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vllm/lib/python3.10/site-packages/pandas/tests/computation/test_eval.py

@@ -0,0 +1,2001 @@
+from __future__ import annotations
+
+from functools import reduce
+from itertools import product
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.compat import PY312
+from pandas.errors import (
+    NumExprClobberingError,
+    PerformanceWarning,
+    UndefinedVariableError,
+)
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_float,
+    is_list_like,
+    is_scalar,
+)
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.computation import (
+    expr,
+    pytables,
+)
+from pandas.core.computation.engines import ENGINES
+from pandas.core.computation.expr import (
+    BaseExprVisitor,
+    PandasExprVisitor,
+    PythonExprVisitor,
+)
+from pandas.core.computation.expressions import (
+    NUMEXPR_INSTALLED,
+    USE_NUMEXPR,
+)
+from pandas.core.computation.ops import (
+    ARITH_OPS_SYMS,
+    SPECIAL_CASE_ARITH_OPS_SYMS,
+    _binary_math_ops,
+    _binary_ops_dict,
+    _unary_math_ops,
+)
+from pandas.core.computation.scope import DEFAULT_GLOBALS
+
+
+@pytest.fixture(
+    params=(
+        pytest.param(
+            engine,
+            marks=[
+                pytest.mark.skipif(
+                    engine == "numexpr" and not USE_NUMEXPR,
+                    reason=f"numexpr enabled->{USE_NUMEXPR}, "
+                    f"installed->{NUMEXPR_INSTALLED}",
+                ),
+                td.skip_if_no("numexpr"),
+            ],
+        )
+        for engine in ENGINES
+    )
+)
+def engine(request):
+    return request.param
+
+
+@pytest.fixture(params=expr.PARSERS)
+def parser(request):
+    return request.param
+
+
+def _eval_single_bin(lhs, cmp1, rhs, engine):
+    c = _binary_ops_dict[cmp1]
+    if ENGINES[engine].has_neg_frac:
+        try:
+            return c(lhs, rhs)
+        except ValueError as e:
+            if str(e).startswith(
+                "negative number cannot be raised to a fractional power"
+            ):
+                return np.nan
+            raise
+    return c(lhs, rhs)
+
+
+# TODO: using range(5) here is a kludge
+@pytest.fixture(
+    params=list(range(5)),
+    ids=["DataFrame", "Series", "SeriesNaN", "DataFrameNaN", "float"],
+)
+def lhs(request):
+    nan_df1 = DataFrame(np.random.default_rng(2).standard_normal((10, 5)))
+    nan_df1[nan_df1 > 0.5] = np.nan
+
+    opts = (
+        DataFrame(np.random.default_rng(2).standard_normal((10, 5))),
+        Series(np.random.default_rng(2).standard_normal(5)),
+        Series([1, 2, np.nan, np.nan, 5]),
+        nan_df1,
+        np.random.default_rng(2).standard_normal(),
+    )
+    return opts[request.param]
+
+
+rhs = lhs
+midhs = lhs
+
+
+@pytest.fixture
+def idx_func_dict():
+    return {
+        "i": lambda n: Index(np.arange(n), dtype=np.int64),
+        "f": lambda n: Index(np.arange(n), dtype=np.float64),
+        "s": lambda n: Index([f"{i}_{chr(i)}" for i in range(97, 97 + n)]),
+        "dt": lambda n: date_range("2020-01-01", periods=n),
+        "td": lambda n: timedelta_range("1 day", periods=n),
+        "p": lambda n: period_range("2020-01-01", periods=n, freq="D"),
+    }
+
+
+class TestEval:
+    @pytest.mark.parametrize(
+        "cmp1",
+        ["!=", "==", "<=", ">=", "<", ">"],
+        ids=["ne", "eq", "le", "ge", "lt", "gt"],
+    )
+    @pytest.mark.parametrize("cmp2", [">", "<"], ids=["gt", "lt"])
+    @pytest.mark.parametrize("binop", expr.BOOL_OPS_SYMS)
+    def test_complex_cmp_ops(self, cmp1, cmp2, binop, lhs, rhs, engine, parser):
+        if parser == "python" and binop in ["and", "or"]:
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
+                pd.eval(ex, engine=engine, parser=parser)
+            return
+
+        lhs_new = _eval_single_bin(lhs, cmp1, rhs, engine)
+        rhs_new = _eval_single_bin(lhs, cmp2, rhs, engine)
+        expected = _eval_single_bin(lhs_new, binop, rhs_new, engine)
+
+        ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("cmp_op", expr.CMP_OPS_SYMS)
+    def test_simple_cmp_ops(self, cmp_op, lhs, rhs, engine, parser):
+        lhs = lhs < 0
+        rhs = rhs < 0
+
+        if parser == "python" and cmp_op in ["in", "not in"]:
+            msg = "'(In|NotIn)' nodes are not implemented"
+
+            with pytest.raises(NotImplementedError, match=msg):
+                ex = f"lhs {cmp_op} rhs"
+                pd.eval(ex, engine=engine, parser=parser)
+            return
+
+        ex = f"lhs {cmp_op} rhs"
+        msg = "|".join(
+            [
+                r"only list-like( or dict-like)? objects are allowed to be "
+                r"passed to (DataFrame\.)?isin\(\), you passed a "
+                r"(`|')bool(`|')",
+                "argument of type 'bool' is not iterable",
+            ]
+        )
+        if cmp_op in ("in", "not in") and not is_list_like(rhs):
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(
+                    ex,
+                    engine=engine,
+                    parser=parser,
+                    local_dict={"lhs": lhs, "rhs": rhs},
+                )
+        else:
+            expected = _eval_single_bin(lhs, cmp_op, rhs, engine)
+            result = pd.eval(ex, engine=engine, parser=parser)
+            tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("op", expr.CMP_OPS_SYMS)
+    def test_compound_invert_op(self, op, lhs, rhs, request, engine, parser):
+        if parser == "python" and op in ["in", "not in"]:
+            msg = "'(In|NotIn)' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                ex = f"~(lhs {op} rhs)"
+                pd.eval(ex, engine=engine, parser=parser)
+            return
+
+        if (
+            is_float(lhs)
+            and not is_float(rhs)
+            and op in ["in", "not in"]
+            and engine == "python"
+            and parser == "pandas"
+        ):
+            mark = pytest.mark.xfail(
+                reason="Looks like expected is negative, unclear whether "
+                "expected is incorrect or result is incorrect"
+            )
+            request.applymarker(mark)
+        skip_these = ["in", "not in"]
+        ex = f"~(lhs {op} rhs)"
+
+        msg = "|".join(
+            [
+                r"only list-like( or dict-like)? objects are allowed to be "
+                r"passed to (DataFrame\.)?isin\(\), you passed a "
+                r"(`|')float(`|')",
+                "argument of type 'float' is not iterable",
+            ]
+        )
+        if is_scalar(rhs) and op in skip_these:
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(
+                    ex,
+                    engine=engine,
+                    parser=parser,
+                    local_dict={"lhs": lhs, "rhs": rhs},
+                )
+        else:
+            # compound
+            if is_scalar(lhs) and is_scalar(rhs):
+                lhs, rhs = (np.array([x]) for x in (lhs, rhs))
+            expected = _eval_single_bin(lhs, op, rhs, engine)
+            if is_scalar(expected):
+                expected = not expected
+            else:
+                expected = ~expected
+            result = pd.eval(ex, engine=engine, parser=parser)
+            tm.assert_almost_equal(expected, result)
+
+    @pytest.mark.parametrize("cmp1", ["<", ">"])
+    @pytest.mark.parametrize("cmp2", ["<", ">"])
+    def test_chained_cmp_op(self, cmp1, cmp2, lhs, midhs, rhs, engine, parser):
+        mid = midhs
+        if parser == "python":
+            ex1 = f"lhs {cmp1} mid {cmp2} rhs"
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(ex1, engine=engine, parser=parser)
+            return
+
+        lhs_new = _eval_single_bin(lhs, cmp1, mid, engine)
+        rhs_new = _eval_single_bin(mid, cmp2, rhs, engine)
+
+        if lhs_new is not None and rhs_new is not None:
+            ex1 = f"lhs {cmp1} mid {cmp2} rhs"
+            ex2 = f"lhs {cmp1} mid and mid {cmp2} rhs"
+            ex3 = f"(lhs {cmp1} mid) & (mid {cmp2} rhs)"
+            expected = _eval_single_bin(lhs_new, "&", rhs_new, engine)
+
+            for ex in (ex1, ex2, ex3):
+                result = pd.eval(ex, engine=engine, parser=parser)
+
+                tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "arith1", sorted(set(ARITH_OPS_SYMS).difference(SPECIAL_CASE_ARITH_OPS_SYMS))
+    )
+    def test_binary_arith_ops(self, arith1, lhs, rhs, engine, parser):
+        ex = f"lhs {arith1} rhs"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        expected = _eval_single_bin(lhs, arith1, rhs, engine)
+
+        tm.assert_almost_equal(result, expected)
+        ex = f"lhs {arith1} rhs {arith1} rhs"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        nlhs = _eval_single_bin(lhs, arith1, rhs, engine)
+        try:
+            nlhs, ghs = nlhs.align(rhs)
+        except (ValueError, TypeError, AttributeError):
+            # ValueError: series frame or frame series align
+            # TypeError, AttributeError: series or frame with scalar align
+            return
+        else:
+            if engine == "numexpr":
+                import numexpr as ne
+
+                # direct numpy comparison
+                expected = ne.evaluate(f"nlhs {arith1} ghs")
+                # Update assert statement due to unreliable numerical
+                # precision component (GH37328)
+                # TODO: update testing code so that assert_almost_equal statement
+                #  can be replaced again by the assert_numpy_array_equal statement
+                tm.assert_almost_equal(result.values, expected)
+            else:
+                expected = eval(f"nlhs {arith1} ghs")
+                tm.assert_almost_equal(result, expected)
+
+    # modulus, pow, and floor division require special casing
+
+    def test_modulus(self, lhs, rhs, engine, parser):
+        ex = r"lhs % rhs"
+        result = pd.eval(ex, engine=engine, parser=parser)
+        expected = lhs % rhs
+        tm.assert_almost_equal(result, expected)
+
+        if engine == "numexpr":
+            import numexpr as ne
+
+            expected = ne.evaluate(r"expected % rhs")
+            if isinstance(result, (DataFrame, Series)):
+                tm.assert_almost_equal(result.values, expected)
+            else:
+                tm.assert_almost_equal(result, expected.item())
+        else:
+            expected = _eval_single_bin(expected, "%", rhs, engine)
+            tm.assert_almost_equal(result, expected)
+
+    def test_floor_division(self, lhs, rhs, engine, parser):
+        ex = "lhs // rhs"
+
+        if engine == "python":
+            res = pd.eval(ex, engine=engine, parser=parser)
+            expected = lhs // rhs
+            tm.assert_equal(res, expected)
+        else:
+            msg = (
+                r"unsupported operand type\(s\) for //: 'VariableNode' and "
+                "'VariableNode'"
+            )
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(
+                    ex,
+                    local_dict={"lhs": lhs, "rhs": rhs},
+                    engine=engine,
+                    parser=parser,
+                )
+
+    @td.skip_if_windows
+    def test_pow(self, lhs, rhs, engine, parser):
+        # odd failure on win32 platform, so skip
+        ex = "lhs ** rhs"
+        expected = _eval_single_bin(lhs, "**", rhs, engine)
+        result = pd.eval(ex, engine=engine, parser=parser)
+
+        if (
+            is_scalar(lhs)
+            and is_scalar(rhs)
+            and isinstance(expected, (complex, np.complexfloating))
+            and np.isnan(result)
+        ):
+            msg = "(DataFrame.columns|numpy array) are different"
+            with pytest.raises(AssertionError, match=msg):
+                tm.assert_numpy_array_equal(result, expected)
+        else:
+            tm.assert_almost_equal(result, expected)
+
+            ex = "(lhs ** rhs) ** rhs"
+            result = pd.eval(ex, engine=engine, parser=parser)
+
+            middle = _eval_single_bin(lhs, "**", rhs, engine)
+            expected = _eval_single_bin(middle, "**", rhs, engine)
+            tm.assert_almost_equal(result, expected)
+
+    def test_check_single_invert_op(self, lhs, engine, parser):
+        # simple
+        try:
+            elb = lhs.astype(bool)
+        except AttributeError:
+            elb = np.array([bool(lhs)])
+        expected = ~elb
+        result = pd.eval("~elb", engine=engine, parser=parser)
+        tm.assert_almost_equal(expected, result)
+
+    def test_frame_invert(self, engine, parser):
+        expr = "~lhs"
+
+        # ~ ##
+        # frame
+        # float always raises
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)))
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'invert_dd'"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "ufunc 'invert' not supported for the input types"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+        # int raises on numexpr
+        lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2)))
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'invert"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = ~lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_frame_equal(expect, result)
+
+        # bool always works
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5)
+        expect = ~lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+        # object raises
+        lhs = DataFrame(
+            {"b": ["a", 1, 2.0], "c": np.random.default_rng(2).standard_normal(3) > 0.5}
+        )
+        if engine == "numexpr":
+            with pytest.raises(ValueError, match="unknown type object"):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "bad operand type for unary ~: 'str'"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+    def test_series_invert(self, engine, parser):
+        # ~ ####
+        expr = "~lhs"
+
+        # series
+        # float raises
+        lhs = Series(np.random.default_rng(2).standard_normal(5))
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'invert_dd'"
+            with pytest.raises(NotImplementedError, match=msg):
+                result = pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "ufunc 'invert' not supported for the input types"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+        # int raises on numexpr
+        lhs = Series(np.random.default_rng(2).integers(5, size=5))
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'invert"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = ~lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_series_equal(expect, result)
+
+        # bool
+        lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5)
+        expect = ~lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+        # float
+        # int
+        # bool
+
+        # object
+        lhs = Series(["a", 1, 2.0])
+        if engine == "numexpr":
+            with pytest.raises(ValueError, match="unknown type object"):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            msg = "bad operand type for unary ~: 'str'"
+            with pytest.raises(TypeError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+
+    def test_frame_negate(self, engine, parser):
+        expr = "-lhs"
+
+        # float
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+        # int
+        lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2)))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+        # bool doesn't work with numexpr but works elsewhere
+        lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5)
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'neg_bb'"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = -lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_frame_equal(expect, result)
+
+    def test_series_negate(self, engine, parser):
+        expr = "-lhs"
+
+        # float
+        lhs = Series(np.random.default_rng(2).standard_normal(5))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+        # int
+        lhs = Series(np.random.default_rng(2).integers(5, size=5))
+        expect = -lhs
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+        # bool doesn't work with numexpr but works elsewhere
+        lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5)
+        if engine == "numexpr":
+            msg = "couldn't find matching opcode for 'neg_bb'"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(expr, engine=engine, parser=parser)
+        else:
+            expect = -lhs
+            result = pd.eval(expr, engine=engine, parser=parser)
+            tm.assert_series_equal(expect, result)
+
+    @pytest.mark.parametrize(
+        "lhs",
+        [
+            # Float
+            DataFrame(np.random.default_rng(2).standard_normal((5, 2))),
+            # Int
+            DataFrame(np.random.default_rng(2).integers(5, size=(5, 2))),
+            # bool doesn't work with numexpr but works elsewhere
+            DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5),
+        ],
+    )
+    def test_frame_pos(self, lhs, engine, parser):
+        expr = "+lhs"
+        expect = lhs
+
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_frame_equal(expect, result)
+
+    @pytest.mark.parametrize(
+        "lhs",
+        [
+            # Float
+            Series(np.random.default_rng(2).standard_normal(5)),
+            # Int
+            Series(np.random.default_rng(2).integers(5, size=5)),
+            # bool doesn't work with numexpr but works elsewhere
+            Series(np.random.default_rng(2).standard_normal(5) > 0.5),
+        ],
+    )
+    def test_series_pos(self, lhs, engine, parser):
+        expr = "+lhs"
+        expect = lhs
+
+        result = pd.eval(expr, engine=engine, parser=parser)
+        tm.assert_series_equal(expect, result)
+
+    def test_scalar_unary(self, engine, parser):
+        msg = "bad operand type for unary ~: 'float'"
+        warn = None
+        if PY312 and not (engine == "numexpr" and parser == "pandas"):
+            warn = DeprecationWarning
+        with pytest.raises(TypeError, match=msg):
+            pd.eval("~1.0", engine=engine, parser=parser)
+
+        assert pd.eval("-1.0", parser=parser, engine=engine) == -1.0
+        assert pd.eval("+1.0", parser=parser, engine=engine) == +1.0
+        assert pd.eval("~1", parser=parser, engine=engine) == ~1
+        assert pd.eval("-1", parser=parser, engine=engine) == -1
+        assert pd.eval("+1", parser=parser, engine=engine) == +1
+        with tm.assert_produces_warning(
+            warn, match="Bitwise inversion", check_stacklevel=False
+        ):
+            assert pd.eval("~True", parser=parser, engine=engine) == ~True
+        with tm.assert_produces_warning(
+            warn, match="Bitwise inversion", check_stacklevel=False
+        ):
+            assert pd.eval("~False", parser=parser, engine=engine) == ~False
+        assert pd.eval("-True", parser=parser, engine=engine) == -True
+        assert pd.eval("-False", parser=parser, engine=engine) == -False
+        assert pd.eval("+True", parser=parser, engine=engine) == +True
+        assert pd.eval("+False", parser=parser, engine=engine) == +False
+
+    def test_unary_in_array(self):
+        # GH 11235
+        # TODO: 2022-01-29: result return list with numexpr 2.7.3 in CI
+        # but cannot reproduce locally
+        result = np.array(
+            pd.eval("[-True, True, +True, -False, False, +False, -37, 37, ~37, +37]"),
+            dtype=np.object_,
+        )
+        expected = np.array(
+            [
+                -True,
+                True,
+                +True,
+                -False,
+                False,
+                +False,
+                -37,
+                37,
+                ~37,
+                +37,
+            ],
+            dtype=np.object_,
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [np.float32, np.float64])
+    @pytest.mark.parametrize("expr", ["x < -0.1", "-5 > x"])
+    def test_float_comparison_bin_op(self, dtype, expr):
+        # GH 16363
+        df = DataFrame({"x": np.array([0], dtype=dtype)})
+        res = df.eval(expr)
+        assert res.values == np.array([False])
+
+    def test_unary_in_function(self):
+        # GH 46471
+        df = DataFrame({"x": [0, 1, np.nan]})
+
+        result = df.eval("x.fillna(-1)")
+        expected = df.x.fillna(-1)
+        # column name becomes None if using numexpr
+        # only check names when the engine is not numexpr
+        tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR)
+
+        result = df.eval("x.shift(1, fill_value=-1)")
+        expected = df.x.shift(1, fill_value=-1)
+        tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR)
+
+    @pytest.mark.parametrize(
+        "ex",
+        (
+            "1 or 2",
+            "1 and 2",
+            "a and b",
+            "a or b",
+            "1 or 2 and (3 + 2) > 3",
+            "2 * x > 2 or 1 and 2",
+            "2 * df > 3 and 1 or a",
+        ),
+    )
+    def test_disallow_scalar_bool_ops(self, ex, engine, parser):
+        x, a, b = np.random.default_rng(2).standard_normal(3), 1, 2  # noqa: F841
+        df = DataFrame(np.random.default_rng(2).standard_normal((3, 2)))  # noqa: F841
+
+        msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
+        with pytest.raises(NotImplementedError, match=msg):
+            pd.eval(ex, engine=engine, parser=parser)
+
+    def test_identical(self, engine, parser):
+        # see gh-10546
+        x = 1
+        result = pd.eval("x", engine=engine, parser=parser)
+        assert result == 1
+        assert is_scalar(result)
+
+        x = 1.5
+        result = pd.eval("x", engine=engine, parser=parser)
+        assert result == 1.5
+        assert is_scalar(result)
+
+        x = False
+        result = pd.eval("x", engine=engine, parser=parser)
+        assert not result
+        assert is_bool(result)
+        assert is_scalar(result)
+
+        x = np.array([1])
+        result = pd.eval("x", engine=engine, parser=parser)
+        tm.assert_numpy_array_equal(result, np.array([1]))
+        assert result.shape == (1,)
+
+        x = np.array([1.5])
+        result = pd.eval("x", engine=engine, parser=parser)
+        tm.assert_numpy_array_equal(result, np.array([1.5]))
+        assert result.shape == (1,)
+
+        x = np.array([False])  # noqa: F841
+        result = pd.eval("x", engine=engine, parser=parser)
+        tm.assert_numpy_array_equal(result, np.array([False]))
+        assert result.shape == (1,)
+
+    def test_line_continuation(self, engine, parser):
+        # GH 11149
+        exp = """1 + 2 * \
+        5 - 1 + 2 """
+        result = pd.eval(exp, engine=engine, parser=parser)
+        assert result == 12
+
+    def test_float_truncation(self, engine, parser):
+        # GH 14241
+        exp = "1000000000.006"
+        result = pd.eval(exp, engine=engine, parser=parser)
+        expected = np.float64(exp)
+        assert result == expected
+
+        df = DataFrame({"A": [1000000000.0009, 1000000000.0011, 1000000000.0015]})
+        cutoff = 1000000000.0006
+        result = df.query(f"A < {cutoff:.4f}")
+        assert result.empty
+
+        cutoff = 1000000000.0010
+        result = df.query(f"A > {cutoff:.4f}")
+        expected = df.loc[[1, 2], :]
+        tm.assert_frame_equal(expected, result)
+
+        exact = 1000000000.0011
+        result = df.query(f"A == {exact:.4f}")
+        expected = df.loc[[1], :]
+        tm.assert_frame_equal(expected, result)
+
+    def test_disallow_python_keywords(self):
+        # GH 18221
+        df = DataFrame([[0, 0, 0]], columns=["foo", "bar", "class"])
+        msg = "Python keyword not valid identifier in numexpr query"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("class == 0")
+
+        df = DataFrame()
+        df.index.name = "lambda"
+        with pytest.raises(SyntaxError, match=msg):
+            df.query("lambda == 0")
+
+    def test_true_false_logic(self):
+        # GH 25823
+        # This behavior is deprecated in Python 3.12
+        with tm.maybe_produces_warning(
+            DeprecationWarning, PY312, check_stacklevel=False
+        ):
+            assert pd.eval("not True") == -2
+            assert pd.eval("not False") == -1
+            assert pd.eval("True and not True") == 0
+
+    def test_and_logic_string_match(self):
+        # GH 25823
+        event = Series({"a": "hello"})
+        assert pd.eval(f"{event.str.match('hello').a}")
+        assert pd.eval(f"{event.str.match('hello').a and event.str.match('hello').a}")
+
+
+# -------------------------------------
+# gh-12388: Typecasting rules consistency with python
+
+
+class TestTypeCasting:
+    @pytest.mark.parametrize("op", ["+", "-", "*", "**", "/"])
+    # maybe someday... numexpr has too many upcasting rules now
+    # chain(*(np.core.sctypes[x] for x in ['uint', 'int', 'float']))
+    @pytest.mark.parametrize("left_right", [("df", "3"), ("3", "df")])
+    def test_binop_typecasting(
+        self, engine, parser, op, complex_or_float_dtype, left_right, request
+    ):
+        # GH#21374
+        dtype = complex_or_float_dtype
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)), dtype=dtype)
+        left, right = left_right
+        s = f"{left} {op} {right}"
+        res = pd.eval(s, engine=engine, parser=parser)
+        if dtype == "complex64" and engine == "numexpr":
+            mark = pytest.mark.xfail(
+                reason="numexpr issue with complex that are upcast "
+                "to complex 128 "
+                "https://github.com/pydata/numexpr/issues/492"
+            )
+            request.applymarker(mark)
+        assert df.values.dtype == dtype
+        assert res.values.dtype == dtype
+        tm.assert_frame_equal(res, eval(s), check_exact=False)
+
+
+# -------------------------------------
+# Basic and complex alignment
+
+
+def should_warn(*args):
+    not_mono = not any(map(operator.attrgetter("is_monotonic_increasing"), args))
+    only_one_dt = reduce(
+        operator.xor, (issubclass(x.dtype.type, np.datetime64) for x in args)
+    )
+    return not_mono and only_one_dt
+
+
+class TestAlignment:
+    index_types = ["i", "s", "dt"]
+    lhs_index_types = index_types + ["s"]  # 'p'
+
+    def test_align_nested_unary_op(self, engine, parser):
+        s = "df * ~2"
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        res = pd.eval(s, engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df * ~2)
+
+    @pytest.mark.filterwarnings("always::RuntimeWarning")
+    @pytest.mark.parametrize("lr_idx_type", lhs_index_types)
+    @pytest.mark.parametrize("rr_idx_type", index_types)
+    @pytest.mark.parametrize("c_idx_type", index_types)
+    def test_basic_frame_alignment(
+        self, engine, parser, lr_idx_type, rr_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[lr_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((20, 10)),
+            index=idx_func_dict[rr_idx_type](20),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        # only warns if not monotonic and not sortable
+        if should_warn(df.index, df2.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df + df2", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df + df2", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df + df2)
+
+    @pytest.mark.parametrize("r_idx_type", lhs_index_types)
+    @pytest.mark.parametrize("c_idx_type", lhs_index_types)
+    def test_frame_comparison(
+        self, engine, parser, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        res = pd.eval("df < 2", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df < 2)
+
+        df3 = DataFrame(
+            np.random.default_rng(2).standard_normal(df.shape),
+            index=df.index,
+            columns=df.columns,
+        )
+        res = pd.eval("df < df3", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df < df3)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("r1", lhs_index_types)
+    @pytest.mark.parametrize("c1", index_types)
+    @pytest.mark.parametrize("r2", index_types)
+    @pytest.mark.parametrize("c2", index_types)
+    def test_medium_complex_frame_alignment(
+        self, engine, parser, r1, c1, r2, c2, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 2)),
+            index=idx_func_dict[r1](3),
+            columns=idx_func_dict[c1](2),
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 2)),
+            index=idx_func_dict[r2](4),
+            columns=idx_func_dict[c2](2),
+        )
+        df3 = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)),
+            index=idx_func_dict[r2](5),
+            columns=idx_func_dict[c2](2),
+        )
+        if should_warn(df.index, df2.index, df3.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df + df2 + df3", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df + df2 + df3", engine=engine, parser=parser)
+        tm.assert_frame_equal(res, df + df2 + df3)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("index_name", ["index", "columns"])
+    @pytest.mark.parametrize("c_idx_type", index_types)
+    @pytest.mark.parametrize("r_idx_type", lhs_index_types)
+    def test_basic_frame_series_alignment(
+        self, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        index = getattr(df, index_name)
+        s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
+
+        if should_warn(df.index, s.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df + s", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df + s", engine=engine, parser=parser)
+
+        if r_idx_type == "dt" or c_idx_type == "dt":
+            expected = df.add(s) if engine == "numexpr" else df + s
+        else:
+            expected = df + s
+        tm.assert_frame_equal(res, expected)
+
+    @pytest.mark.parametrize("index_name", ["index", "columns"])
+    @pytest.mark.parametrize(
+        "r_idx_type, c_idx_type",
+        list(product(["i", "s"], ["i", "s"])) + [("dt", "dt")],
+    )
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_basic_series_frame_alignment(
+        self, request, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        if (
+            engine == "numexpr"
+            and parser in ("pandas", "python")
+            and index_name == "index"
+            and r_idx_type == "i"
+            and c_idx_type == "s"
+        ):
+            reason = (
+                f"Flaky column ordering when engine={engine}, "
+                f"parser={parser}, index_name={index_name}, "
+                f"r_idx_type={r_idx_type}, c_idx_type={c_idx_type}"
+            )
+            request.applymarker(pytest.mark.xfail(reason=reason, strict=False))
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 7)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](7),
+        )
+        index = getattr(df, index_name)
+        s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
+        if should_warn(s.index, df.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("s + df", engine=engine, parser=parser)
+        else:
+            res = pd.eval("s + df", engine=engine, parser=parser)
+
+        if r_idx_type == "dt" or c_idx_type == "dt":
+            expected = df.add(s) if engine == "numexpr" else s + df
+        else:
+            expected = s + df
+        tm.assert_frame_equal(res, expected)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("c_idx_type", index_types)
+    @pytest.mark.parametrize("r_idx_type", lhs_index_types)
+    @pytest.mark.parametrize("index_name", ["index", "columns"])
+    @pytest.mark.parametrize("op", ["+", "*"])
+    def test_series_frame_commutativity(
+        self, engine, parser, index_name, op, r_idx_type, c_idx_type, idx_func_dict
+    ):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 10)),
+            index=idx_func_dict[r_idx_type](10),
+            columns=idx_func_dict[c_idx_type](10),
+        )
+        index = getattr(df, index_name)
+        s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
+
+        lhs = f"s {op} df"
+        rhs = f"df {op} s"
+        if should_warn(df.index, s.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                a = pd.eval(lhs, engine=engine, parser=parser)
+            with tm.assert_produces_warning(RuntimeWarning):
+                b = pd.eval(rhs, engine=engine, parser=parser)
+        else:
+            a = pd.eval(lhs, engine=engine, parser=parser)
+            b = pd.eval(rhs, engine=engine, parser=parser)
+
+        if r_idx_type != "dt" and c_idx_type != "dt":
+            if engine == "numexpr":
+                tm.assert_frame_equal(a, b)
+
+    @pytest.mark.filterwarnings("always::RuntimeWarning")
+    @pytest.mark.parametrize("r1", lhs_index_types)
+    @pytest.mark.parametrize("c1", index_types)
+    @pytest.mark.parametrize("r2", index_types)
+    @pytest.mark.parametrize("c2", index_types)
+    def test_complex_series_frame_alignment(
+        self, engine, parser, r1, c1, r2, c2, idx_func_dict
+    ):
+        n = 3
+        m1 = 5
+        m2 = 2 * m1
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((m1, n)),
+            index=idx_func_dict[r1](m1),
+            columns=idx_func_dict[c1](n),
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal((m2, n)),
+            index=idx_func_dict[r2](m2),
+            columns=idx_func_dict[c2](n),
+        )
+        index = df2.columns
+        ser = Series(np.random.default_rng(2).standard_normal(n), index[:n])
+
+        if r2 == "dt" or c2 == "dt":
+            if engine == "numexpr":
+                expected2 = df2.add(ser)
+            else:
+                expected2 = df2 + ser
+        else:
+            expected2 = df2 + ser
+
+        if r1 == "dt" or c1 == "dt":
+            if engine == "numexpr":
+                expected = expected2.add(df)
+            else:
+                expected = expected2 + df
+        else:
+            expected = expected2 + df
+
+        if should_warn(df2.index, ser.index, df.index):
+            with tm.assert_produces_warning(RuntimeWarning):
+                res = pd.eval("df2 + ser + df", engine=engine, parser=parser)
+        else:
+            res = pd.eval("df2 + ser + df", engine=engine, parser=parser)
+        assert res.shape == expected.shape
+        tm.assert_frame_equal(res, expected)
+
+    def test_performance_warning_for_poor_alignment(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((1000, 10)))
+        s = Series(np.random.default_rng(2).standard_normal(10000))
+        if engine == "numexpr":
+            seen = PerformanceWarning
+        else:
+            seen = False
+
+        with tm.assert_produces_warning(seen):
+            pd.eval("df + s", engine=engine, parser=parser)
+
+        s = Series(np.random.default_rng(2).standard_normal(1000))
+        with tm.assert_produces_warning(False):
+            pd.eval("df + s", engine=engine, parser=parser)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 10000)))
+        s = Series(np.random.default_rng(2).standard_normal(10000))
+        with tm.assert_produces_warning(False):
+            pd.eval("df + s", engine=engine, parser=parser)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 10)))
+        s = Series(np.random.default_rng(2).standard_normal(10000))
+
+        is_python_engine = engine == "python"
+
+        if not is_python_engine:
+            wrn = PerformanceWarning
+        else:
+            wrn = False
+
+        with tm.assert_produces_warning(wrn) as w:
+            pd.eval("df + s", engine=engine, parser=parser)
+
+            if not is_python_engine:
+                assert len(w) == 1
+                msg = str(w[0].message)
+                logged = np.log10(s.size - df.shape[1])
+                expected = (
+                    f"Alignment difference on axis 1 is larger "
+                    f"than an order of magnitude on term 'df', "
+                    f"by more than {logged:.4g}; performance may suffer."
+                )
+                assert msg == expected
+
+
+# ------------------------------------
+# Slightly more complex ops
+
+
+class TestOperations:
+    def eval(self, *args, **kwargs):
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        return pd.eval(*args, **kwargs)
+
+    def test_simple_arith_ops(self, engine, parser):
+        exclude_arith = []
+        if parser == "python":
+            exclude_arith = ["in", "not in"]
+
+        arith_ops = [
+            op
+            for op in expr.ARITH_OPS_SYMS + expr.CMP_OPS_SYMS
+            if op not in exclude_arith
+        ]
+
+        ops = (op for op in arith_ops if op != "//")
+
+        for op in ops:
+            ex = f"1 {op} 1"
+            ex2 = f"x {op} 1"
+            ex3 = f"1 {op} (x + 1)"
+
+            if op in ("in", "not in"):
+                msg = "argument of type 'int' is not iterable"
+                with pytest.raises(TypeError, match=msg):
+                    pd.eval(ex, engine=engine, parser=parser)
+            else:
+                expec = _eval_single_bin(1, op, 1, engine)
+                x = self.eval(ex, engine=engine, parser=parser)
+                assert x == expec
+
+                expec = _eval_single_bin(x, op, 1, engine)
+                y = self.eval(ex2, local_dict={"x": x}, engine=engine, parser=parser)
+                assert y == expec
+
+                expec = _eval_single_bin(1, op, x + 1, engine)
+                y = self.eval(ex3, local_dict={"x": x}, engine=engine, parser=parser)
+                assert y == expec
+
+    @pytest.mark.parametrize("rhs", [True, False])
+    @pytest.mark.parametrize("lhs", [True, False])
+    @pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS)
+    def test_simple_bool_ops(self, rhs, lhs, op):
+        ex = f"{lhs} {op} {rhs}"
+
+        if parser == "python" and op in ["and", "or"]:
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                self.eval(ex)
+            return
+
+        res = self.eval(ex)
+        exp = eval(ex)
+        assert res == exp
+
+    @pytest.mark.parametrize("rhs", [True, False])
+    @pytest.mark.parametrize("lhs", [True, False])
+    @pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS)
+    def test_bool_ops_with_constants(self, rhs, lhs, op):
+        ex = f"{lhs} {op} {rhs}"
+
+        if parser == "python" and op in ["and", "or"]:
+            msg = "'BoolOp' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                self.eval(ex)
+            return
+
+        res = self.eval(ex)
+        exp = eval(ex)
+        assert res == exp
+
+    def test_4d_ndarray_fails(self):
+        x = np.random.default_rng(2).standard_normal((3, 4, 5, 6))
+        y = Series(np.random.default_rng(2).standard_normal(10))
+        msg = "N-dimensional objects, where N > 2, are not supported with eval"
+        with pytest.raises(NotImplementedError, match=msg):
+            self.eval("x + y", local_dict={"x": x, "y": y})
+
+    def test_constant(self):
+        x = self.eval("1")
+        assert x == 1
+
+    def test_single_variable(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)))
+        df2 = self.eval("df", local_dict={"df": df})
+        tm.assert_frame_equal(df, df2)
+
+    def test_failing_subscript_with_name_error(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))  # noqa: F841
+        with pytest.raises(NameError, match="name 'x' is not defined"):
+            self.eval("df[x > 2] > 2")
+
+    def test_lhs_expression_subscript(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        result = self.eval("(df + 1)[df > 2]", local_dict={"df": df})
+        expected = (df + 1)[df > 2]
+        tm.assert_frame_equal(result, expected)
+
+    def test_attr_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc")
+        )
+        expr1 = "df.a < df.b"
+        expec1 = df.a < df.b
+        expr2 = "df.a + df.b + df.c"
+        expec2 = df.a + df.b + df.c
+        expr3 = "df.a + df.b + df.c[df.b < 0]"
+        expec3 = df.a + df.b + df.c[df.b < 0]
+        exprs = expr1, expr2, expr3
+        expecs = expec1, expec2, expec3
+        for e, expec in zip(exprs, expecs):
+            tm.assert_series_equal(expec, self.eval(e, local_dict={"df": df}))
+
+    def test_assignment_fails(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc")
+        )
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        expr1 = "df = df2"
+        msg = "cannot assign without a target object"
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expr1, local_dict={"df": df, "df2": df2})
+
+    def test_assignment_column_multiple_raise(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # multiple assignees
+        with pytest.raises(SyntaxError, match="invalid syntax"):
+            df.eval("d c = a + b")
+
+    def test_assignment_column_invalid_assign(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # invalid assignees
+        msg = "left hand side of an assignment must be a single name"
+        with pytest.raises(SyntaxError, match=msg):
+            df.eval("d,c = a + b")
+
+    def test_assignment_column_invalid_assign_function_call(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        msg = "cannot assign to function call"
+        with pytest.raises(SyntaxError, match=msg):
+            df.eval('Timestamp("20131001") = a + b')
+
+    def test_assignment_single_assign_existing(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # single assignment - existing variable
+        expected = df.copy()
+        expected["a"] = expected["a"] + expected["b"]
+        df.eval("a = a + b", inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+    def test_assignment_single_assign_new(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # single assignment - new variable
+        expected = df.copy()
+        expected["c"] = expected["a"] + expected["b"]
+        df.eval("c = a + b", inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+    def test_assignment_single_assign_local_overlap(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        df = df.copy()
+        a = 1  # noqa: F841
+        df.eval("a = 1 + b", inplace=True)
+
+        expected = df.copy()
+        expected["a"] = 1 + expected["b"]
+        tm.assert_frame_equal(df, expected)
+
+    def test_assignment_single_assign_name(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+
+        a = 1  # noqa: F841
+        old_a = df.a.copy()
+        df.eval("a = a + b", inplace=True)
+        result = old_a + df.b
+        tm.assert_series_equal(result, df.a, check_names=False)
+        assert result.name is None
+
+    def test_assignment_multiple_raises(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # multiple assignment
+        df.eval("c = a + b", inplace=True)
+        msg = "can only assign a single expression"
+        with pytest.raises(SyntaxError, match=msg):
+            df.eval("c = a = b")
+
+    def test_assignment_explicit(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+        # explicit targets
+        self.eval("c = df.a + df.b", local_dict={"df": df}, target=df, inplace=True)
+        expected = df.copy()
+        expected["c"] = expected["a"] + expected["b"]
+        tm.assert_frame_equal(df, expected)
+
+    def test_column_in(self):
+        # GH 11235
+        df = DataFrame({"a": [11], "b": [-32]})
+        result = df.eval("a in [11, -32]")
+        expected = Series([True])
+        # TODO: 2022-01-29: Name check failed with numexpr 2.7.3 in CI
+        # but cannot reproduce locally
+        tm.assert_series_equal(result, expected, check_names=False)
+
+    @pytest.mark.xfail(reason="Unknown: Omitted test_ in name prior.")
+    def test_assignment_not_inplace(self):
+        # see gh-9297
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
+        )
+
+        actual = df.eval("c = a + b", inplace=False)
+        assert actual is not None
+
+        expected = df.copy()
+        expected["c"] = expected["a"] + expected["b"]
+        tm.assert_frame_equal(df, expected)
+
+    def test_multi_line_expression(self, warn_copy_on_write):
+        # GH 11149
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+
+        expected["c"] = expected["a"] + expected["b"]
+        expected["d"] = expected["c"] + expected["b"]
+        answer = df.eval(
+            """
+        c = a + b
+        d = c + b""",
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+        expected["a"] = expected["a"] - 1
+        expected["e"] = expected["a"] + 2
+        answer = df.eval(
+            """
+        a = a - 1
+        e = a + 2""",
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+        # multi-line not valid if not all assignments
+        msg = "Multi-line expressions are only valid if all expressions contain"
+        with pytest.raises(ValueError, match=msg):
+            df.eval(
+                """
+            a = b + 2
+            b - 2""",
+                inplace=False,
+            )
+
+    def test_multi_line_expression_not_inplace(self):
+        # GH 11149
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+
+        expected["c"] = expected["a"] + expected["b"]
+        expected["d"] = expected["c"] + expected["b"]
+        df = df.eval(
+            """
+        c = a + b
+        d = c + b""",
+            inplace=False,
+        )
+        tm.assert_frame_equal(expected, df)
+
+        expected["a"] = expected["a"] - 1
+        expected["e"] = expected["a"] + 2
+        df = df.eval(
+            """
+        a = a - 1
+        e = a + 2""",
+            inplace=False,
+        )
+        tm.assert_frame_equal(expected, df)
+
+    def test_multi_line_expression_local_variable(self):
+        # GH 15342
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+
+        local_var = 7
+        expected["c"] = expected["a"] * local_var
+        expected["d"] = expected["c"] + local_var
+        answer = df.eval(
+            """
+        c = a * @local_var
+        d = c + @local_var
+        """,
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+    def test_multi_line_expression_callable_local_variable(self):
+        # 26426
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+        def local_func(a, b):
+            return b
+
+        expected = df.copy()
+        expected["c"] = expected["a"] * local_func(1, 7)
+        expected["d"] = expected["c"] + local_func(1, 7)
+        answer = df.eval(
+            """
+        c = a * @local_func(1, 7)
+        d = c + @local_func(1, 7)
+        """,
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+    def test_multi_line_expression_callable_local_variable_with_kwargs(self):
+        # 26426
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+        def local_func(a, b):
+            return b
+
+        expected = df.copy()
+        expected["c"] = expected["a"] * local_func(b=7, a=1)
+        expected["d"] = expected["c"] + local_func(b=7, a=1)
+        answer = df.eval(
+            """
+        c = a * @local_func(b=7, a=1)
+        d = c + @local_func(b=7, a=1)
+        """,
+            inplace=True,
+        )
+        tm.assert_frame_equal(expected, df)
+        assert answer is None
+
+    def test_assignment_in_query(self):
+        # GH 8664
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        df_orig = df.copy()
+        msg = "cannot assign without a target object"
+        with pytest.raises(ValueError, match=msg):
+            df.query("a = 1")
+        tm.assert_frame_equal(df, df_orig)
+
+    def test_query_inplace(self):
+        # see gh-11149
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        expected = df.copy()
+        expected = expected[expected["a"] == 2]
+        df.query("a == 2", inplace=True)
+        tm.assert_frame_equal(expected, df)
+
+        df = {}
+        expected = {"a": 3}
+
+        self.eval("a = 1 + 2", target=df, inplace=True)
+        tm.assert_dict_equal(df, expected)
+
+    @pytest.mark.parametrize("invalid_target", [1, "cat", [1, 2], np.array([]), (1, 3)])
+    def test_cannot_item_assign(self, invalid_target):
+        msg = "Cannot assign expression output to target"
+        expression = "a = 1 + 2"
+
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expression, target=invalid_target, inplace=True)
+
+        if hasattr(invalid_target, "copy"):
+            with pytest.raises(ValueError, match=msg):
+                self.eval(expression, target=invalid_target, inplace=False)
+
+    @pytest.mark.parametrize("invalid_target", [1, "cat", (1, 3)])
+    def test_cannot_copy_item(self, invalid_target):
+        msg = "Cannot return a copy of the target"
+        expression = "a = 1 + 2"
+
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expression, target=invalid_target, inplace=False)
+
+    @pytest.mark.parametrize("target", [1, "cat", [1, 2], np.array([]), (1, 3), {1: 2}])
+    def test_inplace_no_assignment(self, target):
+        expression = "1 + 2"
+
+        assert self.eval(expression, target=target, inplace=False) == 3
+
+        msg = "Cannot operate inplace if there is no assignment"
+        with pytest.raises(ValueError, match=msg):
+            self.eval(expression, target=target, inplace=True)
+
+    def test_basic_period_index_boolean_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((2, 2)),
+            columns=period_range("2020-01-01", freq="D", periods=2),
+        )
+        e = df < 2
+        r = self.eval("df < 2", local_dict={"df": df})
+        x = df < 2
+
+        tm.assert_frame_equal(r, e)
+        tm.assert_frame_equal(x, e)
+
+    def test_basic_period_index_subscript_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((2, 2)),
+            columns=period_range("2020-01-01", freq="D", periods=2),
+        )
+        r = self.eval("df[df < 2 + 3]", local_dict={"df": df})
+        e = df[df < 2 + 3]
+        tm.assert_frame_equal(r, e)
+
+    def test_nested_period_index_subscript_expression(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((2, 2)),
+            columns=period_range("2020-01-01", freq="D", periods=2),
+        )
+        r = self.eval("df[df[df < 2] < 2] + df * 2", local_dict={"df": df})
+        e = df[df[df < 2] < 2] + df * 2
+        tm.assert_frame_equal(r, e)
+
+    def test_date_boolean(self, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        df["dates1"] = date_range("1/1/2012", periods=5)
+        res = self.eval(
+            "df.dates1 < 20130101",
+            local_dict={"df": df},
+            engine=engine,
+            parser=parser,
+        )
+        expec = df.dates1 < "20130101"
+        tm.assert_series_equal(res, expec, check_names=False)
+
+    def test_simple_in_ops(self, engine, parser):
+        if parser != "python":
+            res = pd.eval("1 in [1, 2]", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("2 in (1, 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("3 in (1, 2)", engine=engine, parser=parser)
+            assert not res
+
+            res = pd.eval("3 not in (1, 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("[3] not in (1, 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("[3] in ([3], 2)", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("[[3]] in [[[3]], 2]", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("(3,) in [(3,), 2]", engine=engine, parser=parser)
+            assert res
+
+            res = pd.eval("(3,) not in [(3,), 2]", engine=engine, parser=parser)
+            assert not res
+
+            res = pd.eval("[(3,)] in [[(3,)], 2]", engine=engine, parser=parser)
+            assert res
+        else:
+            msg = "'In' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("1 in [1, 2]", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("2 in (1, 2)", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("3 in (1, 2)", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("[(3,)] in (1, 2, [(3,)])", engine=engine, parser=parser)
+            msg = "'NotIn' nodes are not implemented"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("3 not in (1, 2)", engine=engine, parser=parser)
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval("[3] not in (1, 2, [[3]])", engine=engine, parser=parser)
+
+    def test_check_many_exprs(self, engine, parser):
+        a = 1  # noqa: F841
+        expr = " * ".join("a" * 33)
+        expected = 1
+        res = pd.eval(expr, engine=engine, parser=parser)
+        assert res == expected
+
+    @pytest.mark.parametrize(
+        "expr",
+        [
+            "df > 2 and df > 3",
+            "df > 2 or df > 3",
+            "not df > 2",
+        ],
+    )
+    def test_fails_and_or_not(self, expr, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
+        if parser == "python":
+            msg = "'BoolOp' nodes are not implemented"
+            if "not" in expr:
+                msg = "'Not' nodes are not implemented"
+
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(
+                    expr,
+                    local_dict={"df": df},
+                    parser=parser,
+                    engine=engine,
+                )
+        else:
+            # smoke-test, should not raise
+            pd.eval(
+                expr,
+                local_dict={"df": df},
+                parser=parser,
+                engine=engine,
+            )
+
+    @pytest.mark.parametrize("char", ["|", "&"])
+    def test_fails_ampersand_pipe(self, char, engine, parser):
+        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))  # noqa: F841
+        ex = f"(df + 2)[df > 1] > 0 {char} (df > 0)"
+        if parser == "python":
+            msg = "cannot evaluate scalar only bool ops"
+            with pytest.raises(NotImplementedError, match=msg):
+                pd.eval(ex, parser=parser, engine=engine)
+        else:
+            # smoke-test, should not raise
+            pd.eval(ex, parser=parser, engine=engine)
+
+
+class TestMath:
+    def eval(self, *args, **kwargs):
+        kwargs["level"] = kwargs.pop("level", 0) + 1
+        return pd.eval(*args, **kwargs)
+
+    @pytest.mark.skipif(
+        not NUMEXPR_INSTALLED, reason="Unary ops only implemented for numexpr"
+    )
+    @pytest.mark.parametrize("fn", _unary_math_ops)
+    def test_unary_functions(self, fn):
+        df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
+        a = df.a
+
+        expr = f"{fn}(a)"
+        got = self.eval(expr)
+        with np.errstate(all="ignore"):
+            expect = getattr(np, fn)(a)
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    @pytest.mark.parametrize("fn", _binary_math_ops)
+    def test_binary_functions(self, fn):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).standard_normal(10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        a = df.a
+        b = df.b
+
+        expr = f"{fn}(a, b)"
+        got = self.eval(expr)
+        with np.errstate(all="ignore"):
+            expect = getattr(np, fn)(a, b)
+        tm.assert_almost_equal(got, expect, check_names=False)
+
+    def test_df_use_case(self, engine, parser):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).standard_normal(10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        df.eval(
+            "e = arctan2(sin(a), b)",
+            engine=engine,
+            parser=parser,
+            inplace=True,
+        )
+        got = df.e
+        expect = np.arctan2(np.sin(df.a), df.b)
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    def test_df_arithmetic_subexpression(self, engine, parser):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).standard_normal(10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        df.eval("e = sin(a + b)", engine=engine, parser=parser, inplace=True)
+        got = df.e
+        expect = np.sin(df.a + df.b)
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    @pytest.mark.parametrize(
+        "dtype, expect_dtype",
+        [
+            (np.int32, np.float64),
+            (np.int64, np.float64),
+            (np.float32, np.float32),
+            (np.float64, np.float64),
+            pytest.param(np.complex128, np.complex128, marks=td.skip_if_windows),
+        ],
+    )
+    def test_result_types(self, dtype, expect_dtype, engine, parser):
+        # xref https://github.com/pandas-dev/pandas/issues/12293
+        #  this fails on Windows, apparently a floating point precision issue
+
+        # Did not test complex64 because DataFrame is converting it to
+        # complex128. Due to https://github.com/pandas-dev/pandas/issues/10952
+        df = DataFrame(
+            {"a": np.random.default_rng(2).standard_normal(10).astype(dtype)}
+        )
+        assert df.a.dtype == dtype
+        df.eval("b = sin(a)", engine=engine, parser=parser, inplace=True)
+        got = df.b
+        expect = np.sin(df.a)
+        assert expect.dtype == got.dtype
+        assert expect_dtype == got.dtype
+        tm.assert_series_equal(got, expect, check_names=False)
+
+    def test_undefined_func(self, engine, parser):
+        df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
+        msg = '"mysin" is not a supported function'
+
+        with pytest.raises(ValueError, match=msg):
+            df.eval("mysin(a)", engine=engine, parser=parser)
+
+    def test_keyword_arg(self, engine, parser):
+        df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
+        msg = 'Function "sin" does not support keyword arguments'
+
+        with pytest.raises(TypeError, match=msg):
+            df.eval("sin(x=a)", engine=engine, parser=parser)
+
+
+_var_s = np.random.default_rng(2).standard_normal(10)
+
+
+class TestScope:
+    def test_global_scope(self, engine, parser):
+        e = "_var_s * 2"
+        tm.assert_numpy_array_equal(
+            _var_s * 2, pd.eval(e, engine=engine, parser=parser)
+        )
+
+    def test_no_new_locals(self, engine, parser):
+        x = 1
+        lcls = locals().copy()
+        pd.eval("x + 1", local_dict=lcls, engine=engine, parser=parser)
+        lcls2 = locals().copy()
+        lcls2.pop("lcls")
+        assert lcls == lcls2
+
+    def test_no_new_globals(self, engine, parser):
+        x = 1  # noqa: F841
+        gbls = globals().copy()
+        pd.eval("x + 1", engine=engine, parser=parser)
+        gbls2 = globals().copy()
+        assert gbls == gbls2
+
+    def test_empty_locals(self, engine, parser):
+        # GH 47084
+        x = 1  # noqa: F841
+        msg = "name 'x' is not defined"
+        with pytest.raises(UndefinedVariableError, match=msg):
+            pd.eval("x + 1", engine=engine, parser=parser, local_dict={})
+
+    def test_empty_globals(self, engine, parser):
+        # GH 47084
+        msg = "name '_var_s' is not defined"
+        e = "_var_s * 2"
+        with pytest.raises(UndefinedVariableError, match=msg):
+            pd.eval(e, engine=engine, parser=parser, global_dict={})
+
+
+@td.skip_if_no("numexpr")
+def test_invalid_engine():
+    msg = "Invalid engine 'asdf' passed"
+    with pytest.raises(KeyError, match=msg):
+        pd.eval("x + y", local_dict={"x": 1, "y": 2}, engine="asdf")
+
+
+@td.skip_if_no("numexpr")
+@pytest.mark.parametrize(
+    ("use_numexpr", "expected"),
+    (
+        (True, "numexpr"),
+        (False, "python"),
+    ),
+)
+def test_numexpr_option_respected(use_numexpr, expected):
+    # GH 32556
+    from pandas.core.computation.eval import _check_engine
+
+    with pd.option_context("compute.use_numexpr", use_numexpr):
+        result = _check_engine(None)
+        assert result == expected
+
+
+@td.skip_if_no("numexpr")
+def test_numexpr_option_incompatible_op():
+    # GH 32556
+    with pd.option_context("compute.use_numexpr", False):
+        df = DataFrame(
+            {"A": [True, False, True, False, None, None], "B": [1, 2, 3, 4, 5, 6]}
+        )
+        result = df.query("A.isnull()")
+        expected = DataFrame({"A": [None, None], "B": [5, 6]}, index=[4, 5])
+        tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("numexpr")
+def test_invalid_parser():
+    msg = "Invalid parser 'asdf' passed"
+    with pytest.raises(KeyError, match=msg):
+        pd.eval("x + y", local_dict={"x": 1, "y": 2}, parser="asdf")
+
+
+_parsers: dict[str, type[BaseExprVisitor]] = {
+    "python": PythonExprVisitor,
+    "pytables": pytables.PyTablesExprVisitor,
+    "pandas": PandasExprVisitor,
+}
+
+
+@pytest.mark.parametrize("engine", ENGINES)
+@pytest.mark.parametrize("parser", _parsers)
+def test_disallowed_nodes(engine, parser):
+    VisitorClass = _parsers[parser]
+    inst = VisitorClass("x + 1", engine, parser)
+
+    for ops in VisitorClass.unsupported_nodes:
+        msg = "nodes are not implemented"
+        with pytest.raises(NotImplementedError, match=msg):
+            getattr(inst, ops)()
+
+
+def test_syntax_error_exprs(engine, parser):
+    e = "s +"
+    with pytest.raises(SyntaxError, match="invalid syntax"):
+        pd.eval(e, engine=engine, parser=parser)
+
+
+def test_name_error_exprs(engine, parser):
+    e = "s + t"
+    msg = "name 's' is not defined"
+    with pytest.raises(NameError, match=msg):
+        pd.eval(e, engine=engine, parser=parser)
+
+
+@pytest.mark.parametrize("express", ["a + @b", "@a + b", "@a + @b"])
+def test_invalid_local_variable_reference(engine, parser, express):
+    a, b = 1, 2  # noqa: F841
+
+    if parser != "pandas":
+        with pytest.raises(SyntaxError, match="The '@' prefix is only"):
+            pd.eval(express, engine=engine, parser=parser)
+    else:
+        with pytest.raises(SyntaxError, match="The '@' prefix is not"):
+            pd.eval(express, engine=engine, parser=parser)
+
+
+def test_numexpr_builtin_raises(engine, parser):
+    sin, dotted_line = 1, 2
+    if engine == "numexpr":
+        msg = "Variables in expression .+"
+        with pytest.raises(NumExprClobberingError, match=msg):
+            pd.eval("sin + dotted_line", engine=engine, parser=parser)
+    else:
+        res = pd.eval("sin + dotted_line", engine=engine, parser=parser)
+        assert res == sin + dotted_line
+
+
+def test_bad_resolver_raises(engine, parser):
+    cannot_resolve = 42, 3.0
+    with pytest.raises(TypeError, match="Resolver of type .+"):
+        pd.eval("1 + 2", resolvers=cannot_resolve, engine=engine, parser=parser)
+
+
+def test_empty_string_raises(engine, parser):
+    # GH 13139
+    with pytest.raises(ValueError, match="expr cannot be an empty string"):
+        pd.eval("", engine=engine, parser=parser)
+
+
+def test_more_than_one_expression_raises(engine, parser):
+    with pytest.raises(SyntaxError, match="only a single expression is allowed"):
+        pd.eval("1 + 1; 2 + 2", engine=engine, parser=parser)
+
+
+@pytest.mark.parametrize("cmp", ("and", "or"))
+@pytest.mark.parametrize("lhs", (int, float))
+@pytest.mark.parametrize("rhs", (int, float))
+def test_bool_ops_fails_on_scalars(lhs, cmp, rhs, engine, parser):
+    gen = {
+        int: lambda: np.random.default_rng(2).integers(10),
+        float: np.random.default_rng(2).standard_normal,
+    }
+
+    mid = gen[lhs]()  # noqa: F841
+    lhs = gen[lhs]()
+    rhs = gen[rhs]()
+
+    ex1 = f"lhs {cmp} mid {cmp} rhs"
+    ex2 = f"lhs {cmp} mid and mid {cmp} rhs"
+    ex3 = f"(lhs {cmp} mid) & (mid {cmp} rhs)"
+    for ex in (ex1, ex2, ex3):
+        msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
+        with pytest.raises(NotImplementedError, match=msg):
+            pd.eval(ex, engine=engine, parser=parser)
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        "'x'",
+        "...",
+    ],
+)
+def test_equals_various(other):
+    df = DataFrame({"A": ["a", "b", "c"]}, dtype=object)
+    result = df.eval(f"A == {other}")
+    expected = Series([False, False, False], name="A")
+    if USE_NUMEXPR:
+        # https://github.com/pandas-dev/pandas/issues/10239
+        # lose name with numexpr engine. Remove when that's fixed.
+        expected.name = None
+    tm.assert_series_equal(result, expected)
+
+
+def test_inf(engine, parser):
+    s = "inf + 1"
+    expected = np.inf
+    result = pd.eval(s, engine=engine, parser=parser)
+    assert result == expected
+
+
+@pytest.mark.parametrize("column", ["Temp(°C)", "Capacitance(μF)"])
+def test_query_token(engine, column):
+    # See: https://github.com/pandas-dev/pandas/pull/42826
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((5, 2)), columns=[column, "b"]
+    )
+    expected = df[df[column] > 5]
+    query_string = f"`{column}` > 5"
+    result = df.query(query_string, engine=engine)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_negate_lt_eq_le(engine, parser):
+    df = DataFrame([[0, 10], [1, 20]], columns=["cat", "count"])
+    expected = df[~(df.cat > 0)]
+
+    result = df.query("~(cat > 0)", engine=engine, parser=parser)
+    tm.assert_frame_equal(result, expected)
+
+    if parser == "python":
+        msg = "'Not' nodes are not implemented"
+        with pytest.raises(NotImplementedError, match=msg):
+            df.query("not (cat > 0)", engine=engine, parser=parser)
+    else:
+        result = df.query("not (cat > 0)", engine=engine, parser=parser)
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "column",
+    DEFAULT_GLOBALS.keys(),
+)
+def test_eval_no_support_column_name(request, column):
+    # GH 44603
+    if column in ["True", "False", "inf", "Inf"]:
+        request.applymarker(
+            pytest.mark.xfail(
+                raises=KeyError,
+                reason=f"GH 47859 DataFrame eval not supported with {column}",
+            )
+        )
+
+    df = DataFrame(
+        np.random.default_rng(2).integers(0, 100, size=(10, 2)),
+        columns=[column, "col1"],
+    )
+    expected = df[df[column] > 6]
+    result = df.query(f"{column}>6")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_set_inplace(using_copy_on_write, warn_copy_on_write):
+    # https://github.com/pandas-dev/pandas/issues/47449
+    # Ensure we don't only update the DataFrame inplace, but also the actual
+    # column values, such that references to this column also get updated
+    df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
+    result_view = df[:]
+    ser = df["A"]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        df.eval("A = B + C", inplace=True)
+    expected = DataFrame({"A": [11, 13, 15], "B": [4, 5, 6], "C": [7, 8, 9]})
+    tm.assert_frame_equal(df, expected)
+    if not using_copy_on_write:
+        tm.assert_series_equal(ser, expected["A"])
+        tm.assert_series_equal(result_view["A"], expected["A"])
+    else:
+        expected = Series([1, 2, 3], name="A")
+        tm.assert_series_equal(ser, expected)
+        tm.assert_series_equal(result_view["A"], expected)
+
+
+class TestValidate:
+    @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
+    def test_validate_bool_args(self, value):
+        msg = 'For argument "inplace" expected type bool, received type'
+        with pytest.raises(ValueError, match=msg):
+            pd.eval("2+2", inplace=value)

vllm/lib/python3.10/site-packages/pandas/tests/series/accessors/test_cat_accessor.py

@@ -0,0 +1,258 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays.categorical import CategoricalAccessor
+from pandas.core.indexes.accessors import Properties
+
+
+class TestCatAccessor:
+    @pytest.mark.parametrize(
+        "method",
+        [
+            lambda x: x.cat.set_categories([1, 2, 3]),
+            lambda x: x.cat.reorder_categories([2, 3, 1], ordered=True),
+            lambda x: x.cat.rename_categories([1, 2, 3]),
+            lambda x: x.cat.remove_unused_categories(),
+            lambda x: x.cat.remove_categories([2]),
+            lambda x: x.cat.add_categories([4]),
+            lambda x: x.cat.as_ordered(),
+            lambda x: x.cat.as_unordered(),
+        ],
+    )
+    def test_getname_categorical_accessor(self, method):
+        # GH#17509
+        ser = Series([1, 2, 3], name="A").astype("category")
+        expected = "A"
+        result = method(ser).name
+        assert result == expected
+
+    def test_cat_accessor(self):
+        ser = Series(Categorical(["a", "b", np.nan, "a"]))
+        tm.assert_index_equal(ser.cat.categories, Index(["a", "b"]))
+        assert not ser.cat.ordered, False
+
+        exp = Categorical(["a", "b", np.nan, "a"], categories=["b", "a"])
+
+        res = ser.cat.set_categories(["b", "a"])
+        tm.assert_categorical_equal(res.values, exp)
+
+        ser[:] = "a"
+        ser = ser.cat.remove_unused_categories()
+        tm.assert_index_equal(ser.cat.categories, Index(["a"]))
+
+    def test_cat_accessor_api(self):
+        # GH#9322
+
+        assert Series.cat is CategoricalAccessor
+        ser = Series(list("aabbcde")).astype("category")
+        assert isinstance(ser.cat, CategoricalAccessor)
+
+        invalid = Series([1])
+        with pytest.raises(AttributeError, match="only use .cat accessor"):
+            invalid.cat
+        assert not hasattr(invalid, "cat")
+
+    def test_cat_accessor_no_new_attributes(self):
+        # https://github.com/pandas-dev/pandas/issues/10673
+        cat = Series(list("aabbcde")).astype("category")
+        with pytest.raises(AttributeError, match="You cannot add any new attribute"):
+            cat.cat.xlabel = "a"
+
+    def test_categorical_delegations(self):
+        # invalid accessor
+        msg = r"Can only use \.cat accessor with a 'category' dtype"
+        with pytest.raises(AttributeError, match=msg):
+            Series([1, 2, 3]).cat
+        with pytest.raises(AttributeError, match=msg):
+            Series([1, 2, 3]).cat()
+        with pytest.raises(AttributeError, match=msg):
+            Series(["a", "b", "c"]).cat
+        with pytest.raises(AttributeError, match=msg):
+            Series(np.arange(5.0)).cat
+        with pytest.raises(AttributeError, match=msg):
+            Series([Timestamp("20130101")]).cat
+
+        # Series should delegate calls to '.categories', '.codes', '.ordered'
+        # and the methods '.set_categories()' 'drop_unused_categories()' to the
+        # categorical
+        ser = Series(Categorical(["a", "b", "c", "a"], ordered=True))
+        exp_categories = Index(["a", "b", "c"])
+        tm.assert_index_equal(ser.cat.categories, exp_categories)
+        ser = ser.cat.rename_categories([1, 2, 3])
+        exp_categories = Index([1, 2, 3])
+        tm.assert_index_equal(ser.cat.categories, exp_categories)
+
+        exp_codes = Series([0, 1, 2, 0], dtype="int8")
+        tm.assert_series_equal(ser.cat.codes, exp_codes)
+
+        assert ser.cat.ordered
+        ser = ser.cat.as_unordered()
+        assert not ser.cat.ordered
+
+        ser = ser.cat.as_ordered()
+        assert ser.cat.ordered
+
+        # reorder
+        ser = Series(Categorical(["a", "b", "c", "a"], ordered=True))
+        exp_categories = Index(["c", "b", "a"])
+        exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_)
+        ser = ser.cat.set_categories(["c", "b", "a"])
+        tm.assert_index_equal(ser.cat.categories, exp_categories)
+        tm.assert_numpy_array_equal(ser.values.__array__(), exp_values)
+        tm.assert_numpy_array_equal(ser.__array__(), exp_values)
+
+        # remove unused categories
+        ser = Series(Categorical(["a", "b", "b", "a"], categories=["a", "b", "c"]))
+        exp_categories = Index(["a", "b"])
+        exp_values = np.array(["a", "b", "b", "a"], dtype=np.object_)
+        ser = ser.cat.remove_unused_categories()
+        tm.assert_index_equal(ser.cat.categories, exp_categories)
+        tm.assert_numpy_array_equal(ser.values.__array__(), exp_values)
+        tm.assert_numpy_array_equal(ser.__array__(), exp_values)
+
+        # This method is likely to be confused, so test that it raises an error
+        # on wrong inputs:
+        msg = "'Series' object has no attribute 'set_categories'"
+        with pytest.raises(AttributeError, match=msg):
+            ser.set_categories([4, 3, 2, 1])
+
+        # right: ser.cat.set_categories([4,3,2,1])
+
+        # GH#18862 (let Series.cat.rename_categories take callables)
+        ser = Series(Categorical(["a", "b", "c", "a"], ordered=True))
+        result = ser.cat.rename_categories(lambda x: x.upper())
+        expected = Series(
+            Categorical(["A", "B", "C", "A"], categories=["A", "B", "C"], ordered=True)
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [
+            date_range("1/1/2015", periods=5),
+            date_range("1/1/2015", periods=5, tz="MET"),
+            period_range("1/1/2015", freq="D", periods=5),
+            timedelta_range("1 days", "10 days"),
+        ],
+    )
+    def test_dt_accessor_api_for_categorical(self, idx):
+        # https://github.com/pandas-dev/pandas/issues/10661
+
+        ser = Series(idx)
+        cat = ser.astype("category")
+
+        # only testing field (like .day)
+        # and bool (is_month_start)
+        attr_names = type(ser._values)._datetimelike_ops
+
+        assert isinstance(cat.dt, Properties)
+
+        special_func_defs = [
+            ("strftime", ("%Y-%m-%d",), {}),
+            ("round", ("D",), {}),
+            ("floor", ("D",), {}),
+            ("ceil", ("D",), {}),
+            ("asfreq", ("D",), {}),
+            ("as_unit", ("s"), {}),
+        ]
+        if idx.dtype == "M8[ns]":
+            # exclude dt64tz since that is already localized and would raise
+            tup = ("tz_localize", ("UTC",), {})
+            special_func_defs.append(tup)
+        elif idx.dtype.kind == "M":
+            # exclude dt64 since that is not localized so would raise
+            tup = ("tz_convert", ("EST",), {})
+            special_func_defs.append(tup)
+
+        _special_func_names = [f[0] for f in special_func_defs]
+
+        _ignore_names = ["components", "tz_localize", "tz_convert"]
+
+        func_names = [
+            fname
+            for fname in dir(ser.dt)
+            if not (
+                fname.startswith("_")
+                or fname in attr_names
+                or fname in _special_func_names
+                or fname in _ignore_names
+            )
+        ]
+
+        func_defs = [(fname, (), {}) for fname in func_names]
+        func_defs.extend(
+            f_def for f_def in special_func_defs if f_def[0] in dir(ser.dt)
+        )
+
+        for func, args, kwargs in func_defs:
+            warn_cls = []
+            if func == "to_period" and getattr(idx, "tz", None) is not None:
+                # dropping TZ
+                warn_cls.append(UserWarning)
+            if func == "to_pydatetime":
+                # deprecated to return Index[object]
+                warn_cls.append(FutureWarning)
+            if warn_cls:
+                warn_cls = tuple(warn_cls)
+            else:
+                warn_cls = None
+            with tm.assert_produces_warning(warn_cls):
+                res = getattr(cat.dt, func)(*args, **kwargs)
+                exp = getattr(ser.dt, func)(*args, **kwargs)
+
+            tm.assert_equal(res, exp)
+
+        for attr in attr_names:
+            res = getattr(cat.dt, attr)
+            exp = getattr(ser.dt, attr)
+
+            tm.assert_equal(res, exp)
+
+    def test_dt_accessor_api_for_categorical_invalid(self):
+        invalid = Series([1, 2, 3]).astype("category")
+        msg = "Can only use .dt accessor with datetimelike"
+
+        with pytest.raises(AttributeError, match=msg):
+            invalid.dt
+        assert not hasattr(invalid, "str")
+
+    def test_set_categories_setitem(self):
+        # GH#43334
+
+        df = DataFrame({"Survived": [1, 0, 1], "Sex": [0, 1, 1]}, dtype="category")
+
+        df["Survived"] = df["Survived"].cat.rename_categories(["No", "Yes"])
+        df["Sex"] = df["Sex"].cat.rename_categories(["female", "male"])
+
+        # values should not be coerced to NaN
+        assert list(df["Sex"]) == ["female", "male", "male"]
+        assert list(df["Survived"]) == ["Yes", "No", "Yes"]
+
+        df["Sex"] = Categorical(df["Sex"], categories=["female", "male"], ordered=False)
+        df["Survived"] = Categorical(
+            df["Survived"], categories=["No", "Yes"], ordered=False
+        )
+
+        # values should not be coerced to NaN
+        assert list(df["Sex"]) == ["female", "male", "male"]
+        assert list(df["Survived"]) == ["Yes", "No", "Yes"]
+
+    def test_categorical_of_booleans_is_boolean(self):
+        # https://github.com/pandas-dev/pandas/issues/46313
+        df = DataFrame(
+            {"int_cat": [1, 2, 3], "bool_cat": [True, False, False]}, dtype="category"
+        )
+        value = df["bool_cat"].cat.categories.dtype
+        expected = np.dtype(np.bool_)
+        assert value is expected

vllm/lib/python3.10/site-packages/pandas/tests/series/accessors/test_dt_accessor.py

@@ -0,0 +1,843 @@
+import calendar
+from datetime import (
+    date,
+    datetime,
+    time,
+)
+import locale
+import unicodedata
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs.tslibs.timezones import maybe_get_tz
+from pandas.errors import SettingWithCopyError
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_list_like,
+)
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Period,
+    PeriodIndex,
+    Series,
+    TimedeltaIndex,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+
+ok_for_period = PeriodArray._datetimelike_ops
+ok_for_period_methods = ["strftime", "to_timestamp", "asfreq"]
+ok_for_dt = DatetimeArray._datetimelike_ops
+ok_for_dt_methods = [
+    "to_period",
+    "to_pydatetime",
+    "tz_localize",
+    "tz_convert",
+    "normalize",
+    "strftime",
+    "round",
+    "floor",
+    "ceil",
+    "day_name",
+    "month_name",
+    "isocalendar",
+    "as_unit",
+]
+ok_for_td = TimedeltaArray._datetimelike_ops
+ok_for_td_methods = [
+    "components",
+    "to_pytimedelta",
+    "total_seconds",
+    "round",
+    "floor",
+    "ceil",
+    "as_unit",
+]
+
+
+def get_dir(ser):
+    # check limited display api
+    results = [r for r in ser.dt.__dir__() if not r.startswith("_")]
+    return sorted(set(results))
+
+
+class TestSeriesDatetimeValues:
+    def _compare(self, ser, name):
+        # GH 7207, 11128
+        # test .dt namespace accessor
+
+        def get_expected(ser, prop):
+            result = getattr(Index(ser._values), prop)
+            if isinstance(result, np.ndarray):
+                if is_integer_dtype(result):
+                    result = result.astype("int64")
+            elif not is_list_like(result) or isinstance(result, DataFrame):
+                return result
+            return Series(result, index=ser.index, name=ser.name)
+
+        left = getattr(ser.dt, name)
+        right = get_expected(ser, name)
+        if not (is_list_like(left) and is_list_like(right)):
+            assert left == right
+        elif isinstance(left, DataFrame):
+            tm.assert_frame_equal(left, right)
+        else:
+            tm.assert_series_equal(left, right)
+
+    @pytest.mark.parametrize("freq", ["D", "s", "ms"])
+    def test_dt_namespace_accessor_datetime64(self, freq):
+        # GH#7207, GH#11128
+        # test .dt namespace accessor
+
+        # datetimeindex
+        dti = date_range("20130101", periods=5, freq=freq)
+        ser = Series(dti, name="xxx")
+
+        for prop in ok_for_dt:
+            # we test freq below
+            if prop != "freq":
+                self._compare(ser, prop)
+
+        for prop in ok_for_dt_methods:
+            getattr(ser.dt, prop)
+
+        msg = "The behavior of DatetimeProperties.to_pydatetime is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.dt.to_pydatetime()
+        assert isinstance(result, np.ndarray)
+        assert result.dtype == object
+
+        result = ser.dt.tz_localize("US/Eastern")
+        exp_values = DatetimeIndex(ser.values).tz_localize("US/Eastern")
+        expected = Series(exp_values, index=ser.index, name="xxx")
+        tm.assert_series_equal(result, expected)
+
+        tz_result = result.dt.tz
+        assert str(tz_result) == "US/Eastern"
+        freq_result = ser.dt.freq
+        assert freq_result == DatetimeIndex(ser.values, freq="infer").freq
+
+        # let's localize, then convert
+        result = ser.dt.tz_localize("UTC").dt.tz_convert("US/Eastern")
+        exp_values = (
+            DatetimeIndex(ser.values).tz_localize("UTC").tz_convert("US/Eastern")
+        )
+        expected = Series(exp_values, index=ser.index, name="xxx")
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_namespace_accessor_datetime64tz(self):
+        # GH#7207, GH#11128
+        # test .dt namespace accessor
+
+        # datetimeindex with tz
+        dti = date_range("20130101", periods=5, tz="US/Eastern")
+        ser = Series(dti, name="xxx")
+        for prop in ok_for_dt:
+            # we test freq below
+            if prop != "freq":
+                self._compare(ser, prop)
+
+        for prop in ok_for_dt_methods:
+            getattr(ser.dt, prop)
+
+        msg = "The behavior of DatetimeProperties.to_pydatetime is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.dt.to_pydatetime()
+        assert isinstance(result, np.ndarray)
+        assert result.dtype == object
+
+        result = ser.dt.tz_convert("CET")
+        expected = Series(ser._values.tz_convert("CET"), index=ser.index, name="xxx")
+        tm.assert_series_equal(result, expected)
+
+        tz_result = result.dt.tz
+        assert str(tz_result) == "CET"
+        freq_result = ser.dt.freq
+        assert freq_result == DatetimeIndex(ser.values, freq="infer").freq
+
+    def test_dt_namespace_accessor_timedelta(self):
+        # GH#7207, GH#11128
+        # test .dt namespace accessor
+
+        # timedelta index
+        cases = [
+            Series(
+                timedelta_range("1 day", periods=5), index=list("abcde"), name="xxx"
+            ),
+            Series(timedelta_range("1 day 01:23:45", periods=5, freq="s"), name="xxx"),
+            Series(
+                timedelta_range("2 days 01:23:45.012345", periods=5, freq="ms"),
+                name="xxx",
+            ),
+        ]
+        for ser in cases:
+            for prop in ok_for_td:
+                # we test freq below
+                if prop != "freq":
+                    self._compare(ser, prop)
+
+            for prop in ok_for_td_methods:
+                getattr(ser.dt, prop)
+
+            result = ser.dt.components
+            assert isinstance(result, DataFrame)
+            tm.assert_index_equal(result.index, ser.index)
+
+            result = ser.dt.to_pytimedelta()
+            assert isinstance(result, np.ndarray)
+            assert result.dtype == object
+
+            result = ser.dt.total_seconds()
+            assert isinstance(result, Series)
+            assert result.dtype == "float64"
+
+            freq_result = ser.dt.freq
+            assert freq_result == TimedeltaIndex(ser.values, freq="infer").freq
+
+    def test_dt_namespace_accessor_period(self):
+        # GH#7207, GH#11128
+        # test .dt namespace accessor
+
+        # periodindex
+        pi = period_range("20130101", periods=5, freq="D")
+        ser = Series(pi, name="xxx")
+
+        for prop in ok_for_period:
+            # we test freq below
+            if prop != "freq":
+                self._compare(ser, prop)
+
+        for prop in ok_for_period_methods:
+            getattr(ser.dt, prop)
+
+        freq_result = ser.dt.freq
+        assert freq_result == PeriodIndex(ser.values).freq
+
+    def test_dt_namespace_accessor_index_and_values(self):
+        # both
+        index = date_range("20130101", periods=3, freq="D")
+        dti = date_range("20140204", periods=3, freq="s")
+        ser = Series(dti, index=index, name="xxx")
+        exp = Series(
+            np.array([2014, 2014, 2014], dtype="int32"), index=index, name="xxx"
+        )
+        tm.assert_series_equal(ser.dt.year, exp)
+
+        exp = Series(np.array([2, 2, 2], dtype="int32"), index=index, name="xxx")
+        tm.assert_series_equal(ser.dt.month, exp)
+
+        exp = Series(np.array([0, 1, 2], dtype="int32"), index=index, name="xxx")
+        tm.assert_series_equal(ser.dt.second, exp)
+
+        exp = Series([ser.iloc[0]] * 3, index=index, name="xxx")
+        tm.assert_series_equal(ser.dt.normalize(), exp)
+
+    def test_dt_accessor_limited_display_api(self):
+        # tznaive
+        ser = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
+        results = get_dir(ser)
+        tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods)))
+
+        # tzaware
+        ser = Series(date_range("2015-01-01", "2016-01-01", freq="min"), name="xxx")
+        ser = ser.dt.tz_localize("UTC").dt.tz_convert("America/Chicago")
+        results = get_dir(ser)
+        tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods)))
+
+        # Period
+        idx = period_range("20130101", periods=5, freq="D", name="xxx").astype(object)
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            ser = Series(idx)
+        results = get_dir(ser)
+        tm.assert_almost_equal(
+            results, sorted(set(ok_for_period + ok_for_period_methods))
+        )
+
+    def test_dt_accessor_ambiguous_freq_conversions(self):
+        # GH#11295
+        # ambiguous time error on the conversions
+        ser = Series(date_range("2015-01-01", "2016-01-01", freq="min"), name="xxx")
+        ser = ser.dt.tz_localize("UTC").dt.tz_convert("America/Chicago")
+
+        exp_values = date_range(
+            "2015-01-01", "2016-01-01", freq="min", tz="UTC"
+        ).tz_convert("America/Chicago")
+        # freq not preserved by tz_localize above
+        exp_values = exp_values._with_freq(None)
+        expected = Series(exp_values, name="xxx")
+        tm.assert_series_equal(ser, expected)
+
+    def test_dt_accessor_not_writeable(self, using_copy_on_write, warn_copy_on_write):
+        # no setting allowed
+        ser = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
+        with pytest.raises(ValueError, match="modifications"):
+            ser.dt.hour = 5
+
+        # trying to set a copy
+        msg = "modifications to a property of a datetimelike.+not supported"
+        with pd.option_context("chained_assignment", "raise"):
+            if using_copy_on_write:
+                with tm.raises_chained_assignment_error():
+                    ser.dt.hour[0] = 5
+            elif warn_copy_on_write:
+                with tm.assert_produces_warning(
+                    FutureWarning, match="ChainedAssignmentError"
+                ):
+                    ser.dt.hour[0] = 5
+            else:
+                with pytest.raises(SettingWithCopyError, match=msg):
+                    ser.dt.hour[0] = 5
+
+    @pytest.mark.parametrize(
+        "method, dates",
+        [
+            ["round", ["2012-01-02", "2012-01-02", "2012-01-01"]],
+            ["floor", ["2012-01-01", "2012-01-01", "2012-01-01"]],
+            ["ceil", ["2012-01-02", "2012-01-02", "2012-01-02"]],
+        ],
+    )
+    def test_dt_round(self, method, dates):
+        # round
+        ser = Series(
+            pd.to_datetime(
+                ["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"]
+            ),
+            name="xxx",
+        )
+        result = getattr(ser.dt, method)("D")
+        expected = Series(pd.to_datetime(dates), name="xxx")
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_round_tz(self):
+        ser = Series(
+            pd.to_datetime(
+                ["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"]
+            ),
+            name="xxx",
+        )
+        result = ser.dt.tz_localize("UTC").dt.tz_convert("US/Eastern").dt.round("D")
+
+        exp_values = pd.to_datetime(
+            ["2012-01-01", "2012-01-01", "2012-01-01"]
+        ).tz_localize("US/Eastern")
+        expected = Series(exp_values, name="xxx")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("method", ["ceil", "round", "floor"])
+    def test_dt_round_tz_ambiguous(self, method):
+        # GH 18946 round near "fall back" DST
+        df1 = DataFrame(
+            [
+                pd.to_datetime("2017-10-29 02:00:00+02:00", utc=True),
+                pd.to_datetime("2017-10-29 02:00:00+01:00", utc=True),
+                pd.to_datetime("2017-10-29 03:00:00+01:00", utc=True),
+            ],
+            columns=["date"],
+        )
+        df1["date"] = df1["date"].dt.tz_convert("Europe/Madrid")
+        # infer
+        result = getattr(df1.date.dt, method)("h", ambiguous="infer")
+        expected = df1["date"]
+        tm.assert_series_equal(result, expected)
+
+        # bool-array
+        result = getattr(df1.date.dt, method)("h", ambiguous=[True, False, False])
+        tm.assert_series_equal(result, expected)
+
+        # NaT
+        result = getattr(df1.date.dt, method)("h", ambiguous="NaT")
+        expected = df1["date"].copy()
+        expected.iloc[0:2] = pd.NaT
+        tm.assert_series_equal(result, expected)
+
+        # raise
+        with tm.external_error_raised(pytz.AmbiguousTimeError):
+            getattr(df1.date.dt, method)("h", ambiguous="raise")
+
+    @pytest.mark.parametrize(
+        "method, ts_str, freq",
+        [
+            ["ceil", "2018-03-11 01:59:00-0600", "5min"],
+            ["round", "2018-03-11 01:59:00-0600", "5min"],
+            ["floor", "2018-03-11 03:01:00-0500", "2h"],
+        ],
+    )
+    def test_dt_round_tz_nonexistent(self, method, ts_str, freq):
+        # GH 23324 round near "spring forward" DST
+        ser = Series([pd.Timestamp(ts_str, tz="America/Chicago")])
+        result = getattr(ser.dt, method)(freq, nonexistent="shift_forward")
+        expected = Series([pd.Timestamp("2018-03-11 03:00:00", tz="America/Chicago")])
+        tm.assert_series_equal(result, expected)
+
+        result = getattr(ser.dt, method)(freq, nonexistent="NaT")
+        expected = Series([pd.NaT]).dt.tz_localize(result.dt.tz)
+        tm.assert_series_equal(result, expected)
+
+        with pytest.raises(pytz.NonExistentTimeError, match="2018-03-11 02:00:00"):
+            getattr(ser.dt, method)(freq, nonexistent="raise")
+
+    @pytest.mark.parametrize("freq", ["ns", "us", "1000us"])
+    def test_dt_round_nonnano_higher_resolution_no_op(self, freq):
+        # GH 52761
+        ser = Series(
+            ["2020-05-31 08:00:00", "2000-12-31 04:00:05", "1800-03-14 07:30:20"],
+            dtype="datetime64[ms]",
+        )
+        expected = ser.copy()
+        result = ser.dt.round(freq)
+        tm.assert_series_equal(result, expected)
+
+        assert not np.shares_memory(ser.array._ndarray, result.array._ndarray)
+
+    def test_dt_namespace_accessor_categorical(self):
+        # GH 19468
+        dti = DatetimeIndex(["20171111", "20181212"]).repeat(2)
+        ser = Series(pd.Categorical(dti), name="foo")
+        result = ser.dt.year
+        expected = Series([2017, 2017, 2018, 2018], dtype="int32", name="foo")
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_tz_localize_categorical(self, tz_aware_fixture):
+        # GH 27952
+        tz = tz_aware_fixture
+        datetimes = Series(
+            ["2019-01-01", "2019-01-01", "2019-01-02"], dtype="datetime64[ns]"
+        )
+        categorical = datetimes.astype("category")
+        result = categorical.dt.tz_localize(tz)
+        expected = datetimes.dt.tz_localize(tz)
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_tz_convert_categorical(self, tz_aware_fixture):
+        # GH 27952
+        tz = tz_aware_fixture
+        datetimes = Series(
+            ["2019-01-01", "2019-01-01", "2019-01-02"], dtype="datetime64[ns, MET]"
+        )
+        categorical = datetimes.astype("category")
+        result = categorical.dt.tz_convert(tz)
+        expected = datetimes.dt.tz_convert(tz)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("accessor", ["year", "month", "day"])
+    def test_dt_other_accessors_categorical(self, accessor):
+        # GH 27952
+        datetimes = Series(
+            ["2018-01-01", "2018-01-01", "2019-01-02"], dtype="datetime64[ns]"
+        )
+        categorical = datetimes.astype("category")
+        result = getattr(categorical.dt, accessor)
+        expected = getattr(datetimes.dt, accessor)
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_accessor_no_new_attributes(self):
+        # https://github.com/pandas-dev/pandas/issues/10673
+        ser = Series(date_range("20130101", periods=5, freq="D"))
+        with pytest.raises(AttributeError, match="You cannot add any new attribute"):
+            ser.dt.xlabel = "a"
+
+    # error: Unsupported operand types for + ("List[None]" and "List[str]")
+    @pytest.mark.parametrize(
+        "time_locale", [None] + tm.get_locales()  # type: ignore[operator]
+    )
+    def test_dt_accessor_datetime_name_accessors(self, time_locale):
+        # Test Monday -> Sunday and January -> December, in that sequence
+        if time_locale is None:
+            # If the time_locale is None, day-name and month_name should
+            # return the english attributes
+            expected_days = [
+                "Monday",
+                "Tuesday",
+                "Wednesday",
+                "Thursday",
+                "Friday",
+                "Saturday",
+                "Sunday",
+            ]
+            expected_months = [
+                "January",
+                "February",
+                "March",
+                "April",
+                "May",
+                "June",
+                "July",
+                "August",
+                "September",
+                "October",
+                "November",
+                "December",
+            ]
+        else:
+            with tm.set_locale(time_locale, locale.LC_TIME):
+                expected_days = calendar.day_name[:]
+                expected_months = calendar.month_name[1:]
+
+        ser = Series(date_range(freq="D", start=datetime(1998, 1, 1), periods=365))
+        english_days = [
+            "Monday",
+            "Tuesday",
+            "Wednesday",
+            "Thursday",
+            "Friday",
+            "Saturday",
+            "Sunday",
+        ]
+        for day, name, eng_name in zip(range(4, 11), expected_days, english_days):
+            name = name.capitalize()
+            assert ser.dt.day_name(locale=time_locale)[day] == name
+            assert ser.dt.day_name(locale=None)[day] == eng_name
+        ser = pd.concat([ser, Series([pd.NaT])])
+        assert np.isnan(ser.dt.day_name(locale=time_locale).iloc[-1])
+
+        ser = Series(date_range(freq="ME", start="2012", end="2013"))
+        result = ser.dt.month_name(locale=time_locale)
+        expected = Series([month.capitalize() for month in expected_months])
+
+        # work around https://github.com/pandas-dev/pandas/issues/22342
+        result = result.str.normalize("NFD")
+        expected = expected.str.normalize("NFD")
+
+        tm.assert_series_equal(result, expected)
+
+        for s_date, expected in zip(ser, expected_months):
+            result = s_date.month_name(locale=time_locale)
+            expected = expected.capitalize()
+
+            result = unicodedata.normalize("NFD", result)
+            expected = unicodedata.normalize("NFD", expected)
+
+            assert result == expected
+
+        ser = pd.concat([ser, Series([pd.NaT])])
+        assert np.isnan(ser.dt.month_name(locale=time_locale).iloc[-1])
+
+    def test_strftime(self):
+        # GH 10086
+        ser = Series(date_range("20130101", periods=5))
+        result = ser.dt.strftime("%Y/%m/%d")
+        expected = Series(
+            ["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
+        )
+        tm.assert_series_equal(result, expected)
+
+        ser = Series(date_range("2015-02-03 11:22:33.4567", periods=5))
+        result = ser.dt.strftime("%Y/%m/%d %H-%M-%S")
+        expected = Series(
+            [
+                "2015/02/03 11-22-33",
+                "2015/02/04 11-22-33",
+                "2015/02/05 11-22-33",
+                "2015/02/06 11-22-33",
+                "2015/02/07 11-22-33",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+        ser = Series(period_range("20130101", periods=5))
+        result = ser.dt.strftime("%Y/%m/%d")
+        expected = Series(
+            ["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
+        )
+        tm.assert_series_equal(result, expected)
+
+        ser = Series(period_range("2015-02-03 11:22:33.4567", periods=5, freq="s"))
+        result = ser.dt.strftime("%Y/%m/%d %H-%M-%S")
+        expected = Series(
+            [
+                "2015/02/03 11-22-33",
+                "2015/02/03 11-22-34",
+                "2015/02/03 11-22-35",
+                "2015/02/03 11-22-36",
+                "2015/02/03 11-22-37",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_strftime_dt64_days(self):
+        ser = Series(date_range("20130101", periods=5))
+        ser.iloc[0] = pd.NaT
+        result = ser.dt.strftime("%Y/%m/%d")
+        expected = Series(
+            [np.nan, "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
+        )
+        tm.assert_series_equal(result, expected)
+
+        datetime_index = date_range("20150301", periods=5)
+        result = datetime_index.strftime("%Y/%m/%d")
+
+        expected = Index(
+            ["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"],
+            dtype=np.object_,
+        )
+        # dtype may be S10 or U10 depending on python version
+        tm.assert_index_equal(result, expected)
+
+    def test_strftime_period_days(self, using_infer_string):
+        period_index = period_range("20150301", periods=5)
+        result = period_index.strftime("%Y/%m/%d")
+        expected = Index(
+            ["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"],
+            dtype="=U10",
+        )
+        if using_infer_string:
+            expected = expected.astype("string[pyarrow_numpy]")
+        tm.assert_index_equal(result, expected)
+
+    def test_strftime_dt64_microsecond_resolution(self):
+        ser = Series([datetime(2013, 1, 1, 2, 32, 59), datetime(2013, 1, 2, 14, 32, 1)])
+        result = ser.dt.strftime("%Y-%m-%d %H:%M:%S")
+        expected = Series(["2013-01-01 02:32:59", "2013-01-02 14:32:01"])
+        tm.assert_series_equal(result, expected)
+
+    def test_strftime_period_hours(self):
+        ser = Series(period_range("20130101", periods=4, freq="h"))
+        result = ser.dt.strftime("%Y/%m/%d %H:%M:%S")
+        expected = Series(
+            [
+                "2013/01/01 00:00:00",
+                "2013/01/01 01:00:00",
+                "2013/01/01 02:00:00",
+                "2013/01/01 03:00:00",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_strftime_period_minutes(self):
+        ser = Series(period_range("20130101", periods=4, freq="ms"))
+        result = ser.dt.strftime("%Y/%m/%d %H:%M:%S.%l")
+        expected = Series(
+            [
+                "2013/01/01 00:00:00.000",
+                "2013/01/01 00:00:00.001",
+                "2013/01/01 00:00:00.002",
+                "2013/01/01 00:00:00.003",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            DatetimeIndex(["2019-01-01", pd.NaT]),
+            PeriodIndex(["2019-01-01", pd.NaT], dtype="period[D]"),
+        ],
+    )
+    def test_strftime_nat(self, data):
+        # GH 29578
+        ser = Series(data)
+        result = ser.dt.strftime("%Y-%m-%d")
+        expected = Series(["2019-01-01", np.nan])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data", [DatetimeIndex([pd.NaT]), PeriodIndex([pd.NaT], dtype="period[D]")]
+    )
+    def test_strftime_all_nat(self, data):
+        # https://github.com/pandas-dev/pandas/issues/45858
+        ser = Series(data)
+        with tm.assert_produces_warning(None):
+            result = ser.dt.strftime("%Y-%m-%d")
+        expected = Series([np.nan], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_valid_dt_with_missing_values(self):
+        # GH 8689
+        ser = Series(date_range("20130101", periods=5, freq="D"))
+        ser.iloc[2] = pd.NaT
+
+        for attr in ["microsecond", "nanosecond", "second", "minute", "hour", "day"]:
+            expected = getattr(ser.dt, attr).copy()
+            expected.iloc[2] = np.nan
+            result = getattr(ser.dt, attr)
+            tm.assert_series_equal(result, expected)
+
+        result = ser.dt.date
+        expected = Series(
+            [
+                date(2013, 1, 1),
+                date(2013, 1, 2),
+                pd.NaT,
+                date(2013, 1, 4),
+                date(2013, 1, 5),
+            ],
+            dtype="object",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = ser.dt.time
+        expected = Series([time(0), time(0), pd.NaT, time(0), time(0)], dtype="object")
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_accessor_api(self):
+        # GH 9322
+        from pandas.core.indexes.accessors import (
+            CombinedDatetimelikeProperties,
+            DatetimeProperties,
+        )
+
+        assert Series.dt is CombinedDatetimelikeProperties
+
+        ser = Series(date_range("2000-01-01", periods=3))
+        assert isinstance(ser.dt, DatetimeProperties)
+
+    @pytest.mark.parametrize(
+        "ser",
+        [
+            Series(np.arange(5)),
+            Series(list("abcde")),
+            Series(np.random.default_rng(2).standard_normal(5)),
+        ],
+    )
+    def test_dt_accessor_invalid(self, ser):
+        # GH#9322 check that series with incorrect dtypes don't have attr
+        with pytest.raises(AttributeError, match="only use .dt accessor"):
+            ser.dt
+        assert not hasattr(ser, "dt")
+
+    def test_dt_accessor_updates_on_inplace(self):
+        ser = Series(date_range("2018-01-01", periods=10))
+        ser[2] = None
+        return_value = ser.fillna(pd.Timestamp("2018-01-01"), inplace=True)
+        assert return_value is None
+        result = ser.dt.date
+        assert result[0] == result[2]
+
+    def test_date_tz(self):
+        # GH11757
+        rng = DatetimeIndex(
+            ["2014-04-04 23:56", "2014-07-18 21:24", "2015-11-22 22:14"],
+            tz="US/Eastern",
+        )
+        ser = Series(rng)
+        expected = Series([date(2014, 4, 4), date(2014, 7, 18), date(2015, 11, 22)])
+        tm.assert_series_equal(ser.dt.date, expected)
+        tm.assert_series_equal(ser.apply(lambda x: x.date()), expected)
+
+    def test_dt_timetz_accessor(self, tz_naive_fixture):
+        # GH21358
+        tz = maybe_get_tz(tz_naive_fixture)
+
+        dtindex = DatetimeIndex(
+            ["2014-04-04 23:56", "2014-07-18 21:24", "2015-11-22 22:14"], tz=tz
+        )
+        ser = Series(dtindex)
+        expected = Series(
+            [time(23, 56, tzinfo=tz), time(21, 24, tzinfo=tz), time(22, 14, tzinfo=tz)]
+        )
+        result = ser.dt.timetz
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "input_series, expected_output",
+        [
+            [["2020-01-01"], [[2020, 1, 3]]],
+            [[pd.NaT], [[np.nan, np.nan, np.nan]]],
+            [["2019-12-31", "2019-12-29"], [[2020, 1, 2], [2019, 52, 7]]],
+            [["2010-01-01", pd.NaT], [[2009, 53, 5], [np.nan, np.nan, np.nan]]],
+            # see GH#36032
+            [["2016-01-08", "2016-01-04"], [[2016, 1, 5], [2016, 1, 1]]],
+            [["2016-01-07", "2016-01-01"], [[2016, 1, 4], [2015, 53, 5]]],
+        ],
+    )
+    def test_isocalendar(self, input_series, expected_output):
+        result = pd.to_datetime(Series(input_series)).dt.isocalendar()
+        expected_frame = DataFrame(
+            expected_output, columns=["year", "week", "day"], dtype="UInt32"
+        )
+        tm.assert_frame_equal(result, expected_frame)
+
+    def test_hour_index(self):
+        dt_series = Series(
+            date_range(start="2021-01-01", periods=5, freq="h"),
+            index=[2, 6, 7, 8, 11],
+            dtype="category",
+        )
+        result = dt_series.dt.hour
+        expected = Series(
+            [0, 1, 2, 3, 4],
+            dtype="int32",
+            index=[2, 6, 7, 8, 11],
+        )
+        tm.assert_series_equal(result, expected)
+
+
+class TestSeriesPeriodValuesDtAccessor:
+    @pytest.mark.parametrize(
+        "input_vals",
+        [
+            [Period("2016-01", freq="M"), Period("2016-02", freq="M")],
+            [Period("2016-01-01", freq="D"), Period("2016-01-02", freq="D")],
+            [
+                Period("2016-01-01 00:00:00", freq="h"),
+                Period("2016-01-01 01:00:00", freq="h"),
+            ],
+            [
+                Period("2016-01-01 00:00:00", freq="M"),
+                Period("2016-01-01 00:01:00", freq="M"),
+            ],
+            [
+                Period("2016-01-01 00:00:00", freq="s"),
+                Period("2016-01-01 00:00:01", freq="s"),
+            ],
+        ],
+    )
+    def test_end_time_timevalues(self, input_vals):
+        # GH#17157
+        # Check that the time part of the Period is adjusted by end_time
+        # when using the dt accessor on a Series
+        input_vals = PeriodArray._from_sequence(np.asarray(input_vals))
+
+        ser = Series(input_vals)
+        result = ser.dt.end_time
+        expected = ser.apply(lambda x: x.end_time)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("input_vals", [("2001"), ("NaT")])
+    def test_to_period(self, input_vals):
+        # GH#21205
+        expected = Series([input_vals], dtype="Period[D]")
+        result = Series([input_vals], dtype="datetime64[ns]").dt.to_period("D")
+        tm.assert_series_equal(result, expected)
+
+
+def test_normalize_pre_epoch_dates():
+    # GH: 36294
+    ser = pd.to_datetime(Series(["1969-01-01 09:00:00", "2016-01-01 09:00:00"]))
+    result = ser.dt.normalize()
+    expected = pd.to_datetime(Series(["1969-01-01", "2016-01-01"]))
+    tm.assert_series_equal(result, expected)
+
+
+def test_day_attribute_non_nano_beyond_int32():
+    # GH 52386
+    data = np.array(
+        [
+            136457654736252,
+            134736784364431,
+            245345345545332,
+            223432411,
+            2343241,
+            3634548734,
+            23234,
+        ],
+        dtype="timedelta64[s]",
+    )
+    ser = Series(data)
+    result = ser.dt.days
+    expected = Series([1579371003, 1559453522, 2839645203, 2586, 27, 42066, 0])
+    tm.assert_series_equal(result, expected)

vllm/lib/python3.10/site-packages/pandas/tests/series/accessors/test_list_accessor.py

@@ -0,0 +1,129 @@
+import re
+
+import pytest
+
+from pandas import (
+    ArrowDtype,
+    Series,
+)
+import pandas._testing as tm
+
+pa = pytest.importorskip("pyarrow")
+
+from pandas.compat import pa_version_under11p0
+
+
+@pytest.mark.parametrize(
+    "list_dtype",
+    (
+        pa.list_(pa.int64()),
+        pa.list_(pa.int64(), list_size=3),
+        pa.large_list(pa.int64()),
+    ),
+)
+def test_list_getitem(list_dtype):
+    ser = Series(
+        [[1, 2, 3], [4, None, 5], None],
+        dtype=ArrowDtype(list_dtype),
+    )
+    actual = ser.list[1]
+    expected = Series([2, None, None], dtype="int64[pyarrow]")
+    tm.assert_series_equal(actual, expected)
+
+
+def test_list_getitem_slice():
+    ser = Series(
+        [[1, 2, 3], [4, None, 5], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    if pa_version_under11p0:
+        with pytest.raises(
+            NotImplementedError, match="List slice not supported by pyarrow "
+        ):
+            ser.list[1:None:None]
+    else:
+        actual = ser.list[1:None:None]
+        expected = Series(
+            [[2, 3], [None, 5], None], dtype=ArrowDtype(pa.list_(pa.int64()))
+        )
+        tm.assert_series_equal(actual, expected)
+
+
+def test_list_len():
+    ser = Series(
+        [[1, 2, 3], [4, None], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    actual = ser.list.len()
+    expected = Series([3, 2, None], dtype=ArrowDtype(pa.int32()))
+    tm.assert_series_equal(actual, expected)
+
+
+def test_list_flatten():
+    ser = Series(
+        [[1, 2, 3], [4, None], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    actual = ser.list.flatten()
+    expected = Series([1, 2, 3, 4, None], dtype=ArrowDtype(pa.int64()))
+    tm.assert_series_equal(actual, expected)
+
+
+def test_list_getitem_slice_invalid():
+    ser = Series(
+        [[1, 2, 3], [4, None, 5], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    if pa_version_under11p0:
+        with pytest.raises(
+            NotImplementedError, match="List slice not supported by pyarrow "
+        ):
+            ser.list[1:None:0]
+    else:
+        with pytest.raises(pa.lib.ArrowInvalid, match=re.escape("`step` must be >= 1")):
+            ser.list[1:None:0]
+
+
+def test_list_accessor_non_list_dtype():
+    ser = Series(
+        [1, 2, 4],
+        dtype=ArrowDtype(pa.int64()),
+    )
+    with pytest.raises(
+        AttributeError,
+        match=re.escape(
+            "Can only use the '.list' accessor with 'list[pyarrow]' dtype, "
+            "not int64[pyarrow]."
+        ),
+    ):
+        ser.list[1:None:0]
+
+
+@pytest.mark.parametrize(
+    "list_dtype",
+    (
+        pa.list_(pa.int64()),
+        pa.list_(pa.int64(), list_size=3),
+        pa.large_list(pa.int64()),
+    ),
+)
+def test_list_getitem_invalid_index(list_dtype):
+    ser = Series(
+        [[1, 2, 3], [4, None, 5], None],
+        dtype=ArrowDtype(list_dtype),
+    )
+    with pytest.raises(pa.lib.ArrowInvalid, match="Index -1 is out of bounds"):
+        ser.list[-1]
+    with pytest.raises(pa.lib.ArrowInvalid, match="Index 5 is out of bounds"):
+        ser.list[5]
+    with pytest.raises(ValueError, match="key must be an int or slice, got str"):
+        ser.list["abc"]
+
+
+def test_list_accessor_not_iterable():
+    ser = Series(
+        [[1, 2, 3], [4, None], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    with pytest.raises(TypeError, match="'ListAccessor' object is not iterable"):
+        iter(ser.list)

vllm/lib/python3.10/site-packages/pandas/tests/series/accessors/test_sparse_accessor.py

@@ -0,0 +1,9 @@
+from pandas import Series
+
+
+class TestSparseAccessor:
+    def test_sparse_accessor_updates_on_inplace(self):
+        ser = Series([1, 1, 2, 3], dtype="Sparse[int]")
+        return_value = ser.drop([0, 1], inplace=True)
+        assert return_value is None
+        assert ser.sparse.density == 1.0

vllm/lib/python3.10/site-packages/pandas/tests/series/accessors/test_str_accessor.py

@@ -0,0 +1,25 @@
+import pytest
+
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestStrAccessor:
+    def test_str_attribute(self):
+        # GH#9068
+        methods = ["strip", "rstrip", "lstrip"]
+        ser = Series([" jack", "jill ", " jesse ", "frank"])
+        for method in methods:
+            expected = Series([getattr(str, method)(x) for x in ser.values])
+            tm.assert_series_equal(getattr(Series.str, method)(ser.str), expected)
+
+        # str accessor only valid with string values
+        ser = Series(range(5))
+        with pytest.raises(AttributeError, match="only use .str accessor"):
+            ser.str.repeat(2)
+
+    def test_str_accessor_updates_on_inplace(self):
+        ser = Series(list("abc"))
+        return_value = ser.drop([0], inplace=True)
+        assert return_value is None
+        assert len(ser.str.lower()) == 2

vllm/lib/python3.10/site-packages/pandas/tests/series/accessors/test_struct_accessor.py

@@ -0,0 +1,196 @@
+import re
+
+import pytest
+
+from pandas.compat.pyarrow import (
+    pa_version_under11p0,
+    pa_version_under13p0,
+)
+
+from pandas import (
+    ArrowDtype,
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+pa = pytest.importorskip("pyarrow")
+pc = pytest.importorskip("pyarrow.compute")
+
+
+def test_struct_accessor_dtypes():
+    ser = Series(
+        [],
+        dtype=ArrowDtype(
+            pa.struct(
+                [
+                    ("int_col", pa.int64()),
+                    ("string_col", pa.string()),
+                    (
+                        "struct_col",
+                        pa.struct(
+                            [
+                                ("int_col", pa.int64()),
+                                ("float_col", pa.float64()),
+                            ]
+                        ),
+                    ),
+                ]
+            )
+        ),
+    )
+    actual = ser.struct.dtypes
+    expected = Series(
+        [
+            ArrowDtype(pa.int64()),
+            ArrowDtype(pa.string()),
+            ArrowDtype(
+                pa.struct(
+                    [
+                        ("int_col", pa.int64()),
+                        ("float_col", pa.float64()),
+                    ]
+                )
+            ),
+        ],
+        index=Index(["int_col", "string_col", "struct_col"]),
+    )
+    tm.assert_series_equal(actual, expected)
+
+
+@pytest.mark.skipif(pa_version_under13p0, reason="pyarrow>=13.0.0 required")
+def test_struct_accessor_field():
+    index = Index([-100, 42, 123])
+    ser = Series(
+        [
+            {"rice": 1.0, "maize": -1, "wheat": "a"},
+            {"rice": 2.0, "maize": 0, "wheat": "b"},
+            {"rice": 3.0, "maize": 1, "wheat": "c"},
+        ],
+        dtype=ArrowDtype(
+            pa.struct(
+                [
+                    ("rice", pa.float64()),
+                    ("maize", pa.int64()),
+                    ("wheat", pa.string()),
+                ]
+            )
+        ),
+        index=index,
+    )
+    by_name = ser.struct.field("maize")
+    by_name_expected = Series(
+        [-1, 0, 1],
+        dtype=ArrowDtype(pa.int64()),
+        index=index,
+        name="maize",
+    )
+    tm.assert_series_equal(by_name, by_name_expected)
+
+    by_index = ser.struct.field(2)
+    by_index_expected = Series(
+        ["a", "b", "c"],
+        dtype=ArrowDtype(pa.string()),
+        index=index,
+        name="wheat",
+    )
+    tm.assert_series_equal(by_index, by_index_expected)
+
+
+def test_struct_accessor_field_with_invalid_name_or_index():
+    ser = Series([], dtype=ArrowDtype(pa.struct([("field", pa.int64())])))
+
+    with pytest.raises(ValueError, match="name_or_index must be an int, str,"):
+        ser.struct.field(1.1)
+
+
+@pytest.mark.skipif(pa_version_under11p0, reason="pyarrow>=11.0.0 required")
+def test_struct_accessor_explode():
+    index = Index([-100, 42, 123])
+    ser = Series(
+        [
+            {"painted": 1, "snapping": {"sea": "green"}},
+            {"painted": 2, "snapping": {"sea": "leatherback"}},
+            {"painted": 3, "snapping": {"sea": "hawksbill"}},
+        ],
+        dtype=ArrowDtype(
+            pa.struct(
+                [
+                    ("painted", pa.int64()),
+                    ("snapping", pa.struct([("sea", pa.string())])),
+                ]
+            )
+        ),
+        index=index,
+    )
+    actual = ser.struct.explode()
+    expected = DataFrame(
+        {
+            "painted": Series([1, 2, 3], index=index, dtype=ArrowDtype(pa.int64())),
+            "snapping": Series(
+                [{"sea": "green"}, {"sea": "leatherback"}, {"sea": "hawksbill"}],
+                index=index,
+                dtype=ArrowDtype(pa.struct([("sea", pa.string())])),
+            ),
+        },
+    )
+    tm.assert_frame_equal(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "invalid",
+    [
+        pytest.param(Series([1, 2, 3], dtype="int64"), id="int64"),
+        pytest.param(
+            Series(["a", "b", "c"], dtype="string[pyarrow]"), id="string-pyarrow"
+        ),
+    ],
+)
+def test_struct_accessor_api_for_invalid(invalid):
+    with pytest.raises(
+        AttributeError,
+        match=re.escape(
+            "Can only use the '.struct' accessor with 'struct[pyarrow]' dtype, "
+            f"not {invalid.dtype}."
+        ),
+    ):
+        invalid.struct
+
+
+@pytest.mark.parametrize(
+    ["indices", "name"],
+    [
+        (0, "int_col"),
+        ([1, 2], "str_col"),
+        (pc.field("int_col"), "int_col"),
+        ("int_col", "int_col"),
+        (b"string_col", b"string_col"),
+        ([b"string_col"], "string_col"),
+    ],
+)
+@pytest.mark.skipif(pa_version_under13p0, reason="pyarrow>=13.0.0 required")
+def test_struct_accessor_field_expanded(indices, name):
+    arrow_type = pa.struct(
+        [
+            ("int_col", pa.int64()),
+            (
+                "struct_col",
+                pa.struct(
+                    [
+                        ("int_col", pa.int64()),
+                        ("float_col", pa.float64()),
+                        ("str_col", pa.string()),
+                    ]
+                ),
+            ),
+            (b"string_col", pa.string()),
+        ]
+    )
+
+    data = pa.array([], type=arrow_type)
+    ser = Series(data, dtype=ArrowDtype(arrow_type))
+    expected = pc.struct_field(data, indices)
+    result = ser.struct.field(indices)
+    tm.assert_equal(result.array._pa_array.combine_chunks(), expected)
+    assert result.name == name

vllm/lib/python3.10/site-packages/pandas/tests/series/indexing/test_datetime.py

@@ -0,0 +1,499 @@
+"""
+Also test support for datetime64[ns] in Series / DataFrame
+"""
+from datetime import (
+    datetime,
+    timedelta,
+)
+import re
+
+from dateutil.tz import (
+    gettz,
+    tzutc,
+)
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs import index as libindex
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+def test_fancy_getitem():
+    dti = date_range(
+        freq="WOM-1FRI", start=datetime(2005, 1, 1), end=datetime(2010, 1, 1)
+    )
+
+    s = Series(np.arange(len(dti)), index=dti)
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert s[48] == 48
+    assert s["1/2/2009"] == 48
+    assert s["2009-1-2"] == 48
+    assert s[datetime(2009, 1, 2)] == 48
+    assert s[Timestamp(datetime(2009, 1, 2))] == 48
+    with pytest.raises(KeyError, match=r"^'2009-1-3'$"):
+        s["2009-1-3"]
+    tm.assert_series_equal(
+        s["3/6/2009":"2009-06-05"], s[datetime(2009, 3, 6) : datetime(2009, 6, 5)]
+    )
+
+
+def test_fancy_setitem():
+    dti = date_range(
+        freq="WOM-1FRI", start=datetime(2005, 1, 1), end=datetime(2010, 1, 1)
+    )
+
+    s = Series(np.arange(len(dti)), index=dti)
+
+    msg = "Series.__setitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        s[48] = -1
+    assert s.iloc[48] == -1
+    s["1/2/2009"] = -2
+    assert s.iloc[48] == -2
+    s["1/2/2009":"2009-06-05"] = -3
+    assert (s[48:54] == -3).all()
+
+
+@pytest.mark.parametrize("tz_source", ["pytz", "dateutil"])
+def test_getitem_setitem_datetime_tz(tz_source):
+    if tz_source == "pytz":
+        tzget = pytz.timezone
+    else:
+        # handle special case for utc in dateutil
+        tzget = lambda x: tzutc() if x == "UTC" else gettz(x)
+
+    N = 50
+    # testing with timezone, GH #2785
+    rng = date_range("1/1/1990", periods=N, freq="h", tz=tzget("US/Eastern"))
+    ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+
+    # also test Timestamp tz handling, GH #2789
+    result = ts.copy()
+    result["1990-01-01 09:00:00+00:00"] = 0
+    result["1990-01-01 09:00:00+00:00"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts.copy()
+    result["1990-01-01 03:00:00-06:00"] = 0
+    result["1990-01-01 03:00:00-06:00"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    # repeat with datetimes
+    result = ts.copy()
+    result[datetime(1990, 1, 1, 9, tzinfo=tzget("UTC"))] = 0
+    result[datetime(1990, 1, 1, 9, tzinfo=tzget("UTC"))] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts.copy()
+    dt = Timestamp(1990, 1, 1, 3).tz_localize(tzget("US/Central"))
+    dt = dt.to_pydatetime()
+    result[dt] = 0
+    result[dt] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+
+def test_getitem_setitem_datetimeindex():
+    N = 50
+    # testing with timezone, GH #2785
+    rng = date_range("1/1/1990", periods=N, freq="h", tz="US/Eastern")
+    ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+
+    result = ts["1990-01-01 04:00:00"]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts.copy()
+    result["1990-01-01 04:00:00"] = 0
+    result["1990-01-01 04:00:00"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts["1990-01-01 04:00:00":"1990-01-01 07:00:00"]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = 0
+    result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = ts[4:8]
+    tm.assert_series_equal(result, ts)
+
+    lb = "1990-01-01 04:00:00"
+    rb = "1990-01-01 07:00:00"
+    # GH#18435 strings get a pass from tzawareness compat
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    lb = "1990-01-01 04:00:00-0500"
+    rb = "1990-01-01 07:00:00-0500"
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    # But we do not give datetimes a pass on tzawareness compat
+    msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
+    naive = datetime(1990, 1, 1, 4)
+    for key in [naive, Timestamp(naive), np.datetime64(naive, "ns")]:
+        with pytest.raises(KeyError, match=re.escape(repr(key))):
+            # GH#36148 as of 2.0 we require tzawareness-compat
+            ts[key]
+
+    result = ts.copy()
+    # GH#36148 as of 2.0 we do not ignore tzawareness mismatch in indexing,
+    #  so setting it as a new key casts to object rather than matching
+    #  rng[4]
+    result[naive] = ts.iloc[4]
+    assert result.index.dtype == object
+    tm.assert_index_equal(result.index[:-1], rng.astype(object))
+    assert result.index[-1] == naive
+
+    msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
+    with pytest.raises(TypeError, match=msg):
+        # GH#36148 require tzawareness compat as of 2.0
+        ts[naive : datetime(1990, 1, 1, 7)]
+
+    result = ts.copy()
+    with pytest.raises(TypeError, match=msg):
+        # GH#36148 require tzawareness compat as of 2.0
+        result[naive : datetime(1990, 1, 1, 7)] = 0
+    with pytest.raises(TypeError, match=msg):
+        # GH#36148 require tzawareness compat as of 2.0
+        result[naive : datetime(1990, 1, 1, 7)] = 99
+    # the __setitems__ here failed, so result should still match ts
+    tm.assert_series_equal(result, ts)
+
+    lb = naive
+    rb = datetime(1990, 1, 1, 7)
+    msg = r"Invalid comparison between dtype=datetime64\[ns, US/Eastern\] and datetime"
+    with pytest.raises(TypeError, match=msg):
+        # tznaive vs tzaware comparison is invalid
+        # see GH#18376, GH#18162
+        ts[(ts.index >= lb) & (ts.index <= rb)]
+
+    lb = Timestamp(naive).tz_localize(rng.tzinfo)
+    rb = Timestamp(datetime(1990, 1, 1, 7)).tz_localize(rng.tzinfo)
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts[ts.index[4]]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts[ts.index[4:8]]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result[ts.index[4:8]] = 0
+    result.iloc[4:8] = ts.iloc[4:8]
+    tm.assert_series_equal(result, ts)
+
+    # also test partial date slicing
+    result = ts["1990-01-02"]
+    expected = ts[24:48]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result["1990-01-02"] = 0
+    result["1990-01-02"] = ts[24:48]
+    tm.assert_series_equal(result, ts)
+
+
+def test_getitem_setitem_periodindex():
+    N = 50
+    rng = period_range("1/1/1990", periods=N, freq="h")
+    ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+
+    result = ts["1990-01-01 04"]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts.copy()
+    result["1990-01-01 04"] = 0
+    result["1990-01-01 04"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts["1990-01-01 04":"1990-01-01 07"]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result["1990-01-01 04":"1990-01-01 07"] = 0
+    result["1990-01-01 04":"1990-01-01 07"] = ts[4:8]
+    tm.assert_series_equal(result, ts)
+
+    lb = "1990-01-01 04"
+    rb = "1990-01-01 07"
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    # GH 2782
+    result = ts[ts.index[4]]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts[ts.index[4:8]]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result[ts.index[4:8]] = 0
+    result.iloc[4:8] = ts.iloc[4:8]
+    tm.assert_series_equal(result, ts)
+
+
+def test_datetime_indexing():
+    index = date_range("1/1/2000", "1/7/2000")
+    index = index.repeat(3)
+
+    s = Series(len(index), index=index)
+    stamp = Timestamp("1/8/2000")
+
+    with pytest.raises(KeyError, match=re.escape(repr(stamp))):
+        s[stamp]
+    s[stamp] = 0
+    assert s[stamp] == 0
+
+    # not monotonic
+    s = Series(len(index), index=index)
+    s = s[::-1]
+
+    with pytest.raises(KeyError, match=re.escape(repr(stamp))):
+        s[stamp]
+    s[stamp] = 0
+    assert s[stamp] == 0
+
+
+# test duplicates in time series
+
+
+def test_indexing_with_duplicate_datetimeindex(
+    rand_series_with_duplicate_datetimeindex,
+):
+    ts = rand_series_with_duplicate_datetimeindex
+
+    uniques = ts.index.unique()
+    for date in uniques:
+        result = ts[date]
+
+        mask = ts.index == date
+        total = (ts.index == date).sum()
+        expected = ts[mask]
+        if total > 1:
+            tm.assert_series_equal(result, expected)
+        else:
+            tm.assert_almost_equal(result, expected.iloc[0])
+
+        cp = ts.copy()
+        cp[date] = 0
+        expected = Series(np.where(mask, 0, ts), index=ts.index)
+        tm.assert_series_equal(cp, expected)
+
+    key = datetime(2000, 1, 6)
+    with pytest.raises(KeyError, match=re.escape(repr(key))):
+        ts[key]
+
+    # new index
+    ts[datetime(2000, 1, 6)] = 0
+    assert ts[datetime(2000, 1, 6)] == 0
+
+
+def test_loc_getitem_over_size_cutoff(monkeypatch):
+    # #1821
+
+    monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 1000)
+
+    # create large list of non periodic datetime
+    dates = []
+    sec = timedelta(seconds=1)
+    half_sec = timedelta(microseconds=500000)
+    d = datetime(2011, 12, 5, 20, 30)
+    n = 1100
+    for i in range(n):
+        dates.append(d)
+        dates.append(d + sec)
+        dates.append(d + sec + half_sec)
+        dates.append(d + sec + sec + half_sec)
+        d += 3 * sec
+
+    # duplicate some values in the list
+    duplicate_positions = np.random.default_rng(2).integers(0, len(dates) - 1, 20)
+    for p in duplicate_positions:
+        dates[p + 1] = dates[p]
+
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((len(dates), 4)),
+        index=dates,
+        columns=list("ABCD"),
+    )
+
+    pos = n * 3
+    timestamp = df.index[pos]
+    assert timestamp in df.index
+
+    # it works!
+    df.loc[timestamp]
+    assert len(df.loc[[timestamp]]) > 0
+
+
+def test_indexing_over_size_cutoff_period_index(monkeypatch):
+    # GH 27136
+
+    monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 1000)
+
+    n = 1100
+    idx = period_range("1/1/2000", freq="min", periods=n)
+    assert idx._engine.over_size_threshold
+
+    s = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx)
+
+    pos = n - 1
+    timestamp = idx[pos]
+    assert timestamp in s.index
+
+    # it works!
+    s[timestamp]
+    assert len(s.loc[[timestamp]]) > 0
+
+
+def test_indexing_unordered():
+    # GH 2437
+    rng = date_range(start="2011-01-01", end="2011-01-15")
+    ts = Series(np.random.default_rng(2).random(len(rng)), index=rng)
+    ts2 = pd.concat([ts[0:4], ts[-4:], ts[4:-4]])
+
+    for t in ts.index:
+        expected = ts[t]
+        result = ts2[t]
+        assert expected == result
+
+    # GH 3448 (ranges)
+    def compare(slobj):
+        result = ts2[slobj].copy()
+        result = result.sort_index()
+        expected = ts[slobj]
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+
+    for key in [
+        slice("2011-01-01", "2011-01-15"),
+        slice("2010-12-30", "2011-01-15"),
+        slice("2011-01-01", "2011-01-16"),
+        # partial ranges
+        slice("2011-01-01", "2011-01-6"),
+        slice("2011-01-06", "2011-01-8"),
+        slice("2011-01-06", "2011-01-12"),
+    ]:
+        with pytest.raises(
+            KeyError, match="Value based partial slicing on non-monotonic"
+        ):
+            compare(key)
+
+    # single values
+    result = ts2["2011"].sort_index()
+    expected = ts["2011"]
+    expected.index = expected.index._with_freq(None)
+    tm.assert_series_equal(result, expected)
+
+
+def test_indexing_unordered2():
+    # diff freq
+    rng = date_range(datetime(2005, 1, 1), periods=20, freq="ME")
+    ts = Series(np.arange(len(rng)), index=rng)
+    ts = ts.take(np.random.default_rng(2).permutation(20))
+
+    result = ts["2005"]
+    for t in result.index:
+        assert t.year == 2005
+
+
+def test_indexing():
+    idx = date_range("2001-1-1", periods=20, freq="ME")
+    ts = Series(np.random.default_rng(2).random(len(idx)), index=idx)
+
+    # getting
+
+    # GH 3070, make sure semantics work on Series/Frame
+    result = ts["2001"]
+    tm.assert_series_equal(result, ts.iloc[:12])
+
+    df = DataFrame({"A": ts.copy()})
+
+    # GH#36179 pre-2.0 df["2001"] operated as slicing on rows. in 2.0 it behaves
+    #  like any other key, so raises
+    with pytest.raises(KeyError, match="2001"):
+        df["2001"]
+
+    # setting
+    ts = Series(np.random.default_rng(2).random(len(idx)), index=idx)
+    expected = ts.copy()
+    expected.iloc[:12] = 1
+    ts["2001"] = 1
+    tm.assert_series_equal(ts, expected)
+
+    expected = df.copy()
+    expected.iloc[:12, 0] = 1
+    df.loc["2001", "A"] = 1
+    tm.assert_frame_equal(df, expected)
+
+
+def test_getitem_str_month_with_datetimeindex():
+    # GH3546 (not including times on the last day)
+    idx = date_range(start="2013-05-31 00:00", end="2013-05-31 23:00", freq="h")
+    ts = Series(range(len(idx)), index=idx)
+    expected = ts["2013-05"]
+    tm.assert_series_equal(expected, ts)
+
+    idx = date_range(start="2013-05-31 00:00", end="2013-05-31 23:59", freq="s")
+    ts = Series(range(len(idx)), index=idx)
+    expected = ts["2013-05"]
+    tm.assert_series_equal(expected, ts)
+
+
+def test_getitem_str_year_with_datetimeindex():
+    idx = [
+        Timestamp("2013-05-31 00:00"),
+        Timestamp(datetime(2013, 5, 31, 23, 59, 59, 999999)),
+    ]
+    ts = Series(range(len(idx)), index=idx)
+    expected = ts["2013"]
+    tm.assert_series_equal(expected, ts)
+
+
+def test_getitem_str_second_with_datetimeindex():
+    # GH14826, indexing with a seconds resolution string / datetime object
+    df = DataFrame(
+        np.random.default_rng(2).random((5, 5)),
+        columns=["open", "high", "low", "close", "volume"],
+        index=date_range("2012-01-02 18:01:00", periods=5, tz="US/Central", freq="s"),
+    )
+
+    # this is a single date, so will raise
+    with pytest.raises(KeyError, match=r"^'2012-01-02 18:01:02'$"):
+        df["2012-01-02 18:01:02"]
+
+    msg = r"Timestamp\('2012-01-02 18:01:02-0600', tz='US/Central'\)"
+    with pytest.raises(KeyError, match=msg):
+        df[df.index[2]]
+
+
+def test_compare_datetime_with_all_none():
+    # GH#54870
+    ser = Series(["2020-01-01", "2020-01-02"], dtype="datetime64[ns]")
+    ser2 = Series([None, None])
+    result = ser > ser2
+    expected = Series([False, False])
+    tm.assert_series_equal(result, expected)

vllm/lib/python3.10/site-packages/pandas/tests/series/indexing/test_delitem.py

@@ -0,0 +1,70 @@
+import pytest
+
+from pandas import (
+    Index,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesDelItem:
+    def test_delitem(self):
+        # GH#5542
+        # should delete the item inplace
+        s = Series(range(5))
+        del s[0]
+
+        expected = Series(range(1, 5), index=range(1, 5))
+        tm.assert_series_equal(s, expected)
+
+        del s[1]
+        expected = Series(range(2, 5), index=range(2, 5))
+        tm.assert_series_equal(s, expected)
+
+        # only 1 left, del, add, del
+        s = Series(1)
+        del s[0]
+        tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype="int64")))
+        s[0] = 1
+        tm.assert_series_equal(s, Series(1))
+        del s[0]
+        tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype="int64")))
+
+    def test_delitem_object_index(self, using_infer_string):
+        # Index(dtype=object)
+        dtype = "string[pyarrow_numpy]" if using_infer_string else object
+        s = Series(1, index=Index(["a"], dtype=dtype))
+        del s["a"]
+        tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype=dtype)))
+        s["a"] = 1
+        tm.assert_series_equal(s, Series(1, index=Index(["a"], dtype=dtype)))
+        del s["a"]
+        tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype=dtype)))
+
+    def test_delitem_missing_key(self):
+        # empty
+        s = Series(dtype=object)
+
+        with pytest.raises(KeyError, match=r"^0$"):
+            del s[0]
+
+    def test_delitem_extension_dtype(self):
+        # GH#40386
+        # DatetimeTZDtype
+        dti = date_range("2016-01-01", periods=3, tz="US/Pacific")
+        ser = Series(dti)
+
+        expected = ser[[0, 2]]
+        del ser[1]
+        assert ser.dtype == dti.dtype
+        tm.assert_series_equal(ser, expected)
+
+        # PeriodDtype
+        pi = dti.tz_localize(None).to_period("D")
+        ser = Series(pi)
+
+        expected = ser[:2]
+        del ser[2]
+        assert ser.dtype == pi.dtype
+        tm.assert_series_equal(ser, expected)

vllm/lib/python3.10/site-packages/pandas/tests/series/indexing/test_get.py

@@ -0,0 +1,238 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DatetimeIndex,
+    Index,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+def test_get():
+    # GH 6383
+    s = Series(
+        np.array(
+            [
+                43,
+                48,
+                60,
+                48,
+                50,
+                51,
+                50,
+                45,
+                57,
+                48,
+                56,
+                45,
+                51,
+                39,
+                55,
+                43,
+                54,
+                52,
+                51,
+                54,
+            ]
+        )
+    )
+
+    result = s.get(25, 0)
+    expected = 0
+    assert result == expected
+
+    s = Series(
+        np.array(
+            [
+                43,
+                48,
+                60,
+                48,
+                50,
+                51,
+                50,
+                45,
+                57,
+                48,
+                56,
+                45,
+                51,
+                39,
+                55,
+                43,
+                54,
+                52,
+                51,
+                54,
+            ]
+        ),
+        index=Index(
+            [
+                25.0,
+                36.0,
+                49.0,
+                64.0,
+                81.0,
+                100.0,
+                121.0,
+                144.0,
+                169.0,
+                196.0,
+                1225.0,
+                1296.0,
+                1369.0,
+                1444.0,
+                1521.0,
+                1600.0,
+                1681.0,
+                1764.0,
+                1849.0,
+                1936.0,
+            ],
+            dtype=np.float64,
+        ),
+    )
+
+    result = s.get(25, 0)
+    expected = 43
+    assert result == expected
+
+    # GH 7407
+    # with a boolean accessor
+    df = pd.DataFrame({"i": [0] * 3, "b": [False] * 3})
+    vc = df.i.value_counts()
+    result = vc.get(99, default="Missing")
+    assert result == "Missing"
+
+    vc = df.b.value_counts()
+    result = vc.get(False, default="Missing")
+    assert result == 3
+
+    result = vc.get(True, default="Missing")
+    assert result == "Missing"
+
+
+def test_get_nan(float_numpy_dtype):
+    # GH 8569
+    s = Index(range(10), dtype=float_numpy_dtype).to_series()
+    assert s.get(np.nan) is None
+    assert s.get(np.nan, default="Missing") == "Missing"
+
+
+def test_get_nan_multiple(float_numpy_dtype):
+    # GH 8569
+    # ensure that fixing "test_get_nan" above hasn't broken get
+    # with multiple elements
+    s = Index(range(10), dtype=float_numpy_dtype).to_series()
+
+    idx = [2, 30]
+    assert s.get(idx) is None
+
+    idx = [2, np.nan]
+    assert s.get(idx) is None
+
+    # GH 17295 - all missing keys
+    idx = [20, 30]
+    assert s.get(idx) is None
+
+    idx = [np.nan, np.nan]
+    assert s.get(idx) is None
+
+
+def test_get_with_default():
+    # GH#7725
+    d0 = ["a", "b", "c", "d"]
+    d1 = np.arange(4, dtype="int64")
+
+    for data, index in ((d0, d1), (d1, d0)):
+        s = Series(data, index=index)
+        for i, d in zip(index, data):
+            assert s.get(i) == d
+            assert s.get(i, d) == d
+            assert s.get(i, "z") == d
+
+            assert s.get("e", "z") == "z"
+            assert s.get("e", "e") == "e"
+
+            msg = "Series.__getitem__ treating keys as positions is deprecated"
+            warn = None
+            if index is d0:
+                warn = FutureWarning
+            with tm.assert_produces_warning(warn, match=msg):
+                assert s.get(10, "z") == "z"
+                assert s.get(10, 10) == 10
+
+
+@pytest.mark.parametrize(
+    "arr",
+    [
+        np.random.default_rng(2).standard_normal(10),
+        DatetimeIndex(date_range("2020-01-01", periods=10), name="a").tz_localize(
+            tz="US/Eastern"
+        ),
+    ],
+)
+def test_get_with_ea(arr):
+    # GH#21260
+    ser = Series(arr, index=[2 * i for i in range(len(arr))])
+    assert ser.get(4) == ser.iloc[2]
+
+    result = ser.get([4, 6])
+    expected = ser.iloc[[2, 3]]
+    tm.assert_series_equal(result, expected)
+
+    result = ser.get(slice(2))
+    expected = ser.iloc[[0, 1]]
+    tm.assert_series_equal(result, expected)
+
+    assert ser.get(-1) is None
+    assert ser.get(ser.index.max() + 1) is None
+
+    ser = Series(arr[:6], index=list("abcdef"))
+    assert ser.get("c") == ser.iloc[2]
+
+    result = ser.get(slice("b", "d"))
+    expected = ser.iloc[[1, 2, 3]]
+    tm.assert_series_equal(result, expected)
+
+    result = ser.get("Z")
+    assert result is None
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert ser.get(4) == ser.iloc[4]
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert ser.get(-1) == ser.iloc[-1]
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert ser.get(len(ser)) is None
+
+    # GH#21257
+    ser = Series(arr)
+    ser2 = ser[::2]
+    assert ser2.get(1) is None
+
+
+def test_getitem_get(string_series, object_series):
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+
+    for obj in [string_series, object_series]:
+        idx = obj.index[5]
+
+        assert obj[idx] == obj.get(idx)
+        assert obj[idx] == obj.iloc[5]
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert string_series.get(-1) == string_series.get(string_series.index[-1])
+    assert string_series.iloc[5] == string_series.get(string_series.index[5])
+
+
+def test_get_none():
+    # GH#5652
+    s1 = Series(dtype=object)
+    s2 = Series(dtype=object, index=list("abc"))
+    for s in [s1, s2]:
+        result = s.get(None)
+        assert result is None

vllm/lib/python3.10/site-packages/pandas/tests/series/indexing/test_getitem.py

@@ -0,0 +1,735 @@
+"""
+Series.__getitem__ test classes are organized by the type of key passed.
+"""
+from datetime import (
+    date,
+    datetime,
+    time,
+)
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import (
+    conversion,
+    timezones,
+)
+
+from pandas.core.dtypes.common import is_scalar
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.indexing import IndexingError
+
+from pandas.tseries.offsets import BDay
+
+
+class TestSeriesGetitemScalars:
+    def test_getitem_object_index_float_string(self):
+        # GH#17286
+        ser = Series([1] * 4, index=Index(["a", "b", "c", 1.0]))
+        assert ser["a"] == 1
+        assert ser[1.0] == 1
+
+    def test_getitem_float_keys_tuple_values(self):
+        # see GH#13509
+
+        # unique Index
+        ser = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.1, 0.2], name="foo")
+        result = ser[0.0]
+        assert result == (1, 1)
+
+        # non-unique Index
+        expected = Series([(1, 1), (2, 2)], index=[0.0, 0.0], name="foo")
+        ser = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.0, 0.2], name="foo")
+
+        result = ser[0.0]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_unrecognized_scalar(self):
+        # GH#32684 a scalar key that is not recognized by lib.is_scalar
+
+        # a series that might be produced via `frame.dtypes`
+        ser = Series([1, 2], index=[np.dtype("O"), np.dtype("i8")])
+
+        key = ser.index[1]
+
+        result = ser[key]
+        assert result == 2
+
+    def test_getitem_negative_out_of_bounds(self):
+        ser = Series(["a"] * 10, index=["a"] * 10)
+
+        msg = "index -11 is out of bounds for axis 0 with size 10|index out of bounds"
+        warn_msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with pytest.raises(IndexError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                ser[-11]
+
+    def test_getitem_out_of_bounds_indexerror(self, datetime_series):
+        # don't segfault, GH#495
+        msg = r"index \d+ is out of bounds for axis 0 with size \d+"
+        warn_msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with pytest.raises(IndexError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                datetime_series[len(datetime_series)]
+
+    def test_getitem_out_of_bounds_empty_rangeindex_keyerror(self):
+        # GH#917
+        # With a RangeIndex, an int key gives a KeyError
+        ser = Series([], dtype=object)
+        with pytest.raises(KeyError, match="-1"):
+            ser[-1]
+
+    def test_getitem_keyerror_with_integer_index(self, any_int_numpy_dtype):
+        dtype = any_int_numpy_dtype
+        ser = Series(
+            np.random.default_rng(2).standard_normal(6),
+            index=Index([0, 0, 1, 1, 2, 2], dtype=dtype),
+        )
+
+        with pytest.raises(KeyError, match=r"^5$"):
+            ser[5]
+
+        with pytest.raises(KeyError, match=r"^'c'$"):
+            ser["c"]
+
+        # not monotonic
+        ser = Series(
+            np.random.default_rng(2).standard_normal(6), index=[2, 2, 0, 0, 1, 1]
+        )
+
+        with pytest.raises(KeyError, match=r"^5$"):
+            ser[5]
+
+        with pytest.raises(KeyError, match=r"^'c'$"):
+            ser["c"]
+
+    def test_getitem_int64(self, datetime_series):
+        idx = np.int64(5)
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = datetime_series[idx]
+        assert res == datetime_series.iloc[5]
+
+    def test_getitem_full_range(self):
+        # github.com/pandas-dev/pandas/commit/4f433773141d2eb384325714a2776bcc5b2e20f7
+        ser = Series(range(5), index=list(range(5)))
+        result = ser[list(range(5))]
+        tm.assert_series_equal(result, ser)
+
+    # ------------------------------------------------------------------
+    # Series with DatetimeIndex
+
+    @pytest.mark.parametrize("tzstr", ["Europe/Berlin", "dateutil/Europe/Berlin"])
+    def test_getitem_pydatetime_tz(self, tzstr):
+        tz = timezones.maybe_get_tz(tzstr)
+
+        index = date_range(
+            start="2012-12-24 16:00", end="2012-12-24 18:00", freq="h", tz=tzstr
+        )
+        ts = Series(index=index, data=index.hour)
+        time_pandas = Timestamp("2012-12-24 17:00", tz=tzstr)
+
+        dt = datetime(2012, 12, 24, 17, 0)
+        time_datetime = conversion.localize_pydatetime(dt, tz)
+        assert ts[time_pandas] == ts[time_datetime]
+
+    @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_string_index_alias_tz_aware(self, tz):
+        rng = date_range("1/1/2000", periods=10, tz=tz)
+        ser = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        result = ser["1/3/2000"]
+        tm.assert_almost_equal(result, ser.iloc[2])
+
+    def test_getitem_time_object(self):
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        mask = (rng.hour == 9) & (rng.minute == 30)
+        result = ts[time(9, 30)]
+        expected = ts[mask]
+        result.index = result.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+
+    # ------------------------------------------------------------------
+    # Series with CategoricalIndex
+
+    def test_getitem_scalar_categorical_index(self):
+        cats = Categorical([Timestamp("12-31-1999"), Timestamp("12-31-2000")])
+
+        ser = Series([1, 2], index=cats)
+
+        expected = ser.iloc[0]
+        result = ser[cats[0]]
+        assert result == expected
+
+    def test_getitem_numeric_categorical_listlike_matches_scalar(self):
+        # GH#15470
+        ser = Series(["a", "b", "c"], index=pd.CategoricalIndex([2, 1, 0]))
+
+        # 0 is treated as a label
+        assert ser[0] == "c"
+
+        # the listlike analogue should also be treated as labels
+        res = ser[[0]]
+        expected = ser.iloc[-1:]
+        tm.assert_series_equal(res, expected)
+
+        res2 = ser[[0, 1, 2]]
+        tm.assert_series_equal(res2, ser.iloc[::-1])
+
+    def test_getitem_integer_categorical_not_positional(self):
+        # GH#14865
+        ser = Series(["a", "b", "c"], index=Index([1, 2, 3], dtype="category"))
+        assert ser.get(3) == "c"
+        assert ser[3] == "c"
+
+    def test_getitem_str_with_timedeltaindex(self):
+        rng = timedelta_range("1 day 10:11:12", freq="h", periods=500)
+        ser = Series(np.arange(len(rng)), index=rng)
+
+        key = "6 days, 23:11:12"
+        indexer = rng.get_loc(key)
+        assert indexer == 133
+
+        result = ser[key]
+        assert result == ser.iloc[133]
+
+        msg = r"^Timedelta\('50 days 00:00:00'\)$"
+        with pytest.raises(KeyError, match=msg):
+            rng.get_loc("50 days")
+        with pytest.raises(KeyError, match=msg):
+            ser["50 days"]
+
+    def test_getitem_bool_index_positional(self):
+        # GH#48653
+        ser = Series({True: 1, False: 0})
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser[0]
+        assert result == 1
+
+
+class TestSeriesGetitemSlices:
+    def test_getitem_partial_str_slice_with_datetimeindex(self):
+        # GH#34860
+        arr = date_range("1/1/2008", "1/1/2009")
+        ser = arr.to_series()
+        result = ser["2008"]
+
+        rng = date_range(start="2008-01-01", end="2008-12-31")
+        expected = Series(rng, index=rng)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_slice_strings_with_datetimeindex(self):
+        idx = DatetimeIndex(
+            ["1/1/2000", "1/2/2000", "1/2/2000", "1/3/2000", "1/4/2000"]
+        )
+
+        ts = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx)
+
+        result = ts["1/2/2000":]
+        expected = ts[1:]
+        tm.assert_series_equal(result, expected)
+
+        result = ts["1/2/2000":"1/3/2000"]
+        expected = ts[1:4]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_partial_str_slice_with_timedeltaindex(self):
+        rng = timedelta_range("1 day 10:11:12", freq="h", periods=500)
+        ser = Series(np.arange(len(rng)), index=rng)
+
+        result = ser["5 day":"6 day"]
+        expected = ser.iloc[86:134]
+        tm.assert_series_equal(result, expected)
+
+        result = ser["5 day":]
+        expected = ser.iloc[86:]
+        tm.assert_series_equal(result, expected)
+
+        result = ser[:"6 day"]
+        expected = ser.iloc[:134]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_partial_str_slice_high_reso_with_timedeltaindex(self):
+        # higher reso
+        rng = timedelta_range("1 day 10:11:12", freq="us", periods=2000)
+        ser = Series(np.arange(len(rng)), index=rng)
+
+        result = ser["1 day 10:11:12":]
+        expected = ser.iloc[0:]
+        tm.assert_series_equal(result, expected)
+
+        result = ser["1 day 10:11:12.001":]
+        expected = ser.iloc[1000:]
+        tm.assert_series_equal(result, expected)
+
+        result = ser["1 days, 10:11:12.001001"]
+        assert result == ser.iloc[1001]
+
+    def test_getitem_slice_2d(self, datetime_series):
+        # GH#30588 multi-dimensional indexing deprecated
+        with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+            datetime_series[:, np.newaxis]
+
+    def test_getitem_median_slice_bug(self):
+        index = date_range("20090415", "20090519", freq="2B")
+        ser = Series(np.random.default_rng(2).standard_normal(13), index=index)
+
+        indexer = [slice(6, 7, None)]
+        msg = "Indexing with a single-item list"
+        with pytest.raises(ValueError, match=msg):
+            # GH#31299
+            ser[indexer]
+        # but we're OK with a single-element tuple
+        result = ser[(indexer[0],)]
+        expected = ser[indexer[0]]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "slc, positions",
+        [
+            [slice(date(2018, 1, 1), None), [0, 1, 2]],
+            [slice(date(2019, 1, 2), None), [2]],
+            [slice(date(2020, 1, 1), None), []],
+            [slice(None, date(2020, 1, 1)), [0, 1, 2]],
+            [slice(None, date(2019, 1, 1)), [0]],
+        ],
+    )
+    def test_getitem_slice_date(self, slc, positions):
+        # https://github.com/pandas-dev/pandas/issues/31501
+        ser = Series(
+            [0, 1, 2],
+            DatetimeIndex(["2019-01-01", "2019-01-01T06:00:00", "2019-01-02"]),
+        )
+        result = ser[slc]
+        expected = ser.take(positions)
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_slice_float_raises(self, datetime_series):
+        msg = (
+            "cannot do slice indexing on DatetimeIndex with these indexers "
+            r"\[{key}\] of type float"
+        )
+        with pytest.raises(TypeError, match=msg.format(key=r"4\.0")):
+            datetime_series[4.0:10.0]
+
+        with pytest.raises(TypeError, match=msg.format(key=r"4\.5")):
+            datetime_series[4.5:10.0]
+
+    def test_getitem_slice_bug(self):
+        ser = Series(range(10), index=list(range(10)))
+        result = ser[-12:]
+        tm.assert_series_equal(result, ser)
+
+        result = ser[-7:]
+        tm.assert_series_equal(result, ser[3:])
+
+        result = ser[:-12]
+        tm.assert_series_equal(result, ser[:0])
+
+    def test_getitem_slice_integers(self):
+        ser = Series(
+            np.random.default_rng(2).standard_normal(8),
+            index=[2, 4, 6, 8, 10, 12, 14, 16],
+        )
+
+        result = ser[:4]
+        expected = Series(ser.values[:4], index=[2, 4, 6, 8])
+        tm.assert_series_equal(result, expected)
+
+
+class TestSeriesGetitemListLike:
+    @pytest.mark.parametrize("box", [list, np.array, Index, Series])
+    def test_getitem_no_matches(self, box):
+        # GH#33462 we expect the same behavior for list/ndarray/Index/Series
+        ser = Series(["A", "B"])
+
+        key = Series(["C"], dtype=object)
+        key = box(key)
+
+        msg = (
+            r"None of \[Index\(\['C'\], dtype='object|string'\)\] are in the \[index\]"
+        )
+        with pytest.raises(KeyError, match=msg):
+            ser[key]
+
+    def test_getitem_intlist_intindex_periodvalues(self):
+        ser = Series(period_range("2000-01-01", periods=10, freq="D"))
+
+        result = ser[[2, 4]]
+        exp = Series(
+            [pd.Period("2000-01-03", freq="D"), pd.Period("2000-01-05", freq="D")],
+            index=[2, 4],
+            dtype="Period[D]",
+        )
+        tm.assert_series_equal(result, exp)
+        assert result.dtype == "Period[D]"
+
+    @pytest.mark.parametrize("box", [list, np.array, Index])
+    def test_getitem_intlist_intervalindex_non_int(self, box):
+        # GH#33404 fall back to positional since ints are unambiguous
+        dti = date_range("2000-01-03", periods=3)._with_freq(None)
+        ii = pd.IntervalIndex.from_breaks(dti)
+        ser = Series(range(len(ii)), index=ii)
+
+        expected = ser.iloc[:1]
+        key = box([0])
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser[key]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [list, np.array, Index])
+    @pytest.mark.parametrize("dtype", [np.int64, np.float64, np.uint64])
+    def test_getitem_intlist_multiindex_numeric_level(self, dtype, box):
+        # GH#33404 do _not_ fall back to positional since ints are ambiguous
+        idx = Index(range(4)).astype(dtype)
+        dti = date_range("2000-01-03", periods=3)
+        mi = pd.MultiIndex.from_product([idx, dti])
+        ser = Series(range(len(mi))[::-1], index=mi)
+
+        key = box([5])
+        with pytest.raises(KeyError, match="5"):
+            ser[key]
+
+    def test_getitem_uint_array_key(self, any_unsigned_int_numpy_dtype):
+        # GH #37218
+        ser = Series([1, 2, 3])
+        key = np.array([4], dtype=any_unsigned_int_numpy_dtype)
+
+        with pytest.raises(KeyError, match="4"):
+            ser[key]
+        with pytest.raises(KeyError, match="4"):
+            ser.loc[key]
+
+
+class TestGetitemBooleanMask:
+    def test_getitem_boolean(self, string_series):
+        ser = string_series
+        mask = ser > ser.median()
+
+        # passing list is OK
+        result = ser[list(mask)]
+        expected = ser[mask]
+        tm.assert_series_equal(result, expected)
+        tm.assert_index_equal(result.index, ser.index[mask])
+
+    def test_getitem_boolean_empty(self):
+        ser = Series([], dtype=np.int64)
+        ser.index.name = "index_name"
+        ser = ser[ser.isna()]
+        assert ser.index.name == "index_name"
+        assert ser.dtype == np.int64
+
+        # GH#5877
+        # indexing with empty series
+        ser = Series(["A", "B"], dtype=object)
+        expected = Series(dtype=object, index=Index([], dtype="int64"))
+        result = ser[Series([], dtype=object)]
+        tm.assert_series_equal(result, expected)
+
+        # invalid because of the boolean indexer
+        # that's empty or not-aligned
+        msg = (
+            r"Unalignable boolean Series provided as indexer \(index of "
+            r"the boolean Series and of the indexed object do not match"
+        )
+        with pytest.raises(IndexingError, match=msg):
+            ser[Series([], dtype=bool)]
+
+        with pytest.raises(IndexingError, match=msg):
+            ser[Series([True], dtype=bool)]
+
+    def test_getitem_boolean_object(self, string_series):
+        # using column from DataFrame
+
+        ser = string_series
+        mask = ser > ser.median()
+        omask = mask.astype(object)
+
+        # getitem
+        result = ser[omask]
+        expected = ser[mask]
+        tm.assert_series_equal(result, expected)
+
+        # setitem
+        s2 = ser.copy()
+        cop = ser.copy()
+        cop[omask] = 5
+        s2[mask] = 5
+        tm.assert_series_equal(cop, s2)
+
+        # nans raise exception
+        omask[5:10] = np.nan
+        msg = "Cannot mask with non-boolean array containing NA / NaN values"
+        with pytest.raises(ValueError, match=msg):
+            ser[omask]
+        with pytest.raises(ValueError, match=msg):
+            ser[omask] = 5
+
+    def test_getitem_boolean_dt64_copies(self):
+        # GH#36210
+        dti = date_range("2016-01-01", periods=4, tz="US/Pacific")
+        key = np.array([True, True, False, False])
+
+        ser = Series(dti._data)
+
+        res = ser[key]
+        assert res._values._ndarray.base is None
+
+        # compare with numeric case for reference
+        ser2 = Series(range(4))
+        res2 = ser2[key]
+        assert res2._values.base is None
+
+    def test_getitem_boolean_corner(self, datetime_series):
+        ts = datetime_series
+        mask_shifted = ts.shift(1, freq=BDay()) > ts.median()
+
+        msg = (
+            r"Unalignable boolean Series provided as indexer \(index of "
+            r"the boolean Series and of the indexed object do not match"
+        )
+        with pytest.raises(IndexingError, match=msg):
+            ts[mask_shifted]
+
+        with pytest.raises(IndexingError, match=msg):
+            ts.loc[mask_shifted]
+
+    def test_getitem_boolean_different_order(self, string_series):
+        ordered = string_series.sort_values()
+
+        sel = string_series[ordered > 0]
+        exp = string_series[string_series > 0]
+        tm.assert_series_equal(sel, exp)
+
+    def test_getitem_boolean_contiguous_preserve_freq(self):
+        rng = date_range("1/1/2000", "3/1/2000", freq="B")
+
+        mask = np.zeros(len(rng), dtype=bool)
+        mask[10:20] = True
+
+        masked = rng[mask]
+        expected = rng[10:20]
+        assert expected.freq == rng.freq
+        tm.assert_index_equal(masked, expected)
+
+        mask[22] = True
+        masked = rng[mask]
+        assert masked.freq is None
+
+
+class TestGetitemCallable:
+    def test_getitem_callable(self):
+        # GH#12533
+        ser = Series(4, index=list("ABCD"))
+        result = ser[lambda x: "A"]
+        assert result == ser.loc["A"]
+
+        result = ser[lambda x: ["A", "B"]]
+        expected = ser.loc[["A", "B"]]
+        tm.assert_series_equal(result, expected)
+
+        result = ser[lambda x: [True, False, True, True]]
+        expected = ser.iloc[[0, 2, 3]]
+        tm.assert_series_equal(result, expected)
+
+
+def test_getitem_generator(string_series):
+    gen = (x > 0 for x in string_series)
+    result = string_series[gen]
+    result2 = string_series[iter(string_series > 0)]
+    expected = string_series[string_series > 0]
+    tm.assert_series_equal(result, expected)
+    tm.assert_series_equal(result2, expected)
+
+
+@pytest.mark.parametrize(
+    "series",
+    [
+        Series([0, 1]),
+        Series(date_range("2012-01-01", periods=2)),
+        Series(date_range("2012-01-01", periods=2, tz="CET")),
+    ],
+)
+def test_getitem_ndim_deprecated(series):
+    with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+        series[:, None]
+
+
+def test_getitem_multilevel_scalar_slice_not_implemented(
+    multiindex_year_month_day_dataframe_random_data,
+):
+    # not implementing this for now
+    df = multiindex_year_month_day_dataframe_random_data
+    ser = df["A"]
+
+    msg = r"\(2000, slice\(3, 4, None\)\)"
+    with pytest.raises(TypeError, match=msg):
+        ser[2000, 3:4]
+
+
+def test_getitem_dataframe_raises():
+    rng = list(range(10))
+    ser = Series(10, index=rng)
+    df = DataFrame(rng, index=rng)
+    msg = (
+        "Indexing a Series with DataFrame is not supported, "
+        "use the appropriate DataFrame column"
+    )
+    with pytest.raises(TypeError, match=msg):
+        ser[df > 5]
+
+
+def test_getitem_assignment_series_alignment():
+    # https://github.com/pandas-dev/pandas/issues/37427
+    # with getitem, when assigning with a Series, it is not first aligned
+    ser = Series(range(10))
+    idx = np.array([2, 4, 9])
+    ser[idx] = Series([10, 11, 12])
+    expected = Series([0, 1, 10, 3, 11, 5, 6, 7, 8, 12])
+    tm.assert_series_equal(ser, expected)
+
+
+def test_getitem_duplicate_index_mistyped_key_raises_keyerror():
+    # GH#29189 float_index.get_loc(None) should raise KeyError, not TypeError
+    ser = Series([2, 5, 6, 8], index=[2.0, 4.0, 4.0, 5.0])
+    with pytest.raises(KeyError, match="None"):
+        ser[None]
+
+    with pytest.raises(KeyError, match="None"):
+        ser.index.get_loc(None)
+
+    with pytest.raises(KeyError, match="None"):
+        ser.index._engine.get_loc(None)
+
+
+def test_getitem_1tuple_slice_without_multiindex():
+    ser = Series(range(5))
+    key = (slice(3),)
+
+    result = ser[key]
+    expected = ser[key[0]]
+    tm.assert_series_equal(result, expected)
+
+
+def test_getitem_preserve_name(datetime_series):
+    result = datetime_series[datetime_series > 0]
+    assert result.name == datetime_series.name
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = datetime_series[[0, 2, 4]]
+    assert result.name == datetime_series.name
+
+    result = datetime_series[5:10]
+    assert result.name == datetime_series.name
+
+
+def test_getitem_with_integer_labels():
+    # integer indexes, be careful
+    ser = Series(
+        np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2))
+    )
+    inds = [0, 2, 5, 7, 8]
+    arr_inds = np.array([0, 2, 5, 7, 8])
+    with pytest.raises(KeyError, match="not in index"):
+        ser[inds]
+
+    with pytest.raises(KeyError, match="not in index"):
+        ser[arr_inds]
+
+
+def test_getitem_missing(datetime_series):
+    # missing
+    d = datetime_series.index[0] - BDay()
+    msg = r"Timestamp\('1999-12-31 00:00:00'\)"
+    with pytest.raises(KeyError, match=msg):
+        datetime_series[d]
+
+
+def test_getitem_fancy(string_series, object_series):
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        slice1 = string_series[[1, 2, 3]]
+        slice2 = object_series[[1, 2, 3]]
+    assert string_series.index[2] == slice1.index[1]
+    assert object_series.index[2] == slice2.index[1]
+    assert string_series.iloc[2] == slice1.iloc[1]
+    assert object_series.iloc[2] == slice2.iloc[1]
+
+
+def test_getitem_box_float64(datetime_series):
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        value = datetime_series[5]
+    assert isinstance(value, np.float64)
+
+
+def test_getitem_unordered_dup():
+    obj = Series(range(5), index=["c", "a", "a", "b", "b"])
+    assert is_scalar(obj["c"])
+    assert obj["c"] == 0
+
+
+def test_getitem_dups():
+    ser = Series(range(5), index=["A", "A", "B", "C", "C"], dtype=np.int64)
+    expected = Series([3, 4], index=["C", "C"], dtype=np.int64)
+    result = ser["C"]
+    tm.assert_series_equal(result, expected)
+
+
+def test_getitem_categorical_str():
+    # GH#31765
+    ser = Series(range(5), index=Categorical(["a", "b", "c", "a", "b"]))
+    result = ser["a"]
+    expected = ser.iloc[[0, 3]]
+    tm.assert_series_equal(result, expected)
+
+
+def test_slice_can_reorder_not_uniquely_indexed():
+    ser = Series(1, index=["a", "a", "b", "b", "c"])
+    ser[::-1]  # it works!
+
+
+@pytest.mark.parametrize("index_vals", ["aabcd", "aadcb"])
+def test_duplicated_index_getitem_positional_indexer(index_vals):
+    # GH 11747
+    s = Series(range(5), index=list(index_vals))
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s[3]
+    assert result == 3
+
+
+class TestGetitemDeprecatedIndexers:
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_getitem_dict_and_set_deprecated(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2, 3])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser[key]
+
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_setitem_dict_and_set_disallowed(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2, 3])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser[key] = 1

vllm/lib/python3.10/site-packages/pandas/tests/series/indexing/test_indexing.py

@@ -0,0 +1,518 @@
+""" test get/set & misc """
+from datetime import timedelta
+import re
+
+import numpy as np
+import pytest
+
+from pandas.errors import IndexingError
+
+from pandas import (
+    NA,
+    DataFrame,
+    Index,
+    IndexSlice,
+    MultiIndex,
+    NaT,
+    Series,
+    Timedelta,
+    Timestamp,
+    concat,
+    date_range,
+    isna,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+def test_basic_indexing():
+    s = Series(
+        np.random.default_rng(2).standard_normal(5), index=["a", "b", "a", "a", "b"]
+    )
+
+    warn_msg = "Series.__[sg]etitem__ treating keys as positions is deprecated"
+    msg = "index 5 is out of bounds for axis 0 with size 5"
+    with pytest.raises(IndexError, match=msg):
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            s[5]
+    with pytest.raises(IndexError, match=msg):
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            s[5] = 0
+
+    with pytest.raises(KeyError, match=r"^'c'$"):
+        s["c"]
+
+    s = s.sort_index()
+
+    with pytest.raises(IndexError, match=msg):
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            s[5]
+    msg = r"index 5 is out of bounds for axis (0|1) with size 5|^5$"
+    with pytest.raises(IndexError, match=msg):
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            s[5] = 0
+
+
+def test_getitem_numeric_should_not_fallback_to_positional(any_numeric_dtype):
+    # GH51053
+    dtype = any_numeric_dtype
+    idx = Index([1, 0, 1], dtype=dtype)
+    ser = Series(range(3), index=idx)
+    result = ser[1]
+    expected = Series([0, 2], index=Index([1, 1], dtype=dtype))
+    tm.assert_series_equal(result, expected, check_exact=True)
+
+
+def test_setitem_numeric_should_not_fallback_to_positional(any_numeric_dtype):
+    # GH51053
+    dtype = any_numeric_dtype
+    idx = Index([1, 0, 1], dtype=dtype)
+    ser = Series(range(3), index=idx)
+    ser[1] = 10
+    expected = Series([10, 1, 10], index=idx)
+    tm.assert_series_equal(ser, expected, check_exact=True)
+
+
+def test_basic_getitem_with_labels(datetime_series):
+    indices = datetime_series.index[[5, 10, 15]]
+
+    result = datetime_series[indices]
+    expected = datetime_series.reindex(indices)
+    tm.assert_series_equal(result, expected)
+
+    result = datetime_series[indices[0] : indices[2]]
+    expected = datetime_series.loc[indices[0] : indices[2]]
+    tm.assert_series_equal(result, expected)
+
+
+def test_basic_getitem_dt64tz_values():
+    # GH12089
+    # with tz for values
+    ser = Series(
+        date_range("2011-01-01", periods=3, tz="US/Eastern"), index=["a", "b", "c"]
+    )
+    expected = Timestamp("2011-01-01", tz="US/Eastern")
+    result = ser.loc["a"]
+    assert result == expected
+    result = ser.iloc[0]
+    assert result == expected
+    result = ser["a"]
+    assert result == expected
+
+
+def test_getitem_setitem_ellipsis(using_copy_on_write, warn_copy_on_write):
+    s = Series(np.random.default_rng(2).standard_normal(10))
+
+    result = s[...]
+    tm.assert_series_equal(result, s)
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        s[...] = 5
+    if not using_copy_on_write:
+        assert (result == 5).all()
+
+
+@pytest.mark.parametrize(
+    "result_1, duplicate_item, expected_1",
+    [
+        [
+            Series({1: 12, 2: [1, 2, 2, 3]}),
+            Series({1: 313}),
+            Series({1: 12}, dtype=object),
+        ],
+        [
+            Series({1: [1, 2, 3], 2: [1, 2, 2, 3]}),
+            Series({1: [1, 2, 3]}),
+            Series({1: [1, 2, 3]}),
+        ],
+    ],
+)
+def test_getitem_with_duplicates_indices(result_1, duplicate_item, expected_1):
+    # GH 17610
+    result = result_1._append(duplicate_item)
+    expected = expected_1._append(duplicate_item)
+    tm.assert_series_equal(result[1], expected)
+    assert result[2] == result_1[2]
+
+
+def test_getitem_setitem_integers():
+    # caused bug without test
+    s = Series([1, 2, 3], ["a", "b", "c"])
+
+    assert s.iloc[0] == s["a"]
+    s.iloc[0] = 5
+    tm.assert_almost_equal(s["a"], 5)
+
+
+def test_series_box_timestamp():
+    rng = date_range("20090415", "20090519", freq="B")
+    ser = Series(rng)
+    assert isinstance(ser[0], Timestamp)
+    assert isinstance(ser.at[1], Timestamp)
+    assert isinstance(ser.iat[2], Timestamp)
+    assert isinstance(ser.loc[3], Timestamp)
+    assert isinstance(ser.iloc[4], Timestamp)
+
+    ser = Series(rng, index=rng)
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert isinstance(ser[0], Timestamp)
+    assert isinstance(ser.at[rng[1]], Timestamp)
+    assert isinstance(ser.iat[2], Timestamp)
+    assert isinstance(ser.loc[rng[3]], Timestamp)
+    assert isinstance(ser.iloc[4], Timestamp)
+
+
+def test_series_box_timedelta():
+    rng = timedelta_range("1 day 1 s", periods=5, freq="h")
+    ser = Series(rng)
+    assert isinstance(ser[0], Timedelta)
+    assert isinstance(ser.at[1], Timedelta)
+    assert isinstance(ser.iat[2], Timedelta)
+    assert isinstance(ser.loc[3], Timedelta)
+    assert isinstance(ser.iloc[4], Timedelta)
+
+
+def test_getitem_ambiguous_keyerror(indexer_sl):
+    ser = Series(range(10), index=list(range(0, 20, 2)))
+    with pytest.raises(KeyError, match=r"^1$"):
+        indexer_sl(ser)[1]
+
+
+def test_getitem_dups_with_missing(indexer_sl):
+    # breaks reindex, so need to use .loc internally
+    # GH 4246
+    ser = Series([1, 2, 3, 4], ["foo", "bar", "foo", "bah"])
+    with pytest.raises(KeyError, match=re.escape("['bam'] not in index")):
+        indexer_sl(ser)[["foo", "bar", "bah", "bam"]]
+
+
+def test_setitem_ambiguous_keyerror(indexer_sl):
+    s = Series(range(10), index=list(range(0, 20, 2)))
+
+    # equivalent of an append
+    s2 = s.copy()
+    indexer_sl(s2)[1] = 5
+    expected = concat([s, Series([5], index=[1])])
+    tm.assert_series_equal(s2, expected)
+
+
+def test_setitem(datetime_series):
+    datetime_series[datetime_series.index[5]] = np.nan
+    datetime_series.iloc[[1, 2, 17]] = np.nan
+    datetime_series.iloc[6] = np.nan
+    assert np.isnan(datetime_series.iloc[6])
+    assert np.isnan(datetime_series.iloc[2])
+    datetime_series[np.isnan(datetime_series)] = 5
+    assert not np.isnan(datetime_series.iloc[2])
+
+
+def test_setslice(datetime_series):
+    sl = datetime_series[5:20]
+    assert len(sl) == len(sl.index)
+    assert sl.index.is_unique is True
+
+
+def test_basic_getitem_setitem_corner(datetime_series):
+    # invalid tuples, e.g. td.ts[:, None] vs. td.ts[:, 2]
+    msg = "key of type tuple not found and not a MultiIndex"
+    with pytest.raises(KeyError, match=msg):
+        datetime_series[:, 2]
+    with pytest.raises(KeyError, match=msg):
+        datetime_series[:, 2] = 2
+
+    # weird lists. [slice(0, 5)] raises but not two slices
+    msg = "Indexing with a single-item list"
+    with pytest.raises(ValueError, match=msg):
+        # GH#31299
+        datetime_series[[slice(None, 5)]]
+
+    # but we're OK with a single-element tuple
+    result = datetime_series[(slice(None, 5),)]
+    expected = datetime_series[:5]
+    tm.assert_series_equal(result, expected)
+
+    # OK
+    msg = r"unhashable type(: 'slice')?"
+    with pytest.raises(TypeError, match=msg):
+        datetime_series[[5, [None, None]]]
+    with pytest.raises(TypeError, match=msg):
+        datetime_series[[5, [None, None]]] = 2
+
+
+def test_slice(string_series, object_series, using_copy_on_write, warn_copy_on_write):
+    original = string_series.copy()
+    numSlice = string_series[10:20]
+    numSliceEnd = string_series[-10:]
+    objSlice = object_series[10:20]
+
+    assert string_series.index[9] not in numSlice.index
+    assert object_series.index[9] not in objSlice.index
+
+    assert len(numSlice) == len(numSlice.index)
+    assert string_series[numSlice.index[0]] == numSlice[numSlice.index[0]]
+
+    assert numSlice.index[1] == string_series.index[11]
+    tm.assert_numpy_array_equal(np.array(numSliceEnd), np.array(string_series)[-10:])
+
+    # Test return view.
+    sl = string_series[10:20]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        sl[:] = 0
+
+    if using_copy_on_write:
+        # Doesn't modify parent (CoW)
+        tm.assert_series_equal(string_series, original)
+    else:
+        assert (string_series[10:20] == 0).all()
+
+
+def test_timedelta_assignment():
+    # GH 8209
+    s = Series([], dtype=object)
+    s.loc["B"] = timedelta(1)
+    tm.assert_series_equal(s, Series(Timedelta("1 days"), index=["B"]))
+
+    s = s.reindex(s.index.insert(0, "A"))
+    tm.assert_series_equal(s, Series([np.nan, Timedelta("1 days")], index=["A", "B"]))
+
+    s.loc["A"] = timedelta(1)
+    expected = Series(Timedelta("1 days"), index=["A", "B"])
+    tm.assert_series_equal(s, expected)
+
+
+def test_underlying_data_conversion(using_copy_on_write):
+    # GH 4080
+    df = DataFrame({c: [1, 2, 3] for c in ["a", "b", "c"]})
+    return_value = df.set_index(["a", "b", "c"], inplace=True)
+    assert return_value is None
+    s = Series([1], index=[(2, 2, 2)])
+    df["val"] = 0
+    df_original = df.copy()
+    df
+
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            df["val"].update(s)
+        expected = df_original
+    else:
+        with tm.assert_produces_warning(FutureWarning, match="inplace method"):
+            df["val"].update(s)
+        expected = DataFrame(
+            {"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3], "val": [0, 1, 0]}
+        )
+        return_value = expected.set_index(["a", "b", "c"], inplace=True)
+        assert return_value is None
+    tm.assert_frame_equal(df, expected)
+
+
+def test_preserve_refs(datetime_series):
+    seq = datetime_series.iloc[[5, 10, 15]]
+    seq.iloc[1] = np.nan
+    assert not np.isnan(datetime_series.iloc[10])
+
+
+def test_multilevel_preserve_name(lexsorted_two_level_string_multiindex, indexer_sl):
+    index = lexsorted_two_level_string_multiindex
+    ser = Series(
+        np.random.default_rng(2).standard_normal(len(index)), index=index, name="sth"
+    )
+
+    result = indexer_sl(ser)["foo"]
+    assert result.name == ser.name
+
+
+# miscellaneous methods
+
+
+@pytest.mark.parametrize(
+    "index",
+    [
+        date_range("2014-01-01", periods=20, freq="MS"),
+        period_range("2014-01", periods=20, freq="M"),
+        timedelta_range("0", periods=20, freq="h"),
+    ],
+)
+def test_slice_with_negative_step(index):
+    keystr1 = str(index[9])
+    keystr2 = str(index[13])
+
+    ser = Series(np.arange(20), index)
+    SLC = IndexSlice
+
+    for key in [keystr1, index[9]]:
+        tm.assert_indexing_slices_equivalent(ser, SLC[key::-1], SLC[9::-1])
+        tm.assert_indexing_slices_equivalent(ser, SLC[:key:-1], SLC[:8:-1])
+
+        for key2 in [keystr2, index[13]]:
+            tm.assert_indexing_slices_equivalent(ser, SLC[key2:key:-1], SLC[13:8:-1])
+            tm.assert_indexing_slices_equivalent(ser, SLC[key:key2:-1], SLC[0:0:-1])
+
+
+def test_tuple_index():
+    # GH 35534 - Selecting values when a Series has an Index of tuples
+    s = Series([1, 2], index=[("a",), ("b",)])
+    assert s[("a",)] == 1
+    assert s[("b",)] == 2
+    s[("b",)] = 3
+    assert s[("b",)] == 3
+
+
+def test_frozenset_index():
+    # GH35747 - Selecting values when a Series has an Index of frozenset
+    idx0, idx1 = frozenset("a"), frozenset("b")
+    s = Series([1, 2], index=[idx0, idx1])
+    assert s[idx0] == 1
+    assert s[idx1] == 2
+    s[idx1] = 3
+    assert s[idx1] == 3
+
+
+def test_loc_setitem_all_false_indexer():
+    # GH#45778
+    ser = Series([1, 2], index=["a", "b"])
+    expected = ser.copy()
+    rhs = Series([6, 7], index=["a", "b"])
+    ser.loc[ser > 100] = rhs
+    tm.assert_series_equal(ser, expected)
+
+
+def test_loc_boolean_indexer_non_matching_index():
+    # GH#46551
+    ser = Series([1])
+    result = ser.loc[Series([NA, False], dtype="boolean")]
+    expected = Series([], dtype="int64")
+    tm.assert_series_equal(result, expected)
+
+
+def test_loc_boolean_indexer_miss_matching_index():
+    # GH#46551
+    ser = Series([1])
+    indexer = Series([NA, False], dtype="boolean", index=[1, 2])
+    with pytest.raises(IndexingError, match="Unalignable"):
+        ser.loc[indexer]
+
+
+def test_loc_setitem_nested_data_enlargement():
+    # GH#48614
+    df = DataFrame({"a": [1]})
+    ser = Series({"label": df})
+    ser.loc["new_label"] = df
+    expected = Series({"label": df, "new_label": df})
+    tm.assert_series_equal(ser, expected)
+
+
+def test_loc_ea_numeric_index_oob_slice_end():
+    # GH#50161
+    ser = Series(1, index=Index([0, 1, 2], dtype="Int64"))
+    result = ser.loc[2:3]
+    expected = Series(1, index=Index([2], dtype="Int64"))
+    tm.assert_series_equal(result, expected)
+
+
+def test_getitem_bool_int_key():
+    # GH#48653
+    ser = Series({True: 1, False: 0})
+    with pytest.raises(KeyError, match="0"):
+        ser.loc[0]
+
+
+@pytest.mark.parametrize("val", [{}, {"b": "x"}])
+@pytest.mark.parametrize("indexer", [[], [False, False], slice(0, -1), np.array([])])
+def test_setitem_empty_indexer(indexer, val):
+    # GH#45981
+    df = DataFrame({"a": [1, 2], **val})
+    expected = df.copy()
+    df.loc[indexer] = 1.5
+    tm.assert_frame_equal(df, expected)
+
+
+class TestDeprecatedIndexers:
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_getitem_dict_and_set_deprecated(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser.loc[key]
+
+    @pytest.mark.parametrize("key", [{1}, {1: 1}, ({1}, 2), ({1: 1}, 2)])
+    def test_getitem_dict_and_set_deprecated_multiindex(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2], index=MultiIndex.from_tuples([(1, 2), (3, 4)]))
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser.loc[key]
+
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_setitem_dict_and_set_disallowed(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser.loc[key] = 1
+
+    @pytest.mark.parametrize("key", [{1}, {1: 1}, ({1}, 2), ({1: 1}, 2)])
+    def test_setitem_dict_and_set_disallowed_multiindex(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2], index=MultiIndex.from_tuples([(1, 2), (3, 4)]))
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser.loc[key] = 1
+
+
+class TestSetitemValidation:
+    # This is adapted from pandas/tests/arrays/masked/test_indexing.py
+    # but checks for warnings instead of errors.
+    def _check_setitem_invalid(self, ser, invalid, indexer, warn):
+        msg = "Setting an item of incompatible dtype is deprecated"
+        msg = re.escape(msg)
+
+        orig_ser = ser.copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            ser[indexer] = invalid
+            ser = orig_ser.copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            ser.iloc[indexer] = invalid
+            ser = orig_ser.copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            ser.loc[indexer] = invalid
+            ser = orig_ser.copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            ser[:] = invalid
+
+    _invalid_scalars = [
+        1 + 2j,
+        "True",
+        "1",
+        "1.0",
+        NaT,
+        np.datetime64("NaT"),
+        np.timedelta64("NaT"),
+    ]
+    _indexers = [0, [0], slice(0, 1), [True, False, False], slice(None, None, None)]
+
+    @pytest.mark.parametrize(
+        "invalid", _invalid_scalars + [1, 1.0, np.int64(1), np.float64(1)]
+    )
+    @pytest.mark.parametrize("indexer", _indexers)
+    def test_setitem_validation_scalar_bool(self, invalid, indexer):
+        ser = Series([True, False, False], dtype="bool")
+        self._check_setitem_invalid(ser, invalid, indexer, FutureWarning)
+
+    @pytest.mark.parametrize("invalid", _invalid_scalars + [True, 1.5, np.float64(1.5)])
+    @pytest.mark.parametrize("indexer", _indexers)
+    def test_setitem_validation_scalar_int(self, invalid, any_int_numpy_dtype, indexer):
+        ser = Series([1, 2, 3], dtype=any_int_numpy_dtype)
+        if isna(invalid) and invalid is not NaT and not np.isnat(invalid):
+            warn = None
+        else:
+            warn = FutureWarning
+        self._check_setitem_invalid(ser, invalid, indexer, warn)
+
+    @pytest.mark.parametrize("invalid", _invalid_scalars + [True])
+    @pytest.mark.parametrize("indexer", _indexers)
+    def test_setitem_validation_scalar_float(self, invalid, float_numpy_dtype, indexer):
+        ser = Series([1, 2, None], dtype=float_numpy_dtype)
+        self._check_setitem_invalid(ser, invalid, indexer, FutureWarning)

vllm/lib/python3.10/site-packages/pandas/tests/series/indexing/test_mask.py

@@ -0,0 +1,69 @@
+import numpy as np
+import pytest
+
+from pandas import Series
+import pandas._testing as tm
+
+
+def test_mask():
+    # compare with tested results in test_where
+    s = Series(np.random.default_rng(2).standard_normal(5))
+    cond = s > 0
+
+    rs = s.where(~cond, np.nan)
+    tm.assert_series_equal(rs, s.mask(cond))
+
+    rs = s.where(~cond)
+    rs2 = s.mask(cond)
+    tm.assert_series_equal(rs, rs2)
+
+    rs = s.where(~cond, -s)
+    rs2 = s.mask(cond, -s)
+    tm.assert_series_equal(rs, rs2)
+
+    cond = Series([True, False, False, True, False], index=s.index)
+    s2 = -(s.abs())
+    rs = s2.where(~cond[:3])
+    rs2 = s2.mask(cond[:3])
+    tm.assert_series_equal(rs, rs2)
+
+    rs = s2.where(~cond[:3], -s2)
+    rs2 = s2.mask(cond[:3], -s2)
+    tm.assert_series_equal(rs, rs2)
+
+    msg = "Array conditional must be same shape as self"
+    with pytest.raises(ValueError, match=msg):
+        s.mask(1)
+    with pytest.raises(ValueError, match=msg):
+        s.mask(cond[:3].values, -s)
+
+
+def test_mask_casts():
+    # dtype changes
+    ser = Series([1, 2, 3, 4])
+    result = ser.mask(ser > 2, np.nan)
+    expected = Series([1, 2, np.nan, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
+def test_mask_casts2():
+    # see gh-21891
+    ser = Series([1, 2])
+    res = ser.mask([True, False])
+
+    exp = Series([np.nan, 2])
+    tm.assert_series_equal(res, exp)
+
+
+def test_mask_inplace():
+    s = Series(np.random.default_rng(2).standard_normal(5))
+    cond = s > 0
+
+    rs = s.copy()
+    rs.mask(cond, inplace=True)
+    tm.assert_series_equal(rs.dropna(), s[~cond])
+    tm.assert_series_equal(rs, s.mask(cond))
+
+    rs = s.copy()
+    rs.mask(cond, -s, inplace=True)
+    tm.assert_series_equal(rs, s.mask(cond, -s))

vllm/lib/python3.10/site-packages/pandas/tests/series/indexing/test_set_value.py

@@ -0,0 +1,45 @@
+from datetime import datetime
+
+import numpy as np
+
+from pandas import (
+    DatetimeIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+def test_series_set_value():
+    # GH#1561
+
+    dates = [datetime(2001, 1, 1), datetime(2001, 1, 2)]
+    index = DatetimeIndex(dates)
+
+    s = Series(dtype=object)
+    s._set_value(dates[0], 1.0)
+    s._set_value(dates[1], np.nan)
+
+    expected = Series([1.0, np.nan], index=index)
+
+    tm.assert_series_equal(s, expected)
+
+
+def test_set_value_dt64(datetime_series):
+    idx = datetime_series.index[10]
+    res = datetime_series._set_value(idx, 0)
+    assert res is None
+    assert datetime_series[idx] == 0
+
+
+def test_set_value_str_index(string_series):
+    # equiv
+    ser = string_series.copy()
+    res = ser._set_value("foobar", 0)
+    assert res is None
+    assert ser.index[-1] == "foobar"
+    assert ser["foobar"] == 0
+
+    ser2 = string_series.copy()
+    ser2.loc["foobar"] = 0
+    assert ser2.index[-1] == "foobar"
+    assert ser2["foobar"] == 0