text: from distutils.version import LooseVersion
  from functools import reduce
  from itertools import product
  import operator
  from typing import Dict, Type
  import warnings
  
  import numpy as np
  from numpy.random import rand, randint, randn
  import pytest
  
  from pandas.errors import PerformanceWarning
  import pandas.util._test_decorators as td
  
  from pandas.core.dtypes.common import is_bool, is_list_like, is_scalar
  
  import pandas as pd
  from pandas import DataFrame, Series, compat, date_range
  import pandas._testing as tm
  from pandas.core.computation import pytables
  from pandas.core.computation.check import NUMEXPR_VERSION
  from pandas.core.computation.engines import ENGINES, NumExprClobberingError
  import pandas.core.computation.expr as expr
  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,
  )
  
  
  @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}",
              ),
          )
          for engine in ENGINES
      )
  )
  def engine(request):
      return request.param
  
  
  @pytest.fixture(params=expr.PARSERS)
  def parser(request):
      return request.param
  
  
  @pytest.fixture
  def ne_lt_2_6_9():
      if NUMEXPR_INSTALLED and NUMEXPR_VERSION >= LooseVersion("2.6.9"):
          pytest.skip("numexpr is >= 2.6.9")
      return "numexpr"
  
  
  @pytest.fixture
  def unary_fns_for_ne():
      if NUMEXPR_INSTALLED:
          if NUMEXPR_VERSION >= LooseVersion("2.6.9"):
              return _unary_math_ops
          else:
              return tuple(x for x in _unary_math_ops if x not in ("floor", "ceil"))
      else:
          pytest.skip("numexpr is not present")
  
  
  def engine_has_neg_frac(engine):
      return ENGINES[engine].has_neg_frac
  
  
  def _eval_single_bin(lhs, cmp1, rhs, engine):
      c = _binary_ops_dict[cmp1]
      if engine_has_neg_frac(engine):
          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)
  
  
  def _series_and_2d_ndarray(lhs, rhs):
      return (
          isinstance(lhs, Series) and isinstance(rhs, np.ndarray) and rhs.ndim > 1
      ) or (isinstance(rhs, Series) and isinstance(lhs, np.ndarray) and lhs.ndim > 1)
  
  
  def _series_and_frame(lhs, rhs):
      return (isinstance(lhs, Series) and isinstance(rhs, DataFrame)) or (
          isinstance(rhs, Series) and isinstance(lhs, DataFrame)
      )
  
  
  def _bool_and_frame(lhs, rhs):
      return isinstance(lhs, bool) and isinstance(rhs, pd.core.generic.NDFrame)
  
  
  def _is_py3_complex_incompat(result, expected):
      return isinstance(expected, (complex, np.complexfloating)) and np.isnan(result)
  
  
  _good_arith_ops = set(ARITH_OPS_SYMS).difference(SPECIAL_CASE_ARITH_OPS_SYMS)
  
  
  @td.skip_if_no_ne
  class TestEvalNumexprPandas:
      @classmethod
      def setup_class(cls):
          import numexpr as ne
  
          cls.ne = ne
          cls.engine = "numexpr"
          cls.parser = "pandas"
  
      @classmethod
      def teardown_class(cls):
          del cls.engine, cls.parser
          if hasattr(cls, "ne"):
              del cls.ne
  
      def setup_data(self):
          nan_df1 = DataFrame(rand(10, 5))
          nan_df1[nan_df1 > 0.5] = np.nan
          nan_df2 = DataFrame(rand(10, 5))
          nan_df2[nan_df2 > 0.5] = np.nan
  
          self.pandas_lhses = (
              DataFrame(randn(10, 5)),
              Series(randn(5)),
              Series([1, 2, np.nan, np.nan, 5]),
              nan_df1,
          )
          self.pandas_rhses = (
              DataFrame(randn(10, 5)),
              Series(randn(5)),
              Series([1, 2, np.nan, np.nan, 5]),
              nan_df2,
          )
          self.scalar_lhses = (randn(),)
          self.scalar_rhses = (randn(),)
  
          self.lhses = self.pandas_lhses + self.scalar_lhses
          self.rhses = self.pandas_rhses + self.scalar_rhses
  
      def setup_ops(self):
          self.cmp_ops = expr.CMP_OPS_SYMS
          self.cmp2_ops = self.cmp_ops[::-1]
          self.bin_ops = expr.BOOL_OPS_SYMS
          self.special_case_ops = SPECIAL_CASE_ARITH_OPS_SYMS
          self.arith_ops = _good_arith_ops
          self.unary_ops = "-", "~", "not "
  
      def setup_method(self, method):
          self.setup_ops()
          self.setup_data()
          self.current_engines = (engine for engine in ENGINES if engine != self.engine)
  
      def teardown_method(self, method):
          del self.lhses, self.rhses, self.scalar_rhses, self.scalar_lhses
          del self.pandas_rhses, self.pandas_lhses, self.current_engines
  
      @pytest.mark.slow
      @pytest.mark.parametrize(
          "cmp1",
          ["!=", "==", "<=", ">=", "<", ">"],
          ids=["ne", "eq", "le", "ge", "lt", "gt"],
      )
      @pytest.mark.parametrize("cmp2", [">", "<"], ids=["gt", "lt"])
      def test_complex_cmp_ops(self, cmp1, cmp2):
          for lhs, rhs, binop in product(self.lhses, self.rhses, self.bin_ops):
              lhs_new = _eval_single_bin(lhs, cmp1, rhs, self.engine)
              rhs_new = _eval_single_bin(lhs, cmp2, rhs, self.engine)
              expected = _eval_single_bin(lhs_new, binop, rhs_new, self.engine)
  
              ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
              result = pd.eval(ex, engine=self.engine, parser=self.parser)
              self.check_equal(result, expected)
  
      def test_simple_cmp_ops(self):
          bool_lhses = (
              DataFrame(tm.randbool(size=(10, 5))),
              Series(tm.randbool((5,))),
              tm.randbool(),
          )
          bool_rhses = (
              DataFrame(tm.randbool(size=(10, 5))),
              Series(tm.randbool((5,))),
              tm.randbool(),
          )
          for lhs, rhs, cmp_op in product(bool_lhses, bool_rhses, self.cmp_ops):
              self.check_simple_cmp_op(lhs, cmp_op, rhs)
  
      @pytest.mark.slow
      def test_binary_arith_ops(self):
          for lhs, op, rhs in product(self.lhses, self.arith_ops, self.rhses):
              self.check_binary_arith_op(lhs, op, rhs)
  
      def test_modulus(self):
          for lhs, rhs in product(self.lhses, self.rhses):
              self.check_modulus(lhs, "%", rhs)
  
      def test_floor_division(self):
          for lhs, rhs in product(self.lhses, self.rhses):
              self.check_floor_division(lhs, "//", rhs)
  
      @td.skip_if_windows
      def test_pow(self):
          # odd failure on win32 platform, so skip
          for lhs, rhs in product(self.lhses, self.rhses):
              self.check_pow(lhs, "**", rhs)
  
      @pytest.mark.slow
      def test_single_invert_op(self):
          for lhs, op, rhs in product(self.lhses, self.cmp_ops, self.rhses):
              self.check_single_invert_op(lhs, op, rhs)
  
      @pytest.mark.slow
      def test_compound_invert_op(self):
          for lhs, op, rhs in product(self.lhses, self.cmp_ops, self.rhses):
              self.check_compound_invert_op(lhs, op, rhs)
  
      @pytest.mark.slow
      def test_chained_cmp_op(self):
          mids = self.lhses
          cmp_ops = "<", ">"
          for lhs, cmp1, mid, cmp2, rhs in product(
              self.lhses, cmp_ops, mids, cmp_ops, self.rhses
          ):
              self.check_chained_cmp_op(lhs, cmp1, mid, cmp2, rhs)
  
      def check_equal(self, result, expected):
          if isinstance(result, DataFrame):
              tm.assert_frame_equal(result, expected)
          elif isinstance(result, Series):
              tm.assert_series_equal(result, expected)
          elif isinstance(result, np.ndarray):
              tm.assert_numpy_array_equal(result, expected)
          else:
              assert result == expected
  
      def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs):
          def check_operands(left, right, cmp_op):
              return _eval_single_bin(left, cmp_op, right, self.engine)
  
          lhs_new = check_operands(lhs, mid, cmp1)
          rhs_new = check_operands(mid, rhs, cmp2)
  
          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, self.engine)
  
              for ex in (ex1, ex2, ex3):
                  result = pd.eval(ex, engine=self.engine, parser=self.parser)
  
                  tm.assert_almost_equal(result, expected)
  
      def check_simple_cmp_op(self, lhs, cmp1, rhs):
          ex = f"lhs {cmp1} rhs"
          msg = (
              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 cmp1 in ("in", "not in") and not is_list_like(rhs):
              with pytest.raises(TypeError, match=msg):
                  pd.eval(
                      ex,
                      engine=self.engine,
                      parser=self.parser,
                      local_dict={"lhs": lhs, "rhs": rhs},
                  )
          else:
              expected = _eval_single_bin(lhs, cmp1, rhs, self.engine)
              result = pd.eval(ex, engine=self.engine, parser=self.parser)
              self.check_equal(result, expected)
  
      def check_binary_arith_op(self, lhs, arith1, rhs):
          ex = f"lhs {arith1} rhs"
          result = pd.eval(ex, engine=self.engine, parser=self.parser)
          expected = _eval_single_bin(lhs, arith1, rhs, self.engine)
  
          tm.assert_almost_equal(result, expected)
          ex = f"lhs {arith1} rhs {arith1} rhs"
          result = pd.eval(ex, engine=self.engine, parser=self.parser)
          nlhs = _eval_single_bin(lhs, arith1, rhs, self.engine)
          self.check_alignment(result, nlhs, rhs, arith1)
  
      def check_alignment(self, result, nlhs, ghs, op):
          try:
              nlhs, ghs = nlhs.align(ghs)
          except (ValueError, TypeError, AttributeError):
              # ValueError: series frame or frame series align
              # TypeError, AttributeError: series or frame with scalar align
              pass
          else:
  
              # direct numpy comparison
              expected = self.ne.evaluate(f"nlhs {op} ghs")
              tm.assert_numpy_array_equal(result.values, expected)
  
      # modulus, pow, and floor division require special casing
  
      def check_modulus(self, lhs, arith1, rhs):
          ex = f"lhs {arith1} rhs"
          result = pd.eval(ex, engine=self.engine, parser=self.parser)
          expected = lhs % rhs
  
          tm.assert_almost_equal(result, expected)
          expected = self.ne.evaluate(f"expected {arith1} rhs")
          if isinstance(result, (DataFrame, Series)):
              tm.assert_almost_equal(result.values, expected)
          else:
              tm.assert_almost_equal(result, expected.item())
  
      def check_floor_division(self, lhs, arith1, rhs):
          ex = f"lhs {arith1} rhs"
  
          if self.engine == "python":
              res = pd.eval(ex, engine=self.engine, parser=self.parser)
              expected = lhs // rhs
              self.check_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=self.engine,
                      parser=self.parser,
                  )
  
      def get_expected_pow_result(self, lhs, rhs):
          try:
              expected = _eval_single_bin(lhs, "**", rhs, self.engine)
          except ValueError as e:
              if str(e).startswith(
                  "negative number cannot be raised to a fractional power"
              ):
                  if self.engine == "python":
                      pytest.skip(str(e))
                  else:
                      expected = np.nan
              else:
                  raise
          return expected
  
      def check_pow(self, lhs, arith1, rhs):
          ex = f"lhs {arith1} rhs"
          expected = self.get_expected_pow_result(lhs, rhs)
          result = pd.eval(ex, engine=self.engine, parser=self.parser)
  
          if (
              is_scalar(lhs)
              and is_scalar(rhs)
              and _is_py3_complex_incompat(result, expected)
          ):
              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 = f"(lhs {arith1} rhs) {arith1} rhs"
              result = pd.eval(ex, engine=self.engine, parser=self.parser)
              expected = self.get_expected_pow_result(
                  self.get_expected_pow_result(lhs, rhs), rhs
              )
              tm.assert_almost_equal(result, expected)
  
      def check_single_invert_op(self, lhs, cmp1, rhs):
          # simple
          for el in (lhs, rhs):
              try:
                  elb = el.astype(bool)
              except AttributeError:
                  elb = np.array([bool(el)])
              expected = ~elb
              result = pd.eval("~elb", engine=self.engine, parser=self.parser)
              tm.assert_almost_equal(expected, result)
  
              for engine in self.current_engines:
                  tm.assert_almost_equal(
                      result, pd.eval("~elb", engine=engine, parser=self.parser)
                  )
  
      def check_compound_invert_op(self, lhs, cmp1, rhs):
          skip_these = ["in", "not in"]
          ex = f"~(lhs {cmp1} rhs)"
  
          msg = (
              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 cmp1 in skip_these:
              with pytest.raises(TypeError, match=msg):
                  pd.eval(
                      ex,
                      engine=self.engine,
                      parser=self.parser,
                      local_dict={"lhs": lhs, "rhs": rhs},
                  )
          else:
              # compound
              if is_scalar(lhs) and is_scalar(rhs):
                  lhs, rhs = map(lambda x: np.array([x]), (lhs, rhs))
              expected = _eval_single_bin(lhs, cmp1, rhs, self.engine)
              if is_scalar(expected):
                  expected = not expected
              else:
                  expected = ~expected
              result = pd.eval(ex, engine=self.engine, parser=self.parser)
              tm.assert_almost_equal(expected, result)
  
              # make sure the other engines work the same as this one
              for engine in self.current_engines:
                  ev = pd.eval(ex, engine=self.engine, parser=self.parser)
                  tm.assert_almost_equal(ev, result)
  
      def ex(self, op, var_name="lhs"):
          return f"{op}{var_name}"
  
      def test_frame_invert(self):
          expr = self.ex("~")
  
          # ~ ##
          # frame
          # float always raises
          lhs = DataFrame(randn(5, 2))
          if self.engine == "numexpr":
              msg = "couldn't find matching opcode for 'invert_dd'"
              with pytest.raises(NotImplementedError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
          else:
              msg = "ufunc 'invert' not supported for the input types"
              with pytest.raises(TypeError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
  
          # int raises on numexpr
          lhs = DataFrame(randint(5, size=(5, 2)))
          if self.engine == "numexpr":
              msg = "couldn't find matching opcode for 'invert"
              with pytest.raises(NotImplementedError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
          else:
              expect = ~lhs
              result = pd.eval(expr, engine=self.engine, parser=self.parser)
              tm.assert_frame_equal(expect, result)
  
          # bool always works
          lhs = DataFrame(rand(5, 2) > 0.5)
          expect = ~lhs
          result = pd.eval(expr, engine=self.engine, parser=self.parser)
          tm.assert_frame_equal(expect, result)
  
          # object raises
          lhs = DataFrame({"b": ["a", 1, 2.0], "c": rand(3) > 0.5})
          if self.engine == "numexpr":
              with pytest.raises(ValueError, match="unknown type object"):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
          else:
              msg = "bad operand type for unary ~: 'str'"
              with pytest.raises(TypeError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
  
      def test_series_invert(self):
          # ~ ####
          expr = self.ex("~")
  
          # series
          # float raises
          lhs = Series(randn(5))
          if self.engine == "numexpr":
              msg = "couldn't find matching opcode for 'invert_dd'"
              with pytest.raises(NotImplementedError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
          else:
              msg = "ufunc 'invert' not supported for the input types"
              with pytest.raises(TypeError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
  
          # int raises on numexpr
          lhs = Series(randint(5, size=5))
          if self.engine == "numexpr":
              msg = "couldn't find matching opcode for 'invert"
              with pytest.raises(NotImplementedError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
          else:
              expect = ~lhs
              result = pd.eval(expr, engine=self.engine, parser=self.parser)
              tm.assert_series_equal(expect, result)
  
          # bool
          lhs = Series(rand(5) > 0.5)
          expect = ~lhs
          result = pd.eval(expr, engine=self.engine, parser=self.parser)
          tm.assert_series_equal(expect, result)
  
          # float
          # int
          # bool
  
          # object
          lhs = Series(["a", 1, 2.0])
          if self.engine == "numexpr":
              with pytest.raises(ValueError, match="unknown type object"):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
          else:
              msg = "bad operand type for unary ~: 'str'"
              with pytest.raises(TypeError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
  
      def test_frame_negate(self):
          expr = self.ex("-")
  
          # float
          lhs = DataFrame(randn(5, 2))
          expect = -lhs
          result = pd.eval(expr, engine=self.engine, parser=self.parser)
          tm.assert_frame_equal(expect, result)
  
          # int
          lhs = DataFrame(randint(5, size=(5, 2)))
          expect = -lhs
          result = pd.eval(expr, engine=self.engine, parser=self.parser)
          tm.assert_frame_equal(expect, result)
  
          # bool doesn't work with numexpr but works elsewhere
          lhs = DataFrame(rand(5, 2) > 0.5)
          if self.engine == "numexpr":
              msg = "couldn't find matching opcode for 'neg_bb'"
              with pytest.raises(NotImplementedError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
          else:
              expect = -lhs
              result = pd.eval(expr, engine=self.engine, parser=self.parser)
              tm.assert_frame_equal(expect, result)
  
      def test_series_negate(self):
          expr = self.ex("-")
  
          # float
          lhs = Series(randn(5))
          expect = -lhs
          result = pd.eval(expr, engine=self.engine, parser=self.parser)
          tm.assert_series_equal(expect, result)
  
          # int
          lhs = Series(randint(5, size=5))
          expect = -lhs
          result = pd.eval(expr, engine=self.engine, parser=self.parser)
          tm.assert_series_equal(expect, result)
  
          # bool doesn't work with numexpr but works elsewhere
          lhs = Series(rand(5) > 0.5)
          if self.engine == "numexpr":
              msg = "couldn't find matching opcode for 'neg_bb'"
              with pytest.raises(NotImplementedError, match=msg):
                  result = pd.eval(expr, engine=self.engine, parser=self.parser)
          else:
              expect = -lhs
              result = pd.eval(expr, engine=self.engine, parser=self.parser)
              tm.assert_series_equal(expect, result)
  
      @pytest.mark.parametrize(
          "lhs",
          [
              # Float
              DataFrame(randn(5, 2)),
              # Int
              DataFrame(randint(5, size=(5, 2))),
              # bool doesn't work with numexpr but works elsewhere
              DataFrame(rand(5, 2) > 0.5),
          ],
      )
      def test_frame_pos(self, lhs):
          expr = self.ex("+")
          expect = lhs
  
          result = pd.eval(expr, engine=self.engine, parser=self.parser)
          tm.assert_frame_equal(expect, result)
  
      @pytest.mark.parametrize(
          "lhs",
          [
              # Float
              Series(randn(5)),
              # Int
              Series(randint(5, size=5)),
              # bool doesn't work with numexpr but works elsewhere
              Series(rand(5) > 0.5),
          ],
      )
      def test_series_pos(self, lhs):
          expr = self.ex("+")
          expect = lhs
  
          result = pd.eval(expr, engine=self.engine, parser=self.parser)
          tm.assert_series_equal(expect, result)
  
      def test_scalar_unary(self):
          msg = "bad operand type for unary ~: 'float'"
          with pytest.raises(TypeError, match=msg):
              pd.eval("~1.0", engine=self.engine, parser=self.parser)
  
          assert pd.eval("-1.0", parser=self.parser, engine=self.engine) == -1.0
          assert pd.eval("+1.0", parser=self.parser, engine=self.engine) == +1.0
          assert pd.eval("~1", parser=self.parser, engine=self.engine) == ~1
          assert pd.eval("-1", parser=self.parser, engine=self.engine) == -1
          assert pd.eval("+1", parser=self.parser, engine=self.engine) == +1
          assert pd.eval("~True", parser=self.parser, engine=self.engine) == ~True
          assert pd.eval("~False", parser=self.parser, engine=self.engine) == ~False
          assert pd.eval("-True", parser=self.parser, engine=self.engine) == -True
          assert pd.eval("-False", parser=self.parser, engine=self.engine) == -False
          assert pd.eval("+True", parser=self.parser, engine=self.engine) == +True
          assert pd.eval("+False", parser=self.parser, engine=self.engine) == +False
  
      def test_unary_in_array(self):
          # GH 11235
          tm.assert_numpy_array_equal(
              pd.eval(
                  "[-True, True, ~True, +True,"
                  "-False, False, ~False, +False,"
                  "-37, 37, ~37, +37]"
              ),
              np.array(
                  [
                      -True,
                      True,
                      ~True,
                      +True,
                      -False,
                      False,
                      ~False,
                      +False,
                      -37,
                      37,
                      ~37,
                      +37,
                  ],
                  dtype=np.object_,
              ),
          )
  
      @pytest.mark.parametrize("dtype", [np.float32, np.float64])
      def test_float_comparison_bin_op(self, dtype):
          # GH 16363
          df = DataFrame({"x": np.array([0], dtype=dtype)})
          res = df.eval("x < -0.1")
          assert res.values == np.array([False])
  
          res = df.eval("-5 > x")
          assert res.values == np.array([False])
  
      def test_disallow_scalar_bool_ops(self):
          exprs = "1 or 2", "1 and 2"
          exprs += "a and b", "a or b"
          exprs += ("1 or 2 and (3 + 2) > 3",)
          exprs += ("2 * x > 2 or 1 and 2",)
          exprs += ("2 * df > 3 and 1 or a",)
  
          x, a, b, df = np.random.randn(3), 1, 2, DataFrame(randn(3, 2))  # noqa
          for ex in exprs:
              msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
              with pytest.raises(NotImplementedError, match=msg):
                  pd.eval(ex, engine=self.engine, parser=self.parser)
  
      def test_identical(self):
          # see gh-10546
          x = 1
          result = pd.eval("x", engine=self.engine, parser=self.parser)
          assert result == 1
          assert is_scalar(result)
  
          x = 1.5
          result = pd.eval("x", engine=self.engine, parser=self.parser)
          assert result == 1.5
          assert is_scalar(result)
  
          x = False
          result = pd.eval("x", engine=self.engine, parser=self.parser)
          assert not result
          assert is_bool(result)
          assert is_scalar(result)
  
          x = np.array([1])
          result = pd.eval("x", engine=self.engine, parser=self.parser)
          tm.assert_numpy_array_equal(result, np.array([1]))
          assert result.shape == (1,)
  
          x = np.array([1.5])
          result = pd.eval("x", engine=self.engine, parser=self.parser)
          tm.assert_numpy_array_equal(result, np.array([1.5]))
          assert result.shape == (1,)
  
          x = np.array([False])  # noqa
          result = pd.eval("x", engine=self.engine, parser=self.parser)
          tm.assert_numpy_array_equal(result, np.array([False]))
          assert result.shape == (1,)
  
      def test_line_continuation(self):
          # GH 11149
          exp = """1 + 2 * \
          5 - 1 + 2 """
          result = pd.eval(exp, engine=self.engine, parser=self.parser)
          assert result == 12
  
      def test_float_truncation(self):
          # GH 14241
          exp = "1000000000.006"
          result = pd.eval(exp, engine=self.engine, parser=self.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")
  
  
  @td.skip_if_no_ne
  class TestEvalNumexprPython(TestEvalNumexprPandas):
      @classmethod
      def setup_class(cls):
          super().setup_class()
          import numexpr as ne
  
          cls.ne = ne
          cls.engine = "numexpr"
          cls.parser = "python"
  
      def setup_ops(self):
          self.cmp_ops = [op for op in expr.CMP_OPS_SYMS if op not in ("in", "not in")]
          self.cmp2_ops = self.cmp_ops[::-1]
          self.bin_ops = [op for op in expr.BOOL_OPS_SYMS if op not in ("and", "or")]
          self.special_case_ops = SPECIAL_CASE_ARITH_OPS_SYMS
          self.arith_ops = _good_arith_ops
          self.unary_ops = "+", "-", "~"
  
      def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs):
          ex1 = f"lhs {cmp1} mid {cmp2} rhs"
          msg = "'BoolOp' nodes are not implemented"
          with pytest.raises(NotImplementedError, match=msg):
              pd.eval(ex1, engine=self.engine, parser=self.parser)
  
  
  class TestEvalPythonPython(TestEvalNumexprPython):
      @classmethod
      def setup_class(cls):
          super().setup_class()
          cls.engine = "python"
          cls.parser = "python"
  
      def check_modulus(self, lhs, arith1, rhs):
          ex = f"lhs {arith1} rhs"
          result = pd.eval(ex, engine=self.engine, parser=self.parser)
  
          expected = lhs % rhs
          tm.assert_almost_equal(result, expected)
  
          expected = _eval_single_bin(expected, arith1, rhs, self.engine)
          tm.assert_almost_equal(result, expected)
  
      def check_alignment(self, result, nlhs, ghs, op):
          try:
              nlhs, ghs = nlhs.align(ghs)
          except (ValueError, TypeError, AttributeError):
              # ValueError: series frame or frame series align
              # TypeError, AttributeError: series or frame with scalar align
              pass
          else:
              expected = eval(f"nlhs {op} ghs")
              tm.assert_almost_equal(result, expected)
  
  
  class TestEvalPythonPandas(TestEvalPythonPython):
      @classmethod
      def setup_class(cls):
          super().setup_class()
          cls.engine = "python"
          cls.parser = "pandas"
  
      def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs):
          TestEvalNumexprPandas.check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs)
  
  
  f = lambda *args, **kwargs: np.random.randn()
  
  
  # -------------------------------------
  # gh-12388: Typecasting rules consistency with python
  
  
  class TestTypeCasting:
      @pytest.mark.parametrize("op", ["+", "-", "*", "**", "/"])
      # maybe someday... numexpr has too many upcasting rules now
      # chain(*(np.sctypes[x] for x in ['uint', 'int', 'float']))
      @pytest.mark.parametrize("dt", [np.float32, np.float64])
      def test_binop_typecasting(self, engine, parser, op, dt):
          df = tm.makeCustomDataframe(5, 3, data_gen_f=f, dtype=dt)
          s = f"df {op} 3"
          res = pd.eval(s, engine=engine, parser=parser)
          assert df.values.dtype == dt
          assert res.values.dtype == dt
          tm.assert_frame_equal(res, eval(s))
  
          s = f"3 {op} df"
          res = pd.eval(s, engine=engine, parser=parser)
          assert df.values.dtype == dt
          assert res.values.dtype == dt
          tm.assert_frame_equal(res, eval(s))
  
  
  # -------------------------------------
  # Basic and complex alignment
  
  
  def _is_datetime(x):
      return issubclass(x.dtype.type, np.datetime64)
  
  
  def should_warn(*args):
      not_mono = not any(map(operator.attrgetter("is_monotonic"), args))
      only_one_dt = reduce(operator.xor, map(_is_datetime, args))
      return not_mono and only_one_dt
  
  
  class TestAlignment:
  
      index_types = "i", "u", "dt"
      lhs_index_types = index_types + ("s",)  # 'p'
  
      def test_align_nested_unary_op(self, engine, parser):
          s = "df * ~2"
          df = tm.makeCustomDataframe(5, 3, data_gen_f=f)
          res = pd.eval(s, engine=engine, parser=parser)
          tm.assert_frame_equal(res, df * ~2)
  
      def test_basic_frame_alignment(self, engine, parser):
          args = product(self.lhs_index_types, self.index_types, self.index_types)
          with warnings.catch_warnings(record=True):
              warnings.simplefilter("always", RuntimeWarning)
              for lr_idx_type, rr_idx_type, c_idx_type in args:
                  df = tm.makeCustomDataframe(
                      10, 10, data_gen_f=f, r_idx_type=lr_idx_type, c_idx_type=c_idx_type
                  )
                  df2 = tm.makeCustomDataframe(
                      20, 10, data_gen_f=f, r_idx_type=rr_idx_type, c_idx_type=c_idx_type
                  )
                  # 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)
  
      def test_frame_comparison(self, engine, parser):
          args = product(self.lhs_index_types, repeat=2)
          for r_idx_type, c_idx_type in args:
              df = tm.makeCustomDataframe(
                  10, 10, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type
              )
              res = pd.eval("df < 2", engine=engine, parser=parser)
              tm.assert_frame_equal(res, df < 2)
  
              df3 = DataFrame(randn(*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.slow
      def test_medium_complex_frame_alignment(self, engine, parser):
          args = product(
              self.lhs_index_types, self.index_types, self.index_types, self.index_types
          )
  
          with warnings.catch_warnings(record=True):
              warnings.simplefilter("always", RuntimeWarning)
  
              for r1, c1, r2, c2 in args:
                  df = tm.makeCustomDataframe(
                      3, 2, data_gen_f=f, r_idx_type=r1, c_idx_type=c1
                  )
                  df2 = tm.makeCustomDataframe(
                      4, 2, data_gen_f=f, r_idx_type=r2, c_idx_type=c2
                  )
                  df3 = tm.makeCustomDataframe(
                      5, 2, data_gen_f=f, r_idx_type=r2, c_idx_type=c2
                  )
                  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)
  
      def test_basic_frame_series_alignment(self, engine, parser):
          def testit(r_idx_type, c_idx_type, index_name):
              df = tm.makeCustomDataframe(
                  10, 10, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type
              )
              index = getattr(df, index_name)
              s = Series(np.random.randn(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)
  
          args = product(self.lhs_index_types, self.index_types, ("index", "columns"))
          with warnings.catch_warnings(record=True):
              warnings.simplefilter("always", RuntimeWarning)
              for r_idx_type, c_idx_type, index_name in args:
                  testit(r_idx_type, c_idx_type, index_name)
  
      def test_basic_series_frame_alignment(self, engine, parser):
          def testit(r_idx_type, c_idx_type, index_name):
              df = tm.makeCustomDataframe(
                  10, 7, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type
              )
              index = getattr(df, index_name)
              s = Series(np.random.randn(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)
  
          # only test dt with dt, otherwise weird joins result
          args = product(["i", "u", "s"], ["i", "u", "s"], ("index", "columns"))
          with warnings.catch_warnings(record=True):
              # avoid warning about comparing strings and ints
              warnings.simplefilter("ignore", RuntimeWarning)
  
              for r_idx_type, c_idx_type, index_name in args:
                  testit(r_idx_type, c_idx_type, index_name)
  
          # dt with dt
          args = product(["dt"], ["dt"], ("index", "columns"))
          with warnings.catch_warnings(record=True):
              # avoid warning about comparing strings and ints
              warnings.simplefilter("ignore", RuntimeWarning)
  
              for r_idx_type, c_idx_type, index_name in args:
                  testit(r_idx_type, c_idx_type, index_name)
  
      def test_series_frame_commutativity(self, engine, parser):
          args = product(
              self.lhs_index_types, self.index_types, ("+", "*"), ("index", "columns")
          )
  
          with warnings.catch_warnings(record=True):
              warnings.simplefilter("always", RuntimeWarning)
              for r_idx_type, c_idx_type, op, index_name in args:
                  df = tm.makeCustomDataframe(
                      10, 10, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type
                  )
                  index = getattr(df, index_name)
                  s = Series(np.random.randn(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.slow
      def test_complex_series_frame_alignment(self, engine, parser):
          import random
  
          args = product(
              self.lhs_index_types, self.index_types, self.index_types, self.index_types
          )
          n = 3
          m1 = 5
          m2 = 2 * m1
  
          with warnings.catch_warnings(record=True):
              warnings.simplefilter("always", RuntimeWarning)
              for r1, r2, c1, c2 in args:
                  index_name = random.choice(["index", "columns"])
                  obj_name = random.choice(["df", "df2"])
  
                  df = tm.makeCustomDataframe(
                      m1, n, data_gen_f=f, r_idx_type=r1, c_idx_type=c1
                  )
                  df2 = tm.makeCustomDataframe(
                      m2, n, data_gen_f=f, r_idx_type=r2, c_idx_type=c2
                  )
                  index = getattr(locals().get(obj_name), index_name)
                  ser = Series(np.random.randn(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(randn(1000, 10))
          s = Series(randn(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(randn(1000))
          with tm.assert_produces_warning(False):
              pd.eval("df + s", engine=engine, parser=parser)
  
          df = DataFrame(randn(10, 10000))
          s = Series(randn(10000))
          with tm.assert_produces_warning(False):
              pd.eval("df + s", engine=engine, parser=parser)
  
          df = DataFrame(randn(10, 10))
          s = Series(randn(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)
                  loged = 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 {loged:.4g}; performance may suffer"
                  )
                  assert msg == expected
  
  
  # ------------------------------------
  # Slightly more complex ops
  
  
  @td.skip_if_no_ne
  class TestOperationsNumExprPandas:
      @classmethod
      def setup_class(cls):
          cls.engine = "numexpr"
          cls.parser = "pandas"
          cls.arith_ops = expr.ARITH_OPS_SYMS + expr.CMP_OPS_SYMS
  
      @classmethod
      def teardown_class(cls):
          del cls.engine, cls.parser
  
      def eval(self, *args, **kwargs):
          kwargs["engine"] = self.engine
          kwargs["parser"] = self.parser
          kwargs["level"] = kwargs.pop("level", 0) + 1
          return pd.eval(*args, **kwargs)
  
      def test_simple_arith_ops(self):
          ops = (op for op in self.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=self.engine, parser=self.parser)
              else:
                  expec = _eval_single_bin(1, op, 1, self.engine)
                  x = self.eval(ex, engine=self.engine, parser=self.parser)
                  assert x == expec
  
                  expec = _eval_single_bin(x, op, 1, self.engine)
                  y = self.eval(
                      ex2, local_dict={"x": x}, engine=self.engine, parser=self.parser
                  )
                  assert y == expec
  
                  expec = _eval_single_bin(1, op, x + 1, self.engine)
                  y = self.eval(
                      ex3, local_dict={"x": x}, engine=self.engine, parser=self.parser
                  )
                  assert y == expec
  
      def test_simple_bool_ops(self):
          for op, lhs, rhs in product(expr.BOOL_OPS_SYMS, (True, False), (True, False)):
              ex = f"{lhs} {op} {rhs}"
              res = self.eval(ex)
              exp = eval(ex)
              assert res == exp
  
      def test_bool_ops_with_constants(self):
          for op, lhs, rhs in product(
              expr.BOOL_OPS_SYMS, ("True", "False"), ("True", "False")
          ):
              ex = f"{lhs} {op} {rhs}"
              res = self.eval(ex)
              exp = eval(ex)
              assert res == exp
  
      def test_4d_ndarray_fails(self):
          x = randn(3, 4, 5, 6)
          y = Series(randn(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(randn(10, 2))
          df2 = self.eval("df", local_dict={"df": df})
          tm.assert_frame_equal(df, df2)
  
      def test_truediv(self):
          s = np.array([1])
          ex = "s / 1"
          d = {"s": s}  # noqa
  
          # FutureWarning: The `truediv` parameter in pd.eval is deprecated and will be
          # removed in a future version.
          with tm.assert_produces_warning(FutureWarning):
              res = self.eval(ex, truediv=False)
          tm.assert_numpy_array_equal(res, np.array([1.0]))
  
          with tm.assert_produces_warning(FutureWarning):
              res = self.eval(ex, truediv=True)
          tm.assert_numpy_array_equal(res, np.array([1.0]))
  
          with tm.assert_produces_warning(FutureWarning):
              res = self.eval("1 / 2", truediv=True)
          expec = 0.5
          assert res == expec
  
          with tm.assert_produces_warning(FutureWarning):
              res = self.eval("1 / 2", truediv=False)
          expec = 0.5
          assert res == expec
  
          with tm.assert_produces_warning(FutureWarning):
              res = self.eval("s / 2", truediv=False)
          expec = 0.5
          assert res == expec
  
          with tm.assert_produces_warning(FutureWarning):
              res = self.eval("s / 2", truediv=True)
          expec = 0.5
          assert res == expec
  
      def test_failing_subscript_with_name_error(self):
          df = DataFrame(np.random.randn(5, 3))  # noqa
          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.randn(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.randn(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.randn(5, 3), columns=list("abc"))
          df2 = DataFrame(np.random.randn(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(self):
          df = DataFrame(np.random.randn(5, 2), columns=list("ab"))
          orig_df = df.copy()
  
          # multiple assignees
          with pytest.raises(SyntaxError, match="invalid syntax"):
              df.eval("d c = a + b")
  
          # 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")
          if compat.PY38:
              msg = "cannot assign to function call"
          else:
              msg = "can't assign to function call"
          with pytest.raises(SyntaxError, match=msg):
              df.eval('Timestamp("20131001") = a + b')
  
          # single assignment - existing variable
          expected = orig_df.copy()
          expected["a"] = expected["a"] + expected["b"]
          df = orig_df.copy()
          df.eval("a = a + b", inplace=True)
          tm.assert_frame_equal(df, expected)
  
          # single assignment - new variable
          expected = orig_df.copy()
          expected["c"] = expected["a"] + expected["b"]
          df = orig_df.copy()
          df.eval("c = a + b", inplace=True)
          tm.assert_frame_equal(df, expected)
  
          # with a local name overlap
          def f():
              df = orig_df.copy()
              a = 1  # noqa
              df.eval("a = 1 + b", inplace=True)
              return df
  
          df = f()
          expected = orig_df.copy()
          expected["a"] = 1 + expected["b"]
          tm.assert_frame_equal(df, expected)
  
          df = orig_df.copy()
  
          def f():
              a = 1  # noqa
              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
  
          f()
  
          # multiple assignment
          df = orig_df.copy()
          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")
  
          # explicit targets
          df = orig_df.copy()
          self.eval("c = df.a + df.b", local_dict={"df": df}, target=df, inplace=True)
          expected = orig_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])
          tm.assert_series_equal(result, expected)
  
      def assignment_not_inplace(self):
          # see gh-9297
          df = DataFrame(np.random.randn(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):
          # 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"]
          ans = df.eval(
              """
          c = a + b
          d = c + b""",
              inplace=True,
          )
          tm.assert_frame_equal(expected, df)
          assert ans is None
  
          expected["a"] = expected["a"] - 1
          expected["e"] = expected["a"] + 2
          ans = df.eval(
              """
          a = a - 1
          e = a + 2""",
              inplace=True,
          )
          tm.assert_frame_equal(expected, df)
          assert ans 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
          ans = df.eval(
              """
          c = a * @local_var
          d = c + @local_var
          """,
              inplace=True,
          )
          tm.assert_frame_equal(expected, df)
          assert ans 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)
          ans = df.eval(
              """
          c = a * @local_func(1, 7)
          d = c + @local_func(1, 7)
          """,
              inplace=True,
          )
          tm.assert_frame_equal(expected, df)
          assert ans 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)
          ans = 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 ans 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)])
      @pytest.mark.filterwarnings("ignore::FutureWarning")
      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 = tm.makeCustomDataframe(2, 2, data_gen_f=f, c_idx_type="p", r_idx_type="i")
  
          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 = tm.makeCustomDataframe(2, 2, data_gen_f=f, c_idx_type="p", r_idx_type="i")
          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 = tm.makeCustomDataframe(2, 2, data_gen_f=f, c_idx_type="p", r_idx_type="i")
          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):
          df = DataFrame(randn(5, 3))
          df["dates1"] = date_range("1/1/2012", periods=5)
          res = self.eval(
              "df.dates1 < 20130101",
              local_dict={"df": df},
              engine=self.engine,
              parser=self.parser,
          )
          expec = df.dates1 < "20130101"
          tm.assert_series_equal(res, expec, check_names=False)
  
      def test_simple_in_ops(self):
          if self.parser != "python":
              res = pd.eval("1 in [1, 2]", engine=self.engine, parser=self.parser)
              assert res
  
              res = pd.eval("2 in (1, 2)", engine=self.engine, parser=self.parser)
              assert res
  
              res = pd.eval("3 in (1, 2)", engine=self.engine, parser=self.parser)
              assert not res
  
              res = pd.eval("3 not in (1, 2)", engine=self.engine, parser=self.parser)
              assert res
  
              res = pd.eval("[3] not in (1, 2)", engine=self.engine, parser=self.parser)
              assert res
  
              res = pd.eval("[3] in ([3], 2)", engine=self.engine, parser=self.parser)
              assert res
  
              res = pd.eval("[[3]] in [[[3]], 2]", engine=self.engine, parser=self.parser)
              assert res
  
              res = pd.eval("(3,) in [(3,), 2]", engine=self.engine, parser=self.parser)
              assert res
  
              res = pd.eval(
                  "(3,) not in [(3,), 2]", engine=self.engine, parser=self.parser
              )
              assert not res
  
              res = pd.eval(
                  "[(3,)] in [[(3,)], 2]", engine=self.engine, parser=self.parser
              )
              assert res
          else:
              msg = "'In' nodes are not implemented"
              with pytest.raises(NotImplementedError, match=msg):
                  pd.eval("1 in [1, 2]", engine=self.engine, parser=self.parser)
              with pytest.raises(NotImplementedError, match=msg):
                  pd.eval("2 in (1, 2)", engine=self.engine, parser=self.parser)
              with pytest.raises(NotImplementedError, match=msg):
                  pd.eval("3 in (1, 2)", engine=self.engine, parser=self.parser)
              with pytest.raises(NotImplementedError, match=msg):
                  pd.eval(
                      "[(3,)] in (1, 2, [(3,)])", engine=self.engine, parser=self.parser
                  )
              msg = "'NotIn' nodes are not implemented"
              with pytest.raises(NotImplementedError, match=msg):
                  pd.eval("3 not in (1, 2)", engine=self.engine, parser=self.parser)
              with pytest.raises(NotImplementedError, match=msg):
                  pd.eval(
                      "[3] not in (1, 2, [[3]])", engine=self.engine, parser=self.parser
                  )
  
  
  @td.skip_if_no_ne
  class TestOperationsNumExprPython(TestOperationsNumExprPandas):
      @classmethod
      def setup_class(cls):
          super().setup_class()
          cls.engine = "numexpr"
          cls.parser = "python"
          cls.arith_ops = [
              op
              for op in expr.ARITH_OPS_SYMS + expr.CMP_OPS_SYMS
              if op not in ("in", "not in")
          ]
  
      def test_check_many_exprs(self):
          a = 1  # noqa
          expr = " * ".join("a" * 33)
          expected = 1
          res = pd.eval(expr, engine=self.engine, parser=self.parser)
          assert res == expected
  
      def test_fails_and(self):
          df = DataFrame(np.random.randn(5, 3))
          msg = "'BoolOp' nodes are not implemented"
          with pytest.raises(NotImplementedError, match=msg):
              pd.eval(
                  "df > 2 and df > 3",
                  local_dict={"df": df},
                  parser=self.parser,
                  engine=self.engine,
              )
  
      def test_fails_or(self):
          df = DataFrame(np.random.randn(5, 3))
          msg = "'BoolOp' nodes are not implemented"
          with pytest.raises(NotImplementedError, match=msg):
              pd.eval(
                  "df > 2 or df > 3",
                  local_dict={"df": df},
                  parser=self.parser,
                  engine=self.engine,
              )
  
      def test_fails_not(self):
          df = DataFrame(np.random.randn(5, 3))
          msg = "'Not' nodes are not implemented"
          with pytest.raises(NotImplementedError, match=msg):
              pd.eval(
                  "not df > 2",
                  local_dict={"df": df},
                  parser=self.parser,
                  engine=self.engine,
              )
  
      def test_fails_ampersand(self):
          df = DataFrame(np.random.randn(5, 3))  # noqa
          ex = "(df + 2)[df > 1] > 0 & (df > 0)"
          msg = "cannot evaluate scalar only bool ops"
          with pytest.raises(NotImplementedError, match=msg):
              pd.eval(ex, parser=self.parser, engine=self.engine)
  
      def test_fails_pipe(self):
          df = DataFrame(np.random.randn(5, 3))  # noqa
          ex = "(df + 2)[df > 1] > 0 | (df > 0)"
          msg = "cannot evaluate scalar only bool ops"
          with pytest.raises(NotImplementedError, match=msg):
              pd.eval(ex, parser=self.parser, engine=self.engine)
  
      def test_bool_ops_with_constants(self):
          for op, lhs, rhs in product(
              expr.BOOL_OPS_SYMS, ("True", "False"), ("True", "False")
          ):
              ex = f"{lhs} {op} {rhs}"
              if op in ("and", "or"):
                  msg = "'BoolOp' nodes are not implemented"
                  with pytest.raises(NotImplementedError, match=msg):
                      self.eval(ex)
              else:
                  res = self.eval(ex)
                  exp = eval(ex)
                  assert res == exp
  
      def test_simple_bool_ops(self):
          for op, lhs, rhs in product(expr.BOOL_OPS_SYMS, (True, False), (True, False)):
              ex = f"lhs {op} rhs"
              if op in ("and", "or"):
                  msg = "'BoolOp' nodes are not implemented"
                  with pytest.raises(NotImplementedError, match=msg):
                      pd.eval(ex, engine=self.engine, parser=self.parser)
              else:
                  res = pd.eval(ex, engine=self.engine, parser=self.parser)
                  exp = eval(ex)
                  assert res == exp
  
  
  class TestOperationsPythonPython(TestOperationsNumExprPython):
      @classmethod
      def setup_class(cls):
          super().setup_class()
          cls.engine = cls.parser = "python"
          cls.arith_ops = [
              op
              for op in expr.ARITH_OPS_SYMS + expr.CMP_OPS_SYMS
              if op not in ("in", "not in")
          ]
  
  
  class TestOperationsPythonPandas(TestOperationsNumExprPandas):
      @classmethod
      def setup_class(cls):
          super().setup_class()
          cls.engine = "python"
          cls.parser = "pandas"
          cls.arith_ops = expr.ARITH_OPS_SYMS + expr.CMP_OPS_SYMS
  
  
  @td.skip_if_no_ne
  class TestMathPythonPython:
      @classmethod
      def setup_class(cls):
          cls.engine = "python"
          cls.parser = "pandas"
          cls.unary_fns = _unary_math_ops
          cls.binary_fns = _binary_math_ops
  
      @classmethod
      def teardown_class(cls):
          del cls.engine, cls.parser
  
      def eval(self, *args, **kwargs):
          kwargs["engine"] = self.engine
          kwargs["parser"] = self.parser
          kwargs["level"] = kwargs.pop("level", 0) + 1
          return pd.eval(*args, **kwargs)
  
      def test_unary_functions(self, unary_fns_for_ne):
          df = DataFrame({"a": np.random.randn(10)})
          a = df.a
  
          for fn in unary_fns_for_ne:
              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)
  
      def test_floor_and_ceil_functions_raise_error(self, ne_lt_2_6_9, unary_fns_for_ne):
          for fn in ("floor", "ceil"):
              msg = f'"{fn}" is not a supported function'
              with pytest.raises(ValueError, match=msg):
                  expr = f"{fn}(100)"
                  self.eval(expr)
  
      def test_binary_functions(self):
          df = DataFrame({"a": np.random.randn(10), "b": np.random.randn(10)})
          a = df.a
          b = df.b
          for fn in self.binary_fns:
              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):
          df = DataFrame({"a": np.random.randn(10), "b": np.random.randn(10)})
          df.eval(
              "e = arctan2(sin(a), b)",
              engine=self.engine,
              parser=self.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):
          df = DataFrame({"a": np.random.randn(10), "b": np.random.randn(10)})
          df.eval("e = sin(a + b)", engine=self.engine, parser=self.parser, inplace=True)
          got = df.e
          expect = np.sin(df.a + df.b)
          tm.assert_series_equal(got, expect, check_names=False)
  
      def check_result_type(self, dtype, expect_dtype):
          df = DataFrame({"a": np.random.randn(10).astype(dtype)})
          assert df.a.dtype == dtype
          df.eval("b = sin(a)", engine=self.engine, parser=self.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_result_types(self):
          self.check_result_type(np.int32, np.float64)
          self.check_result_type(np.int64, np.float64)
          self.check_result_type(np.float32, np.float32)
          self.check_result_type(np.float64, np.float64)
  
      @td.skip_if_windows
      def test_result_complex128(self):
          # 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
          self.check_result_type(np.complex128, np.complex128)
  
      def test_undefined_func(self):
          df = DataFrame({"a": np.random.randn(10)})
          msg = '"mysin" is not a supported function'
  
          with pytest.raises(ValueError, match=msg):
              df.eval("mysin(a)", engine=self.engine, parser=self.parser)
  
      def test_keyword_arg(self):
          df = DataFrame({"a": np.random.randn(10)})
          msg = 'Function "sin" does not support keyword arguments'
  
          with pytest.raises(TypeError, match=msg):
              df.eval("sin(x=a)", engine=self.engine, parser=self.parser)
  
  
  class TestMathPythonPandas(TestMathPythonPython):
      @classmethod
      def setup_class(cls):
          super().setup_class()
          cls.engine = "python"
          cls.parser = "pandas"
  
  
  class TestMathNumExprPandas(TestMathPythonPython):
      @classmethod
      def setup_class(cls):
          super().setup_class()
          cls.engine = "numexpr"
          cls.parser = "pandas"
  
  
  class TestMathNumExprPython(TestMathPythonPython):
      @classmethod
      def setup_class(cls):
          super().setup_class()
          cls.engine = "numexpr"
          cls.parser = "python"
  
  
  _var_s = randn(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
          gbls = globals().copy()
          pd.eval("x + 1", engine=engine, parser=parser)
          gbls2 = globals().copy()
          assert gbls == gbls2
  
  
  @td.skip_if_no_ne
  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_ne
  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]
      uns_ops = VisitorClass.unsupported_nodes
      inst = VisitorClass("x + 1", engine, parser)
  
      for ops in uns_ops:
          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)
  
  
  def test_invalid_local_variable_reference(engine, parser):
      a, b = 1, 2  # noqa
      exprs = "a + @b", "@a + b", "@a + @b"
  
      for _expr in exprs:
          if parser != "pandas":
              with pytest.raises(SyntaxError, match="The '@' prefix is only"):
                  pd.eval(_expr, engine=engine, parser=parser)
          else:
              with pytest.raises(SyntaxError, match="The '@' prefix is not"):
                  pd.eval(_expr, 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.randint(10), float: np.random.randn}
  
      mid = gen[lhs]()  # noqa
      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'",
          pytest.param(
              "...", marks=pytest.mark.xfail(not compat.PY38, reason="GH-28116")
          ),
      ],
  )
  def test_equals_various(other):
      df = DataFrame({"A": ["a", "b", "c"]})
      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
  
  
  def test_truediv_deprecated(engine, parser):
      # GH#29182
      match = "The `truediv` parameter in pd.eval is deprecated"
  
      with tm.assert_produces_warning(FutureWarning) as m:
          pd.eval("1+1", engine=engine, parser=parser, truediv=True)
  
      assert len(m) == 1
      assert match in str(m[0].message)
  
      with tm.assert_produces_warning(FutureWarning) as m:
          pd.eval("1+1", engine=engine, parser=parser, truediv=False)
  
      assert len(m) == 1
      assert match in str(m[0].message)
  
  
  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)
  
  
  class TestValidate:
      def test_validate_bool_args(self):
          invalid_values = [1, "True", [1, 2, 3], 5.0]
  
          for value in invalid_values:
              msg = 'For argument "inplace" expected type bool, received type'
              with pytest.raises(ValueError, match=msg):
                  pd.eval("2+2", inplace=value)
  