+
+
+class TestExcelWriterEngineTests:
+    @pytest.mark.parametrize(
+        "klass,ext",
+        [
+            pytest.param(_XlsxWriter, ".xlsx", marks=td.skip_if_no("xlsxwriter")),
+            pytest.param(_OpenpyxlWriter, ".xlsx", marks=td.skip_if_no("openpyxl")),
+        ],
+    )
+    def test_ExcelWriter_dispatch(self, klass, ext):
+        with tm.ensure_clean(ext) as path:
+            with ExcelWriter(path) as writer:
+                if ext == ".xlsx" and bool(
+                    import_optional_dependency("xlsxwriter", errors="ignore")
+                ):
+                    # xlsxwriter has preference over openpyxl if both installed
+                    assert isinstance(writer, _XlsxWriter)
+                else:
+                    assert isinstance(writer, klass)
+
+    def test_ExcelWriter_dispatch_raises(self):
+        with pytest.raises(ValueError, match="No engine"):
+            ExcelWriter("nothing")
+
+    def test_register_writer(self):
+        class DummyClass(ExcelWriter):
+            called_save = False
+            called_write_cells = False
+            called_sheets = False
+            _supported_extensions = ("xlsx", "xls")
+            _engine = "dummy"
+
+            def book(self):
+                pass
+
+            def _save(self):
+                type(self).called_save = True
+
+            def _write_cells(self, *args, **kwargs):
+                type(self).called_write_cells = True
+
+            @property
+            def sheets(self):
+                type(self).called_sheets = True
+
+            @classmethod
+            def assert_called_and_reset(cls):
+                assert cls.called_save
+                assert cls.called_write_cells
+                assert not cls.called_sheets
+                cls.called_save = False
+                cls.called_write_cells = False
+
+        register_writer(DummyClass)
+
+        with option_context("io.excel.xlsx.writer", "dummy"):
+            path = "something.xlsx"
+            with tm.ensure_clean(path) as filepath:
+                with ExcelWriter(filepath) as writer:
+                    assert isinstance(writer, DummyClass)
+                df = DataFrame(
+                    ["a"],
+                    columns=Index(["b"], name="foo"),
+                    index=Index(["c"], name="bar"),
+                )
+                df.to_excel(filepath)
+            DummyClass.assert_called_and_reset()
+
+        with tm.ensure_clean("something.xls") as filepath:
+            df.to_excel(filepath, engine="dummy")
+        DummyClass.assert_called_and_reset()
+
+
+@td.skip_if_no("xlrd")
+@td.skip_if_no("openpyxl")
+class TestFSPath:
+    def test_excelfile_fspath(self):
+        with tm.ensure_clean("foo.xlsx") as path:
+            df = DataFrame({"A": [1, 2]})
+            df.to_excel(path)
+            with ExcelFile(path) as xl:
+                result = os.fspath(xl)
+            assert result == path
+
+    def test_excelwriter_fspath(self):
+        with tm.ensure_clean("foo.xlsx") as path:
+            with ExcelWriter(path) as writer:
+                assert os.fspath(writer) == str(path)
+
+    def test_to_excel_pos_args_deprecation(self):
+        # GH-54229
+        df = DataFrame({"a": [1, 2, 3]})
+        msg = (
+            r"Starting with pandas version 3.0 all arguments of to_excel except "
+            r"for the argument 'excel_writer' will be keyword-only."
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            buf = BytesIO()
+            writer = ExcelWriter(buf)
+            df.to_excel(writer, "Sheet_name_1")
+
+
+@pytest.mark.parametrize("klass", _writers.values())
+def test_subclass_attr(klass):
+    # testing that subclasses of ExcelWriter don't have public attributes (issue 49602)
+    attrs_base = {name for name in dir(ExcelWriter) if not name.startswith("_")}
+    attrs_klass = {name for name in dir(klass) if not name.startswith("_")}
+    assert not attrs_base.symmetric_difference(attrs_klass)

env-llmeval/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlrd.py

@@ -0,0 +1,76 @@
+import io
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+import pandas._testing as tm
+
+from pandas.io.excel import ExcelFile
+from pandas.io.excel._base import inspect_excel_format
+
+xlrd = pytest.importorskip("xlrd")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture(params=[".xls"])
+def read_ext_xlrd(request):
+    """
+    Valid extensions for reading Excel files with xlrd.
+
+    Similar to read_ext, but excludes .ods, .xlsb, and for xlrd>2 .xlsx, .xlsm
+    """
+    return request.param
+
+
+def test_read_xlrd_book(read_ext_xlrd, datapath):
+    engine = "xlrd"
+    sheet_name = "Sheet1"
+    pth = datapath("io", "data", "excel", "test1.xls")
+    with xlrd.open_workbook(pth) as book:
+        with ExcelFile(book, engine=engine) as xl:
+            result = pd.read_excel(xl, sheet_name=sheet_name, index_col=0)
+
+        expected = pd.read_excel(
+            book, sheet_name=sheet_name, engine=engine, index_col=0
+        )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_xlsx_fails(datapath):
+    # GH 29375
+    from xlrd.biffh import XLRDError
+
+    path = datapath("io", "data", "excel", "test1.xlsx")
+    with pytest.raises(XLRDError, match="Excel xlsx file; not supported"):
+        pd.read_excel(path, engine="xlrd")
+
+
+def test_nan_in_xls(datapath):
+    # GH 54564
+    path = datapath("io", "data", "excel", "test6.xls")
+
+    expected = pd.DataFrame({0: np.r_[0, 2].astype("int64"), 1: np.r_[1, np.nan]})
+
+    result = pd.read_excel(path, header=None)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "file_header",
+    [
+        b"\x09\x00\x04\x00\x07\x00\x10\x00",
+        b"\x09\x02\x06\x00\x00\x00\x10\x00",
+        b"\x09\x04\x06\x00\x00\x00\x10\x00",
+        b"\xd0\xcf\x11\xe0\xa1\xb1\x1a\xe1",
+    ],
+)
+def test_read_old_xls_files(file_header):
+    # GH 41226
+    f = io.BytesIO(file_header)
+    assert inspect_excel_format(f) == "xls"

env-llmeval/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlsxwriter.py

@@ -0,0 +1,86 @@
+import contextlib
+
+import pytest
+
+from pandas.compat import is_platform_windows
+
+from pandas import DataFrame
+import pandas._testing as tm
+
+from pandas.io.excel import ExcelWriter
+
+xlsxwriter = pytest.importorskip("xlsxwriter")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture
+def ext():
+    return ".xlsx"
+
+
+def test_column_format(ext):
+    # Test that column formats are applied to cells. Test for issue #9167.
+    # Applicable to xlsxwriter only.
+    openpyxl = pytest.importorskip("openpyxl")
+
+    with tm.ensure_clean(ext) as path:
+        frame = DataFrame({"A": [123456, 123456], "B": [123456, 123456]})
+
+        with ExcelWriter(path) as writer:
+            frame.to_excel(writer)
+
+            # Add a number format to col B and ensure it is applied to cells.
+            num_format = "#,##0"
+            write_workbook = writer.book
+            write_worksheet = write_workbook.worksheets()[0]
+            col_format = write_workbook.add_format({"num_format": num_format})
+            write_worksheet.set_column("B:B", None, col_format)
+
+        with contextlib.closing(openpyxl.load_workbook(path)) as read_workbook:
+            try:
+                read_worksheet = read_workbook["Sheet1"]
+            except TypeError:
+                # compat
+                read_worksheet = read_workbook.get_sheet_by_name(name="Sheet1")
+
+        # Get the number format from the cell.
+        try:
+            cell = read_worksheet["B2"]
+        except TypeError:
+            # compat
+            cell = read_worksheet.cell("B2")
+
+        try:
+            read_num_format = cell.number_format
+        except AttributeError:
+            read_num_format = cell.style.number_format._format_code
+
+        assert read_num_format == num_format
+
+
+def test_write_append_mode_raises(ext):
+    msg = "Append mode is not supported with xlsxwriter!"
+
+    with tm.ensure_clean(ext) as f:
+        with pytest.raises(ValueError, match=msg):
+            ExcelWriter(f, engine="xlsxwriter", mode="a")
+
+
+@pytest.mark.parametrize("nan_inf_to_errors", [True, False])
+def test_engine_kwargs(ext, nan_inf_to_errors):
+    # GH 42286
+    engine_kwargs = {"options": {"nan_inf_to_errors": nan_inf_to_errors}}
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f, engine="xlsxwriter", engine_kwargs=engine_kwargs) as writer:
+            assert writer.book.nan_inf_to_errors == nan_inf_to_errors
+
+
+def test_book_and_sheets_consistent(ext):
+    # GH#45687 - Ensure sheets is updated if user modifies book
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f, engine="xlsxwriter") as writer:
+            assert writer.sheets == {}
+            sheet = writer.book.add_worksheet("test_name")
+            assert writer.sheets == {"test_name": sheet}