mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-09-20 06:17:29 +02:00
2073 lines
72 KiB
Python
2073 lines
72 KiB
Python
from distutils.version import LooseVersion
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from functools import reduce
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from itertools import product
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import operator
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from typing import Dict, Type
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import warnings
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import numpy as np
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from numpy.random import rand, randint, randn
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import pytest
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from pandas.errors import PerformanceWarning
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import pandas.util._test_decorators as td
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from pandas.core.dtypes.common import is_bool, is_list_like, is_scalar
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import pandas as pd
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from pandas import DataFrame, Series, compat, date_range
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import pandas._testing as tm
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from pandas.core.computation import pytables
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from pandas.core.computation.check import _NUMEXPR_VERSION
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from pandas.core.computation.engines import NumExprClobberingError, _engines
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import pandas.core.computation.expr as expr
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from pandas.core.computation.expr import (
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BaseExprVisitor,
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PandasExprVisitor,
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PythonExprVisitor,
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)
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from pandas.core.computation.expressions import _NUMEXPR_INSTALLED, _USE_NUMEXPR
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from pandas.core.computation.ops import (
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_arith_ops_syms,
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_binary_math_ops,
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_binary_ops_dict,
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_special_case_arith_ops_syms,
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_unary_math_ops,
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)
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@pytest.fixture(
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params=(
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pytest.param(
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engine,
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marks=pytest.mark.skipif(
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engine == "numexpr" and not _USE_NUMEXPR,
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reason=f"numexpr enabled->{_USE_NUMEXPR}, "
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f"installed->{_NUMEXPR_INSTALLED}",
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),
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)
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for engine in _engines
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)
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) # noqa
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def engine(request):
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return request.param
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@pytest.fixture(params=expr._parsers)
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def parser(request):
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return request.param
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@pytest.fixture
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def ne_lt_2_6_9():
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if _NUMEXPR_INSTALLED and _NUMEXPR_VERSION >= LooseVersion("2.6.9"):
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pytest.skip("numexpr is >= 2.6.9")
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return "numexpr"
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@pytest.fixture
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def unary_fns_for_ne():
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if _NUMEXPR_INSTALLED:
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if _NUMEXPR_VERSION >= LooseVersion("2.6.9"):
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return _unary_math_ops
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else:
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return tuple(x for x in _unary_math_ops if x not in ("floor", "ceil"))
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else:
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pytest.skip("numexpr is not present")
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def engine_has_neg_frac(engine):
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return _engines[engine].has_neg_frac
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def _eval_single_bin(lhs, cmp1, rhs, engine):
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c = _binary_ops_dict[cmp1]
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if engine_has_neg_frac(engine):
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try:
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return c(lhs, rhs)
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except ValueError as e:
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if str(e).startswith(
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"negative number cannot be raised to a fractional power"
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):
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return np.nan
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raise
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return c(lhs, rhs)
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def _series_and_2d_ndarray(lhs, rhs):
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return (
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isinstance(lhs, Series) and isinstance(rhs, np.ndarray) and rhs.ndim > 1
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) or (isinstance(rhs, Series) and isinstance(lhs, np.ndarray) and lhs.ndim > 1)
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def _series_and_frame(lhs, rhs):
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return (isinstance(lhs, Series) and isinstance(rhs, DataFrame)) or (
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isinstance(rhs, Series) and isinstance(lhs, DataFrame)
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)
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def _bool_and_frame(lhs, rhs):
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return isinstance(lhs, bool) and isinstance(rhs, pd.core.generic.NDFrame)
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def _is_py3_complex_incompat(result, expected):
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return isinstance(expected, (complex, np.complexfloating)) and np.isnan(result)
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_good_arith_ops = set(_arith_ops_syms).difference(_special_case_arith_ops_syms)
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@td.skip_if_no_ne
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class TestEvalNumexprPandas:
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@classmethod
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def setup_class(cls):
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import numexpr as ne
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cls.ne = ne
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cls.engine = "numexpr"
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cls.parser = "pandas"
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@classmethod
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def teardown_class(cls):
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del cls.engine, cls.parser
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if hasattr(cls, "ne"):
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del cls.ne
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def setup_data(self):
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nan_df1 = DataFrame(rand(10, 5))
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nan_df1[nan_df1 > 0.5] = np.nan
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nan_df2 = DataFrame(rand(10, 5))
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nan_df2[nan_df2 > 0.5] = np.nan
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self.pandas_lhses = (
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DataFrame(randn(10, 5)),
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Series(randn(5)),
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Series([1, 2, np.nan, np.nan, 5]),
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nan_df1,
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)
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self.pandas_rhses = (
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DataFrame(randn(10, 5)),
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Series(randn(5)),
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Series([1, 2, np.nan, np.nan, 5]),
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nan_df2,
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)
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self.scalar_lhses = (randn(),)
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self.scalar_rhses = (randn(),)
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self.lhses = self.pandas_lhses + self.scalar_lhses
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self.rhses = self.pandas_rhses + self.scalar_rhses
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def setup_ops(self):
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self.cmp_ops = expr._cmp_ops_syms
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self.cmp2_ops = self.cmp_ops[::-1]
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self.bin_ops = expr._bool_ops_syms
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self.special_case_ops = _special_case_arith_ops_syms
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self.arith_ops = _good_arith_ops
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self.unary_ops = "-", "~", "not "
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def setup_method(self, method):
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self.setup_ops()
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self.setup_data()
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self.current_engines = filter(lambda x: x != self.engine, _engines)
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def teardown_method(self, method):
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del self.lhses, self.rhses, self.scalar_rhses, self.scalar_lhses
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del self.pandas_rhses, self.pandas_lhses, self.current_engines
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@pytest.mark.slow
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@pytest.mark.parametrize(
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"cmp1",
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["!=", "==", "<=", ">=", "<", ">"],
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ids=["ne", "eq", "le", "ge", "lt", "gt"],
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)
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@pytest.mark.parametrize("cmp2", [">", "<"], ids=["gt", "lt"])
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def test_complex_cmp_ops(self, cmp1, cmp2):
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for lhs, rhs, binop in product(self.lhses, self.rhses, self.bin_ops):
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lhs_new = _eval_single_bin(lhs, cmp1, rhs, self.engine)
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rhs_new = _eval_single_bin(lhs, cmp2, rhs, self.engine)
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expected = _eval_single_bin(lhs_new, binop, rhs_new, self.engine)
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ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
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result = pd.eval(ex, engine=self.engine, parser=self.parser)
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self.check_equal(result, expected)
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def test_simple_cmp_ops(self):
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bool_lhses = (
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DataFrame(tm.randbool(size=(10, 5))),
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Series(tm.randbool((5,))),
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tm.randbool(),
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)
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bool_rhses = (
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DataFrame(tm.randbool(size=(10, 5))),
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Series(tm.randbool((5,))),
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tm.randbool(),
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)
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for lhs, rhs, cmp_op in product(bool_lhses, bool_rhses, self.cmp_ops):
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self.check_simple_cmp_op(lhs, cmp_op, rhs)
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@pytest.mark.slow
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def test_binary_arith_ops(self):
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for lhs, op, rhs in product(self.lhses, self.arith_ops, self.rhses):
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self.check_binary_arith_op(lhs, op, rhs)
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def test_modulus(self):
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for lhs, rhs in product(self.lhses, self.rhses):
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self.check_modulus(lhs, "%", rhs)
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def test_floor_division(self):
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for lhs, rhs in product(self.lhses, self.rhses):
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self.check_floor_division(lhs, "//", rhs)
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@td.skip_if_windows
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def test_pow(self):
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# odd failure on win32 platform, so skip
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for lhs, rhs in product(self.lhses, self.rhses):
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self.check_pow(lhs, "**", rhs)
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@pytest.mark.slow
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def test_single_invert_op(self):
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for lhs, op, rhs in product(self.lhses, self.cmp_ops, self.rhses):
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self.check_single_invert_op(lhs, op, rhs)
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@pytest.mark.slow
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def test_compound_invert_op(self):
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for lhs, op, rhs in product(self.lhses, self.cmp_ops, self.rhses):
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self.check_compound_invert_op(lhs, op, rhs)
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@pytest.mark.slow
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def test_chained_cmp_op(self):
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mids = self.lhses
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cmp_ops = "<", ">"
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for lhs, cmp1, mid, cmp2, rhs in product(
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self.lhses, cmp_ops, mids, cmp_ops, self.rhses
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):
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self.check_chained_cmp_op(lhs, cmp1, mid, cmp2, rhs)
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def check_equal(self, result, expected):
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if isinstance(result, DataFrame):
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tm.assert_frame_equal(result, expected)
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elif isinstance(result, Series):
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tm.assert_series_equal(result, expected)
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elif isinstance(result, np.ndarray):
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tm.assert_numpy_array_equal(result, expected)
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else:
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assert result == expected
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def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs):
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def check_operands(left, right, cmp_op):
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return _eval_single_bin(left, cmp_op, right, self.engine)
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lhs_new = check_operands(lhs, mid, cmp1)
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rhs_new = check_operands(mid, rhs, cmp2)
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if lhs_new is not None and rhs_new is not None:
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ex1 = f"lhs {cmp1} mid {cmp2} rhs"
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ex2 = f"lhs {cmp1} mid and mid {cmp2} rhs"
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ex3 = f"(lhs {cmp1} mid) & (mid {cmp2} rhs)"
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expected = _eval_single_bin(lhs_new, "&", rhs_new, self.engine)
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for ex in (ex1, ex2, ex3):
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result = pd.eval(ex, engine=self.engine, parser=self.parser)
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tm.assert_almost_equal(result, expected)
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def check_simple_cmp_op(self, lhs, cmp1, rhs):
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ex = f"lhs {cmp1} rhs"
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msg = (
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r"only list-like( or dict-like)? objects are allowed to be "
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r"passed to (DataFrame\.)?isin\(\), you passed a "
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r"(\[|')bool(\]|')|"
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"argument of type 'bool' is not iterable"
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)
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if cmp1 in ("in", "not in") and not is_list_like(rhs):
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with pytest.raises(TypeError, match=msg):
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pd.eval(
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ex,
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engine=self.engine,
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parser=self.parser,
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local_dict={"lhs": lhs, "rhs": rhs},
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)
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else:
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expected = _eval_single_bin(lhs, cmp1, rhs, self.engine)
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result = pd.eval(ex, engine=self.engine, parser=self.parser)
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self.check_equal(result, expected)
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def check_binary_arith_op(self, lhs, arith1, rhs):
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ex = f"lhs {arith1} rhs"
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result = pd.eval(ex, engine=self.engine, parser=self.parser)
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expected = _eval_single_bin(lhs, arith1, rhs, self.engine)
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tm.assert_almost_equal(result, expected)
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ex = f"lhs {arith1} rhs {arith1} rhs"
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result = pd.eval(ex, engine=self.engine, parser=self.parser)
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nlhs = _eval_single_bin(lhs, arith1, rhs, self.engine)
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self.check_alignment(result, nlhs, rhs, arith1)
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def check_alignment(self, result, nlhs, ghs, op):
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try:
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nlhs, ghs = nlhs.align(ghs)
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except (ValueError, TypeError, AttributeError):
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# ValueError: series frame or frame series align
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# TypeError, AttributeError: series or frame with scalar align
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pass
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else:
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# direct numpy comparison
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expected = self.ne.evaluate(f"nlhs {op} ghs")
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tm.assert_numpy_array_equal(result.values, expected)
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# modulus, pow, and floor division require special casing
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def check_modulus(self, lhs, arith1, rhs):
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ex = f"lhs {arith1} rhs"
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result = pd.eval(ex, engine=self.engine, parser=self.parser)
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expected = lhs % rhs
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tm.assert_almost_equal(result, expected)
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expected = self.ne.evaluate(f"expected {arith1} rhs")
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if isinstance(result, (DataFrame, Series)):
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tm.assert_almost_equal(result.values, expected)
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else:
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tm.assert_almost_equal(result, expected.item())
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def check_floor_division(self, lhs, arith1, rhs):
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ex = f"lhs {arith1} rhs"
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if self.engine == "python":
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res = pd.eval(ex, engine=self.engine, parser=self.parser)
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expected = lhs // rhs
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self.check_equal(res, expected)
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else:
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msg = (
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r"unsupported operand type\(s\) for //: 'VariableNode' and "
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"'VariableNode'"
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)
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with pytest.raises(TypeError, match=msg):
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pd.eval(
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ex,
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local_dict={"lhs": lhs, "rhs": rhs},
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engine=self.engine,
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parser=self.parser,
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)
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def get_expected_pow_result(self, lhs, rhs):
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try:
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expected = _eval_single_bin(lhs, "**", rhs, self.engine)
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except ValueError as e:
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if str(e).startswith(
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"negative number cannot be raised to a fractional power"
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):
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if self.engine == "python":
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pytest.skip(str(e))
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else:
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expected = np.nan
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else:
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raise
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return expected
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def check_pow(self, lhs, arith1, rhs):
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ex = f"lhs {arith1} rhs"
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expected = self.get_expected_pow_result(lhs, rhs)
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result = pd.eval(ex, engine=self.engine, parser=self.parser)
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if (
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is_scalar(lhs)
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and is_scalar(rhs)
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and _is_py3_complex_incompat(result, expected)
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):
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msg = "(DataFrame.columns|numpy array) are different"
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with pytest.raises(AssertionError, match=msg):
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tm.assert_numpy_array_equal(result, expected)
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else:
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tm.assert_almost_equal(result, expected)
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ex = f"(lhs {arith1} rhs) {arith1} rhs"
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result = pd.eval(ex, engine=self.engine, parser=self.parser)
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expected = self.get_expected_pow_result(
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self.get_expected_pow_result(lhs, rhs), rhs
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)
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tm.assert_almost_equal(result, expected)
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def check_single_invert_op(self, lhs, cmp1, rhs):
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# simple
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for el in (lhs, rhs):
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try:
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elb = el.astype(bool)
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except AttributeError:
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elb = np.array([bool(el)])
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expected = ~elb
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result = pd.eval("~elb", engine=self.engine, parser=self.parser)
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tm.assert_almost_equal(expected, result)
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for engine in self.current_engines:
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tm.assert_almost_equal(
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result, pd.eval("~elb", engine=engine, parser=self.parser)
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)
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|
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def check_compound_invert_op(self, lhs, cmp1, rhs):
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skip_these = ["in", "not in"]
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ex = f"~(lhs {cmp1} rhs)"
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|
|
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msg = (
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r"only list-like( or dict-like)? objects are allowed to be "
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|
r"passed to (DataFrame\.)?isin\(\), you passed a "
|
|
r"(\[|')float(\]|')|"
|
|
"argument of type 'float' is not iterable"
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)
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if is_scalar(rhs) and cmp1 in skip_these:
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with pytest.raises(TypeError, match=msg):
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pd.eval(
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ex,
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engine=self.engine,
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parser=self.parser,
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local_dict={"lhs": lhs, "rhs": rhs},
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)
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else:
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# compound
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if is_scalar(lhs) and is_scalar(rhs):
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lhs, rhs = map(lambda x: np.array([x]), (lhs, rhs))
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expected = _eval_single_bin(lhs, cmp1, rhs, self.engine)
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if is_scalar(expected):
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expected = not expected
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else:
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expected = ~expected
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result = pd.eval(ex, engine=self.engine, parser=self.parser)
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tm.assert_almost_equal(expected, result)
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|
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# make sure the other engines work the same as this one
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for engine in self.current_engines:
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ev = pd.eval(ex, engine=self.engine, parser=self.parser)
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tm.assert_almost_equal(ev, result)
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def ex(self, op, var_name="lhs"):
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return f"{op}{var_name}"
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|
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def test_frame_invert(self):
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expr = self.ex("~")
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|
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# ~ ##
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# frame
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# float always raises
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lhs = DataFrame(randn(5, 2))
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if self.engine == "numexpr":
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msg = "couldn't find matching opcode for 'invert_dd'"
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with pytest.raises(NotImplementedError, match=msg):
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result = pd.eval(expr, engine=self.engine, parser=self.parser)
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|
else:
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msg = "ufunc 'invert' not supported for the input types"
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with pytest.raises(TypeError, match=msg):
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result = pd.eval(expr, engine=self.engine, parser=self.parser)
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|
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# int raises on numexpr
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lhs = DataFrame(randint(5, size=(5, 2)))
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if self.engine == "numexpr":
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msg = "couldn't find matching opcode for 'invert"
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with pytest.raises(NotImplementedError, match=msg):
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result = pd.eval(expr, engine=self.engine, parser=self.parser)
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else:
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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 = pd.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 = pd.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 = pd.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 = pd.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 = list(
|
|
filter(lambda x: x not in ("in", "not in"), expr._cmp_ops_syms)
|
|
)
|
|
self.cmp2_ops = self.cmp_ops[::-1]
|
|
self.bin_ops = [s for s in expr._bool_ops_syms if s 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)
|
|
s = Series(np.random.randn(n), index[:n])
|
|
|
|
if r2 == "dt" or c2 == "dt":
|
|
if engine == "numexpr":
|
|
expected2 = df2.add(s)
|
|
else:
|
|
expected2 = df2 + s
|
|
else:
|
|
expected2 = df2 + s
|
|
|
|
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, s.index, df.index):
|
|
with tm.assert_produces_warning(RuntimeWarning):
|
|
res = pd.eval("df2 + s + df", engine=engine, parser=parser)
|
|
else:
|
|
res = pd.eval("df2 + s + 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 = self.arith_ops
|
|
|
|
for op in filter(lambda x: x != "//", 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 = pd.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 = pd.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 = pd.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 = pd.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 = pd.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 = pd.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 = pd.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 = expr._arith_ops_syms + expr._cmp_ops_syms
|
|
cls.arith_ops = filter(lambda x: x not in ("in", "not in"), cls.arith_ops)
|
|
|
|
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 = expr._arith_ops_syms + expr._cmp_ops_syms
|
|
cls.arith_ops = filter(lambda x: x not in ("in", "not in"), cls.arith_ops)
|
|
|
|
|
|
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"
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|
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 # noqa
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|
lcls = locals().copy()
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|
pd.eval("x + 1", local_dict=lcls, engine=engine, parser=parser)
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|
lcls2 = locals().copy()
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|
lcls2.pop("lcls")
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|
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)
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|
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]() # noqa
|
|
rhs = gen[rhs]() # noqa
|
|
|
|
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 = pd.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)
|