1992 lines
70 KiB
Python
1992 lines
70 KiB
Python
from __future__ import annotations
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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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import numpy as np
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import pytest
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from pandas.compat import PY312
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from pandas.errors import (
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NumExprClobberingError,
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PerformanceWarning,
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UndefinedVariableError,
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)
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import pandas.util._test_decorators as td
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from pandas.core.dtypes.common import (
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is_bool,
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is_float,
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is_list_like,
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is_scalar,
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)
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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Series,
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date_range,
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period_range,
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timedelta_range,
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)
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import pandas._testing as tm
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from pandas.core.computation import (
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expr,
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pytables,
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)
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from pandas.core.computation.engines import ENGINES
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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 (
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NUMEXPR_INSTALLED,
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USE_NUMEXPR,
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)
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from pandas.core.computation.ops import (
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ARITH_OPS_SYMS,
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SPECIAL_CASE_ARITH_OPS_SYMS,
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_binary_math_ops,
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_binary_ops_dict,
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_unary_math_ops,
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)
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from pandas.core.computation.scope import DEFAULT_GLOBALS
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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=[
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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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td.skip_if_no("numexpr"),
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],
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)
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for engine in ENGINES
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)
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)
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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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def _eval_single_bin(lhs, cmp1, rhs, engine):
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c = _binary_ops_dict[cmp1]
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if ENGINES[engine].has_neg_frac:
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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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# TODO: using range(5) here is a kludge
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@pytest.fixture(
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params=list(range(5)),
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ids=["DataFrame", "Series", "SeriesNaN", "DataFrameNaN", "float"],
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)
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def lhs(request):
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nan_df1 = DataFrame(np.random.default_rng(2).standard_normal((10, 5)))
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nan_df1[nan_df1 > 0.5] = np.nan
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opts = (
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DataFrame(np.random.default_rng(2).standard_normal((10, 5))),
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Series(np.random.default_rng(2).standard_normal(5)),
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Series([1, 2, np.nan, np.nan, 5]),
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nan_df1,
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np.random.default_rng(2).standard_normal(),
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)
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return opts[request.param]
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rhs = lhs
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midhs = lhs
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@pytest.fixture
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def idx_func_dict():
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return {
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"i": lambda n: Index(np.arange(n), dtype=np.int64),
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"f": lambda n: Index(np.arange(n), dtype=np.float64),
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"s": lambda n: Index([f"{i}_{chr(i)}" for i in range(97, 97 + n)]),
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"dt": lambda n: date_range("2020-01-01", periods=n),
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"td": lambda n: timedelta_range("1 day", periods=n),
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"p": lambda n: period_range("2020-01-01", periods=n, freq="D"),
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}
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class TestEval:
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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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@pytest.mark.parametrize("binop", expr.BOOL_OPS_SYMS)
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def test_complex_cmp_ops(self, cmp1, cmp2, binop, lhs, rhs, engine, parser):
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if parser == "python" and binop in ["and", "or"]:
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msg = "'BoolOp' nodes are not implemented"
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with pytest.raises(NotImplementedError, match=msg):
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ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
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pd.eval(ex, engine=engine, parser=parser)
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return
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lhs_new = _eval_single_bin(lhs, cmp1, rhs, engine)
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rhs_new = _eval_single_bin(lhs, cmp2, rhs, engine)
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expected = _eval_single_bin(lhs_new, binop, rhs_new, engine)
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ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)"
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result = pd.eval(ex, engine=engine, parser=parser)
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tm.assert_equal(result, expected)
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@pytest.mark.parametrize("cmp_op", expr.CMP_OPS_SYMS)
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def test_simple_cmp_ops(self, cmp_op, lhs, rhs, engine, parser):
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lhs = lhs < 0
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rhs = rhs < 0
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if parser == "python" and cmp_op in ["in", "not in"]:
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msg = "'(In|NotIn)' nodes are not implemented"
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with pytest.raises(NotImplementedError, match=msg):
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ex = f"lhs {cmp_op} rhs"
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pd.eval(ex, engine=engine, parser=parser)
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return
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ex = f"lhs {cmp_op} rhs"
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msg = "|".join(
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[
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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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)
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if cmp_op 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=engine,
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parser=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, cmp_op, rhs, engine)
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result = pd.eval(ex, engine=engine, parser=parser)
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tm.assert_equal(result, expected)
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@pytest.mark.parametrize("op", expr.CMP_OPS_SYMS)
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def test_compound_invert_op(self, op, lhs, rhs, request, engine, parser):
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if parser == "python" and op in ["in", "not in"]:
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msg = "'(In|NotIn)' nodes are not implemented"
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with pytest.raises(NotImplementedError, match=msg):
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ex = f"~(lhs {op} rhs)"
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pd.eval(ex, engine=engine, parser=parser)
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return
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if (
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is_float(lhs)
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and not is_float(rhs)
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and op in ["in", "not in"]
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and engine == "python"
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and parser == "pandas"
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):
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mark = pytest.mark.xfail(
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reason="Looks like expected is negative, unclear whether "
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"expected is incorrect or result is incorrect"
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)
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request.applymarker(mark)
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skip_these = ["in", "not in"]
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ex = f"~(lhs {op} rhs)"
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msg = "|".join(
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[
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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"(`|')float(`|')",
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"argument of type 'float' is not iterable",
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]
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)
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if is_scalar(rhs) and op 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=engine,
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parser=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 = (np.array([x]) for x in (lhs, rhs))
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expected = _eval_single_bin(lhs, op, rhs, 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=engine, parser=parser)
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tm.assert_almost_equal(expected, result)
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@pytest.mark.parametrize("cmp1", ["<", ">"])
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@pytest.mark.parametrize("cmp2", ["<", ">"])
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def test_chained_cmp_op(self, cmp1, cmp2, lhs, midhs, rhs, engine, parser):
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mid = midhs
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if parser == "python":
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ex1 = f"lhs {cmp1} mid {cmp2} rhs"
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msg = "'BoolOp' nodes are not implemented"
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with pytest.raises(NotImplementedError, match=msg):
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pd.eval(ex1, engine=engine, parser=parser)
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return
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lhs_new = _eval_single_bin(lhs, cmp1, mid, engine)
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rhs_new = _eval_single_bin(mid, cmp2, rhs, engine)
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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, engine)
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for ex in (ex1, ex2, ex3):
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result = pd.eval(ex, engine=engine, parser=parser)
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tm.assert_almost_equal(result, expected)
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@pytest.mark.parametrize(
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"arith1", sorted(set(ARITH_OPS_SYMS).difference(SPECIAL_CASE_ARITH_OPS_SYMS))
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)
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def test_binary_arith_ops(self, arith1, lhs, rhs, engine, parser):
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ex = f"lhs {arith1} rhs"
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result = pd.eval(ex, engine=engine, parser=parser)
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expected = _eval_single_bin(lhs, arith1, rhs, 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=engine, parser=parser)
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nlhs = _eval_single_bin(lhs, arith1, rhs, engine)
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try:
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nlhs, ghs = nlhs.align(rhs)
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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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return
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else:
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if engine == "numexpr":
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import numexpr as ne
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# direct numpy comparison
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expected = ne.evaluate(f"nlhs {arith1} ghs")
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# Update assert statement due to unreliable numerical
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# precision component (GH37328)
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# TODO: update testing code so that assert_almost_equal statement
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# can be replaced again by the assert_numpy_array_equal statement
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tm.assert_almost_equal(result.values, expected)
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else:
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expected = eval(f"nlhs {arith1} ghs")
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tm.assert_almost_equal(result, expected)
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# modulus, pow, and floor division require special casing
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def test_modulus(self, lhs, rhs, engine, parser):
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ex = r"lhs % rhs"
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result = pd.eval(ex, engine=engine, parser=parser)
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expected = lhs % rhs
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tm.assert_almost_equal(result, expected)
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if engine == "numexpr":
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import numexpr as ne
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expected = ne.evaluate(r"expected % 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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else:
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expected = _eval_single_bin(expected, "%", rhs, engine)
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tm.assert_almost_equal(result, expected)
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def test_floor_division(self, lhs, rhs, engine, parser):
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ex = "lhs // rhs"
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if engine == "python":
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res = pd.eval(ex, engine=engine, parser=parser)
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expected = lhs // rhs
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tm.assert_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=engine,
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parser=parser,
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)
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@td.skip_if_windows
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def test_pow(self, lhs, rhs, engine, parser):
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# odd failure on win32 platform, so skip
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ex = "lhs ** rhs"
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expected = _eval_single_bin(lhs, "**", rhs, engine)
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result = pd.eval(ex, engine=engine, parser=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 isinstance(expected, (complex, np.complexfloating))
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and np.isnan(result)
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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 = "(lhs ** rhs) ** rhs"
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result = pd.eval(ex, engine=engine, parser=parser)
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middle = _eval_single_bin(lhs, "**", rhs, engine)
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expected = _eval_single_bin(middle, "**", rhs, engine)
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tm.assert_almost_equal(result, expected)
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def test_check_single_invert_op(self, lhs, engine, parser):
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# simple
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try:
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elb = lhs.astype(bool)
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except AttributeError:
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elb = np.array([bool(lhs)])
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expected = ~elb
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result = pd.eval("~elb", engine=engine, parser=parser)
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tm.assert_almost_equal(expected, result)
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def test_frame_invert(self, engine, parser):
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expr = "~lhs"
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# ~ ##
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# frame
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# float always raises
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lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)))
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if 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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pd.eval(expr, engine=engine, parser=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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pd.eval(expr, engine=engine, parser=parser)
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# int raises on numexpr
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lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2)))
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if 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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pd.eval(expr, engine=engine, parser=parser)
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else:
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expect = ~lhs
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result = pd.eval(expr, engine=engine, parser=parser)
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tm.assert_frame_equal(expect, result)
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# bool always works
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lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5)
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expect = ~lhs
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result = pd.eval(expr, engine=engine, parser=parser)
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tm.assert_frame_equal(expect, result)
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# object raises
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lhs = DataFrame(
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{"b": ["a", 1, 2.0], "c": np.random.default_rng(2).standard_normal(3) > 0.5}
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)
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if engine == "numexpr":
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with pytest.raises(ValueError, match="unknown type object"):
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pd.eval(expr, engine=engine, parser=parser)
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else:
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msg = "bad operand type for unary ~: 'str'"
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with pytest.raises(TypeError, match=msg):
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pd.eval(expr, engine=engine, parser=parser)
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def test_series_invert(self, engine, parser):
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# ~ ####
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expr = "~lhs"
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# series
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# float raises
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lhs = Series(np.random.default_rng(2).standard_normal(5))
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if 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=engine, parser=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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pd.eval(expr, engine=engine, parser=parser)
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# int raises on numexpr
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lhs = Series(np.random.default_rng(2).integers(5, size=5))
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if 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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pd.eval(expr, engine=engine, parser=parser)
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else:
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expect = ~lhs
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result = pd.eval(expr, engine=engine, parser=parser)
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tm.assert_series_equal(expect, result)
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# bool
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lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5)
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expect = ~lhs
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result = pd.eval(expr, engine=engine, parser=parser)
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tm.assert_series_equal(expect, result)
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# float
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# int
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# bool
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# object
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lhs = Series(["a", 1, 2.0])
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if engine == "numexpr":
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with pytest.raises(ValueError, match="unknown type object"):
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pd.eval(expr, engine=engine, parser=parser)
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else:
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msg = "bad operand type for unary ~: 'str'"
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with pytest.raises(TypeError, match=msg):
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pd.eval(expr, engine=engine, parser=parser)
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def test_frame_negate(self, engine, parser):
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expr = "-lhs"
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# float
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lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)))
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expect = -lhs
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result = pd.eval(expr, engine=engine, parser=parser)
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tm.assert_frame_equal(expect, result)
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# int
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lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2)))
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expect = -lhs
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result = pd.eval(expr, engine=engine, parser=parser)
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tm.assert_frame_equal(expect, result)
|
|
|
|
# bool doesn't work with numexpr but works elsewhere
|
|
lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5)
|
|
if engine == "numexpr":
|
|
msg = "couldn't find matching opcode for 'neg_bb'"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval(expr, engine=engine, parser=parser)
|
|
else:
|
|
expect = -lhs
|
|
result = pd.eval(expr, engine=engine, parser=parser)
|
|
tm.assert_frame_equal(expect, result)
|
|
|
|
def test_series_negate(self, engine, parser):
|
|
expr = "-lhs"
|
|
|
|
# float
|
|
lhs = Series(np.random.default_rng(2).standard_normal(5))
|
|
expect = -lhs
|
|
result = pd.eval(expr, engine=engine, parser=parser)
|
|
tm.assert_series_equal(expect, result)
|
|
|
|
# int
|
|
lhs = Series(np.random.default_rng(2).integers(5, size=5))
|
|
expect = -lhs
|
|
result = pd.eval(expr, engine=engine, parser=parser)
|
|
tm.assert_series_equal(expect, result)
|
|
|
|
# bool doesn't work with numexpr but works elsewhere
|
|
lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5)
|
|
if engine == "numexpr":
|
|
msg = "couldn't find matching opcode for 'neg_bb'"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval(expr, engine=engine, parser=parser)
|
|
else:
|
|
expect = -lhs
|
|
result = pd.eval(expr, engine=engine, parser=parser)
|
|
tm.assert_series_equal(expect, result)
|
|
|
|
@pytest.mark.parametrize(
|
|
"lhs",
|
|
[
|
|
# Float
|
|
DataFrame(np.random.default_rng(2).standard_normal((5, 2))),
|
|
# Int
|
|
DataFrame(np.random.default_rng(2).integers(5, size=(5, 2))),
|
|
# bool doesn't work with numexpr but works elsewhere
|
|
DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5),
|
|
],
|
|
)
|
|
def test_frame_pos(self, lhs, engine, parser):
|
|
expr = "+lhs"
|
|
expect = lhs
|
|
|
|
result = pd.eval(expr, engine=engine, parser=parser)
|
|
tm.assert_frame_equal(expect, result)
|
|
|
|
@pytest.mark.parametrize(
|
|
"lhs",
|
|
[
|
|
# Float
|
|
Series(np.random.default_rng(2).standard_normal(5)),
|
|
# Int
|
|
Series(np.random.default_rng(2).integers(5, size=5)),
|
|
# bool doesn't work with numexpr but works elsewhere
|
|
Series(np.random.default_rng(2).standard_normal(5) > 0.5),
|
|
],
|
|
)
|
|
def test_series_pos(self, lhs, engine, parser):
|
|
expr = "+lhs"
|
|
expect = lhs
|
|
|
|
result = pd.eval(expr, engine=engine, parser=parser)
|
|
tm.assert_series_equal(expect, result)
|
|
|
|
def test_scalar_unary(self, engine, parser):
|
|
msg = "bad operand type for unary ~: 'float'"
|
|
warn = None
|
|
if PY312 and not (engine == "numexpr" and parser == "pandas"):
|
|
warn = DeprecationWarning
|
|
with pytest.raises(TypeError, match=msg):
|
|
pd.eval("~1.0", engine=engine, parser=parser)
|
|
|
|
assert pd.eval("-1.0", parser=parser, engine=engine) == -1.0
|
|
assert pd.eval("+1.0", parser=parser, engine=engine) == +1.0
|
|
assert pd.eval("~1", parser=parser, engine=engine) == ~1
|
|
assert pd.eval("-1", parser=parser, engine=engine) == -1
|
|
assert pd.eval("+1", parser=parser, engine=engine) == +1
|
|
with tm.assert_produces_warning(
|
|
warn, match="Bitwise inversion", check_stacklevel=False
|
|
):
|
|
assert pd.eval("~True", parser=parser, engine=engine) == ~True
|
|
with tm.assert_produces_warning(
|
|
warn, match="Bitwise inversion", check_stacklevel=False
|
|
):
|
|
assert pd.eval("~False", parser=parser, engine=engine) == ~False
|
|
assert pd.eval("-True", parser=parser, engine=engine) == -True
|
|
assert pd.eval("-False", parser=parser, engine=engine) == -False
|
|
assert pd.eval("+True", parser=parser, engine=engine) == +True
|
|
assert pd.eval("+False", parser=parser, engine=engine) == +False
|
|
|
|
def test_unary_in_array(self):
|
|
# GH 11235
|
|
# TODO: 2022-01-29: result return list with numexpr 2.7.3 in CI
|
|
# but cannot reproduce locally
|
|
result = np.array(
|
|
pd.eval("[-True, True, +True, -False, False, +False, -37, 37, ~37, +37]"),
|
|
dtype=np.object_,
|
|
)
|
|
expected = np.array(
|
|
[
|
|
-True,
|
|
True,
|
|
+True,
|
|
-False,
|
|
False,
|
|
+False,
|
|
-37,
|
|
37,
|
|
~37,
|
|
+37,
|
|
],
|
|
dtype=np.object_,
|
|
)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
|
|
@pytest.mark.parametrize("expr", ["x < -0.1", "-5 > x"])
|
|
def test_float_comparison_bin_op(self, dtype, expr):
|
|
# GH 16363
|
|
df = DataFrame({"x": np.array([0], dtype=dtype)})
|
|
res = df.eval(expr)
|
|
assert res.values == np.array([False])
|
|
|
|
def test_unary_in_function(self):
|
|
# GH 46471
|
|
df = DataFrame({"x": [0, 1, np.nan]})
|
|
|
|
result = df.eval("x.fillna(-1)")
|
|
expected = df.x.fillna(-1)
|
|
# column name becomes None if using numexpr
|
|
# only check names when the engine is not numexpr
|
|
tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR)
|
|
|
|
result = df.eval("x.shift(1, fill_value=-1)")
|
|
expected = df.x.shift(1, fill_value=-1)
|
|
tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR)
|
|
|
|
@pytest.mark.parametrize(
|
|
"ex",
|
|
(
|
|
"1 or 2",
|
|
"1 and 2",
|
|
"a and b",
|
|
"a or b",
|
|
"1 or 2 and (3 + 2) > 3",
|
|
"2 * x > 2 or 1 and 2",
|
|
"2 * df > 3 and 1 or a",
|
|
),
|
|
)
|
|
def test_disallow_scalar_bool_ops(self, ex, engine, parser):
|
|
x, a, b = np.random.default_rng(2).standard_normal(3), 1, 2 # noqa: F841
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((3, 2))) # noqa: F841
|
|
|
|
msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval(ex, engine=engine, parser=parser)
|
|
|
|
def test_identical(self, engine, parser):
|
|
# see gh-10546
|
|
x = 1
|
|
result = pd.eval("x", engine=engine, parser=parser)
|
|
assert result == 1
|
|
assert is_scalar(result)
|
|
|
|
x = 1.5
|
|
result = pd.eval("x", engine=engine, parser=parser)
|
|
assert result == 1.5
|
|
assert is_scalar(result)
|
|
|
|
x = False
|
|
result = pd.eval("x", engine=engine, parser=parser)
|
|
assert not result
|
|
assert is_bool(result)
|
|
assert is_scalar(result)
|
|
|
|
x = np.array([1])
|
|
result = pd.eval("x", engine=engine, parser=parser)
|
|
tm.assert_numpy_array_equal(result, np.array([1]))
|
|
assert result.shape == (1,)
|
|
|
|
x = np.array([1.5])
|
|
result = pd.eval("x", engine=engine, parser=parser)
|
|
tm.assert_numpy_array_equal(result, np.array([1.5]))
|
|
assert result.shape == (1,)
|
|
|
|
x = np.array([False]) # noqa: F841
|
|
result = pd.eval("x", engine=engine, parser=parser)
|
|
tm.assert_numpy_array_equal(result, np.array([False]))
|
|
assert result.shape == (1,)
|
|
|
|
def test_line_continuation(self, engine, parser):
|
|
# GH 11149
|
|
exp = """1 + 2 * \
|
|
5 - 1 + 2 """
|
|
result = pd.eval(exp, engine=engine, parser=parser)
|
|
assert result == 12
|
|
|
|
def test_float_truncation(self, engine, parser):
|
|
# GH 14241
|
|
exp = "1000000000.006"
|
|
result = pd.eval(exp, engine=engine, parser=parser)
|
|
expected = np.float64(exp)
|
|
assert result == expected
|
|
|
|
df = DataFrame({"A": [1000000000.0009, 1000000000.0011, 1000000000.0015]})
|
|
cutoff = 1000000000.0006
|
|
result = df.query(f"A < {cutoff:.4f}")
|
|
assert result.empty
|
|
|
|
cutoff = 1000000000.0010
|
|
result = df.query(f"A > {cutoff:.4f}")
|
|
expected = df.loc[[1, 2], :]
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
exact = 1000000000.0011
|
|
result = df.query(f"A == {exact:.4f}")
|
|
expected = df.loc[[1], :]
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
def test_disallow_python_keywords(self):
|
|
# GH 18221
|
|
df = DataFrame([[0, 0, 0]], columns=["foo", "bar", "class"])
|
|
msg = "Python keyword not valid identifier in numexpr query"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.query("class == 0")
|
|
|
|
df = DataFrame()
|
|
df.index.name = "lambda"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.query("lambda == 0")
|
|
|
|
def test_true_false_logic(self):
|
|
# GH 25823
|
|
# This behavior is deprecated in Python 3.12
|
|
with tm.maybe_produces_warning(
|
|
DeprecationWarning, PY312, check_stacklevel=False
|
|
):
|
|
assert pd.eval("not True") == -2
|
|
assert pd.eval("not False") == -1
|
|
assert pd.eval("True and not True") == 0
|
|
|
|
def test_and_logic_string_match(self):
|
|
# GH 25823
|
|
event = Series({"a": "hello"})
|
|
assert pd.eval(f"{event.str.match('hello').a}")
|
|
assert pd.eval(f"{event.str.match('hello').a and event.str.match('hello').a}")
|
|
|
|
|
|
# -------------------------------------
|
|
# gh-12388: Typecasting rules consistency with python
|
|
|
|
|
|
class TestTypeCasting:
|
|
@pytest.mark.parametrize("op", ["+", "-", "*", "**", "/"])
|
|
# maybe someday... numexpr has too many upcasting rules now
|
|
# chain(*(np.core.sctypes[x] for x in ['uint', 'int', 'float']))
|
|
@pytest.mark.parametrize("dt", [np.float32, np.float64])
|
|
@pytest.mark.parametrize("left_right", [("df", "3"), ("3", "df")])
|
|
def test_binop_typecasting(self, engine, parser, op, dt, left_right):
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)), dtype=dt)
|
|
left, right = left_right
|
|
s = f"{left} {op} {right}"
|
|
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 should_warn(*args):
|
|
not_mono = not any(map(operator.attrgetter("is_monotonic_increasing"), args))
|
|
only_one_dt = reduce(
|
|
operator.xor, (issubclass(x.dtype.type, np.datetime64) for x in args)
|
|
)
|
|
return not_mono and only_one_dt
|
|
|
|
|
|
class TestAlignment:
|
|
index_types = ["i", "s", "dt"]
|
|
lhs_index_types = index_types + ["s"] # 'p'
|
|
|
|
def test_align_nested_unary_op(self, engine, parser):
|
|
s = "df * ~2"
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
|
|
res = pd.eval(s, engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, df * ~2)
|
|
|
|
@pytest.mark.filterwarnings("always::RuntimeWarning")
|
|
@pytest.mark.parametrize("lr_idx_type", lhs_index_types)
|
|
@pytest.mark.parametrize("rr_idx_type", index_types)
|
|
@pytest.mark.parametrize("c_idx_type", index_types)
|
|
def test_basic_frame_alignment(
|
|
self, engine, parser, lr_idx_type, rr_idx_type, c_idx_type, idx_func_dict
|
|
):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((10, 10)),
|
|
index=idx_func_dict[lr_idx_type](10),
|
|
columns=idx_func_dict[c_idx_type](10),
|
|
)
|
|
df2 = DataFrame(
|
|
np.random.default_rng(2).standard_normal((20, 10)),
|
|
index=idx_func_dict[rr_idx_type](20),
|
|
columns=idx_func_dict[c_idx_type](10),
|
|
)
|
|
# only warns if not monotonic and not sortable
|
|
if should_warn(df.index, df2.index):
|
|
with tm.assert_produces_warning(RuntimeWarning):
|
|
res = pd.eval("df + df2", engine=engine, parser=parser)
|
|
else:
|
|
res = pd.eval("df + df2", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, df + df2)
|
|
|
|
@pytest.mark.parametrize("r_idx_type", lhs_index_types)
|
|
@pytest.mark.parametrize("c_idx_type", lhs_index_types)
|
|
def test_frame_comparison(
|
|
self, engine, parser, r_idx_type, c_idx_type, idx_func_dict
|
|
):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((10, 10)),
|
|
index=idx_func_dict[r_idx_type](10),
|
|
columns=idx_func_dict[c_idx_type](10),
|
|
)
|
|
res = pd.eval("df < 2", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, df < 2)
|
|
|
|
df3 = DataFrame(
|
|
np.random.default_rng(2).standard_normal(df.shape),
|
|
index=df.index,
|
|
columns=df.columns,
|
|
)
|
|
res = pd.eval("df < df3", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, df < df3)
|
|
|
|
@pytest.mark.filterwarnings("ignore::RuntimeWarning")
|
|
@pytest.mark.parametrize("r1", lhs_index_types)
|
|
@pytest.mark.parametrize("c1", index_types)
|
|
@pytest.mark.parametrize("r2", index_types)
|
|
@pytest.mark.parametrize("c2", index_types)
|
|
def test_medium_complex_frame_alignment(
|
|
self, engine, parser, r1, c1, r2, c2, idx_func_dict
|
|
):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((3, 2)),
|
|
index=idx_func_dict[r1](3),
|
|
columns=idx_func_dict[c1](2),
|
|
)
|
|
df2 = DataFrame(
|
|
np.random.default_rng(2).standard_normal((4, 2)),
|
|
index=idx_func_dict[r2](4),
|
|
columns=idx_func_dict[c2](2),
|
|
)
|
|
df3 = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)),
|
|
index=idx_func_dict[r2](5),
|
|
columns=idx_func_dict[c2](2),
|
|
)
|
|
if should_warn(df.index, df2.index, df3.index):
|
|
with tm.assert_produces_warning(RuntimeWarning):
|
|
res = pd.eval("df + df2 + df3", engine=engine, parser=parser)
|
|
else:
|
|
res = pd.eval("df + df2 + df3", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(res, df + df2 + df3)
|
|
|
|
@pytest.mark.filterwarnings("ignore::RuntimeWarning")
|
|
@pytest.mark.parametrize("index_name", ["index", "columns"])
|
|
@pytest.mark.parametrize("c_idx_type", index_types)
|
|
@pytest.mark.parametrize("r_idx_type", lhs_index_types)
|
|
def test_basic_frame_series_alignment(
|
|
self, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict
|
|
):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((10, 10)),
|
|
index=idx_func_dict[r_idx_type](10),
|
|
columns=idx_func_dict[c_idx_type](10),
|
|
)
|
|
index = getattr(df, index_name)
|
|
s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
|
|
|
|
if should_warn(df.index, s.index):
|
|
with tm.assert_produces_warning(RuntimeWarning):
|
|
res = pd.eval("df + s", engine=engine, parser=parser)
|
|
else:
|
|
res = pd.eval("df + s", engine=engine, parser=parser)
|
|
|
|
if r_idx_type == "dt" or c_idx_type == "dt":
|
|
expected = df.add(s) if engine == "numexpr" else df + s
|
|
else:
|
|
expected = df + s
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
@pytest.mark.parametrize("index_name", ["index", "columns"])
|
|
@pytest.mark.parametrize(
|
|
"r_idx_type, c_idx_type",
|
|
list(product(["i", "s"], ["i", "s"])) + [("dt", "dt")],
|
|
)
|
|
@pytest.mark.filterwarnings("ignore::RuntimeWarning")
|
|
def test_basic_series_frame_alignment(
|
|
self, request, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict
|
|
):
|
|
if (
|
|
engine == "numexpr"
|
|
and parser in ("pandas", "python")
|
|
and index_name == "index"
|
|
and r_idx_type == "i"
|
|
and c_idx_type == "s"
|
|
):
|
|
reason = (
|
|
f"Flaky column ordering when engine={engine}, "
|
|
f"parser={parser}, index_name={index_name}, "
|
|
f"r_idx_type={r_idx_type}, c_idx_type={c_idx_type}"
|
|
)
|
|
request.applymarker(pytest.mark.xfail(reason=reason, strict=False))
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((10, 7)),
|
|
index=idx_func_dict[r_idx_type](10),
|
|
columns=idx_func_dict[c_idx_type](7),
|
|
)
|
|
index = getattr(df, index_name)
|
|
s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
|
|
if should_warn(s.index, df.index):
|
|
with tm.assert_produces_warning(RuntimeWarning):
|
|
res = pd.eval("s + df", engine=engine, parser=parser)
|
|
else:
|
|
res = pd.eval("s + df", engine=engine, parser=parser)
|
|
|
|
if r_idx_type == "dt" or c_idx_type == "dt":
|
|
expected = df.add(s) if engine == "numexpr" else s + df
|
|
else:
|
|
expected = s + df
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
@pytest.mark.filterwarnings("ignore::RuntimeWarning")
|
|
@pytest.mark.parametrize("c_idx_type", index_types)
|
|
@pytest.mark.parametrize("r_idx_type", lhs_index_types)
|
|
@pytest.mark.parametrize("index_name", ["index", "columns"])
|
|
@pytest.mark.parametrize("op", ["+", "*"])
|
|
def test_series_frame_commutativity(
|
|
self, engine, parser, index_name, op, r_idx_type, c_idx_type, idx_func_dict
|
|
):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((10, 10)),
|
|
index=idx_func_dict[r_idx_type](10),
|
|
columns=idx_func_dict[c_idx_type](10),
|
|
)
|
|
index = getattr(df, index_name)
|
|
s = Series(np.random.default_rng(2).standard_normal(5), index[:5])
|
|
|
|
lhs = f"s {op} df"
|
|
rhs = f"df {op} s"
|
|
if should_warn(df.index, s.index):
|
|
with tm.assert_produces_warning(RuntimeWarning):
|
|
a = pd.eval(lhs, engine=engine, parser=parser)
|
|
with tm.assert_produces_warning(RuntimeWarning):
|
|
b = pd.eval(rhs, engine=engine, parser=parser)
|
|
else:
|
|
a = pd.eval(lhs, engine=engine, parser=parser)
|
|
b = pd.eval(rhs, engine=engine, parser=parser)
|
|
|
|
if r_idx_type != "dt" and c_idx_type != "dt":
|
|
if engine == "numexpr":
|
|
tm.assert_frame_equal(a, b)
|
|
|
|
@pytest.mark.filterwarnings("always::RuntimeWarning")
|
|
@pytest.mark.parametrize("r1", lhs_index_types)
|
|
@pytest.mark.parametrize("c1", index_types)
|
|
@pytest.mark.parametrize("r2", index_types)
|
|
@pytest.mark.parametrize("c2", index_types)
|
|
def test_complex_series_frame_alignment(
|
|
self, engine, parser, r1, c1, r2, c2, idx_func_dict
|
|
):
|
|
n = 3
|
|
m1 = 5
|
|
m2 = 2 * m1
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((m1, n)),
|
|
index=idx_func_dict[r1](m1),
|
|
columns=idx_func_dict[c1](n),
|
|
)
|
|
df2 = DataFrame(
|
|
np.random.default_rng(2).standard_normal((m2, n)),
|
|
index=idx_func_dict[r2](m2),
|
|
columns=idx_func_dict[c2](n),
|
|
)
|
|
index = df2.columns
|
|
ser = Series(np.random.default_rng(2).standard_normal(n), index[:n])
|
|
|
|
if r2 == "dt" or c2 == "dt":
|
|
if engine == "numexpr":
|
|
expected2 = df2.add(ser)
|
|
else:
|
|
expected2 = df2 + ser
|
|
else:
|
|
expected2 = df2 + ser
|
|
|
|
if r1 == "dt" or c1 == "dt":
|
|
if engine == "numexpr":
|
|
expected = expected2.add(df)
|
|
else:
|
|
expected = expected2 + df
|
|
else:
|
|
expected = expected2 + df
|
|
|
|
if should_warn(df2.index, ser.index, df.index):
|
|
with tm.assert_produces_warning(RuntimeWarning):
|
|
res = pd.eval("df2 + ser + df", engine=engine, parser=parser)
|
|
else:
|
|
res = pd.eval("df2 + ser + df", engine=engine, parser=parser)
|
|
assert res.shape == expected.shape
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
def test_performance_warning_for_poor_alignment(self, engine, parser):
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((1000, 10)))
|
|
s = Series(np.random.default_rng(2).standard_normal(10000))
|
|
if engine == "numexpr":
|
|
seen = PerformanceWarning
|
|
else:
|
|
seen = False
|
|
|
|
with tm.assert_produces_warning(seen):
|
|
pd.eval("df + s", engine=engine, parser=parser)
|
|
|
|
s = Series(np.random.default_rng(2).standard_normal(1000))
|
|
with tm.assert_produces_warning(False):
|
|
pd.eval("df + s", engine=engine, parser=parser)
|
|
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((10, 10000)))
|
|
s = Series(np.random.default_rng(2).standard_normal(10000))
|
|
with tm.assert_produces_warning(False):
|
|
pd.eval("df + s", engine=engine, parser=parser)
|
|
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((10, 10)))
|
|
s = Series(np.random.default_rng(2).standard_normal(10000))
|
|
|
|
is_python_engine = engine == "python"
|
|
|
|
if not is_python_engine:
|
|
wrn = PerformanceWarning
|
|
else:
|
|
wrn = False
|
|
|
|
with tm.assert_produces_warning(wrn) as w:
|
|
pd.eval("df + s", engine=engine, parser=parser)
|
|
|
|
if not is_python_engine:
|
|
assert len(w) == 1
|
|
msg = str(w[0].message)
|
|
logged = np.log10(s.size - df.shape[1])
|
|
expected = (
|
|
f"Alignment difference on axis 1 is larger "
|
|
f"than an order of magnitude on term 'df', "
|
|
f"by more than {logged:.4g}; performance may suffer."
|
|
)
|
|
assert msg == expected
|
|
|
|
|
|
# ------------------------------------
|
|
# Slightly more complex ops
|
|
|
|
|
|
class TestOperations:
|
|
def eval(self, *args, **kwargs):
|
|
kwargs["level"] = kwargs.pop("level", 0) + 1
|
|
return pd.eval(*args, **kwargs)
|
|
|
|
def test_simple_arith_ops(self, engine, parser):
|
|
exclude_arith = []
|
|
if parser == "python":
|
|
exclude_arith = ["in", "not in"]
|
|
|
|
arith_ops = [
|
|
op
|
|
for op in expr.ARITH_OPS_SYMS + expr.CMP_OPS_SYMS
|
|
if op not in exclude_arith
|
|
]
|
|
|
|
ops = (op for op in arith_ops if op != "//")
|
|
|
|
for op in ops:
|
|
ex = f"1 {op} 1"
|
|
ex2 = f"x {op} 1"
|
|
ex3 = f"1 {op} (x + 1)"
|
|
|
|
if op in ("in", "not in"):
|
|
msg = "argument of type 'int' is not iterable"
|
|
with pytest.raises(TypeError, match=msg):
|
|
pd.eval(ex, engine=engine, parser=parser)
|
|
else:
|
|
expec = _eval_single_bin(1, op, 1, engine)
|
|
x = self.eval(ex, engine=engine, parser=parser)
|
|
assert x == expec
|
|
|
|
expec = _eval_single_bin(x, op, 1, engine)
|
|
y = self.eval(ex2, local_dict={"x": x}, engine=engine, parser=parser)
|
|
assert y == expec
|
|
|
|
expec = _eval_single_bin(1, op, x + 1, engine)
|
|
y = self.eval(ex3, local_dict={"x": x}, engine=engine, parser=parser)
|
|
assert y == expec
|
|
|
|
@pytest.mark.parametrize("rhs", [True, False])
|
|
@pytest.mark.parametrize("lhs", [True, False])
|
|
@pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS)
|
|
def test_simple_bool_ops(self, rhs, lhs, op):
|
|
ex = f"{lhs} {op} {rhs}"
|
|
|
|
if parser == "python" and op in ["and", "or"]:
|
|
msg = "'BoolOp' nodes are not implemented"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
self.eval(ex)
|
|
return
|
|
|
|
res = self.eval(ex)
|
|
exp = eval(ex)
|
|
assert res == exp
|
|
|
|
@pytest.mark.parametrize("rhs", [True, False])
|
|
@pytest.mark.parametrize("lhs", [True, False])
|
|
@pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS)
|
|
def test_bool_ops_with_constants(self, rhs, lhs, op):
|
|
ex = f"{lhs} {op} {rhs}"
|
|
|
|
if parser == "python" and op in ["and", "or"]:
|
|
msg = "'BoolOp' nodes are not implemented"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
self.eval(ex)
|
|
return
|
|
|
|
res = self.eval(ex)
|
|
exp = eval(ex)
|
|
assert res == exp
|
|
|
|
def test_4d_ndarray_fails(self):
|
|
x = np.random.default_rng(2).standard_normal((3, 4, 5, 6))
|
|
y = Series(np.random.default_rng(2).standard_normal(10))
|
|
msg = "N-dimensional objects, where N > 2, are not supported with eval"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
self.eval("x + y", local_dict={"x": x, "y": y})
|
|
|
|
def test_constant(self):
|
|
x = self.eval("1")
|
|
assert x == 1
|
|
|
|
def test_single_variable(self):
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)))
|
|
df2 = self.eval("df", local_dict={"df": df})
|
|
tm.assert_frame_equal(df, df2)
|
|
|
|
def test_failing_subscript_with_name_error(self):
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) # noqa: F841
|
|
with pytest.raises(NameError, match="name 'x' is not defined"):
|
|
self.eval("df[x > 2] > 2")
|
|
|
|
def test_lhs_expression_subscript(self):
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
|
|
result = self.eval("(df + 1)[df > 2]", local_dict={"df": df})
|
|
expected = (df + 1)[df > 2]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_attr_expression(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc")
|
|
)
|
|
expr1 = "df.a < df.b"
|
|
expec1 = df.a < df.b
|
|
expr2 = "df.a + df.b + df.c"
|
|
expec2 = df.a + df.b + df.c
|
|
expr3 = "df.a + df.b + df.c[df.b < 0]"
|
|
expec3 = df.a + df.b + df.c[df.b < 0]
|
|
exprs = expr1, expr2, expr3
|
|
expecs = expec1, expec2, expec3
|
|
for e, expec in zip(exprs, expecs):
|
|
tm.assert_series_equal(expec, self.eval(e, local_dict={"df": df}))
|
|
|
|
def test_assignment_fails(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc")
|
|
)
|
|
df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
|
|
expr1 = "df = df2"
|
|
msg = "cannot assign without a target object"
|
|
with pytest.raises(ValueError, match=msg):
|
|
self.eval(expr1, local_dict={"df": df, "df2": df2})
|
|
|
|
def test_assignment_column_multiple_raise(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
# multiple assignees
|
|
with pytest.raises(SyntaxError, match="invalid syntax"):
|
|
df.eval("d c = a + b")
|
|
|
|
def test_assignment_column_invalid_assign(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
# invalid assignees
|
|
msg = "left hand side of an assignment must be a single name"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.eval("d,c = a + b")
|
|
|
|
def test_assignment_column_invalid_assign_function_call(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
msg = "cannot assign to function call"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.eval('Timestamp("20131001") = a + b')
|
|
|
|
def test_assignment_single_assign_existing(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
# single assignment - existing variable
|
|
expected = df.copy()
|
|
expected["a"] = expected["a"] + expected["b"]
|
|
df.eval("a = a + b", inplace=True)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_assignment_single_assign_new(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
# single assignment - new variable
|
|
expected = df.copy()
|
|
expected["c"] = expected["a"] + expected["b"]
|
|
df.eval("c = a + b", inplace=True)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_assignment_single_assign_local_overlap(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
df = df.copy()
|
|
a = 1 # noqa: F841
|
|
df.eval("a = 1 + b", inplace=True)
|
|
|
|
expected = df.copy()
|
|
expected["a"] = 1 + expected["b"]
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_assignment_single_assign_name(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
|
|
a = 1 # noqa: F841
|
|
old_a = df.a.copy()
|
|
df.eval("a = a + b", inplace=True)
|
|
result = old_a + df.b
|
|
tm.assert_series_equal(result, df.a, check_names=False)
|
|
assert result.name is None
|
|
|
|
def test_assignment_multiple_raises(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
# multiple assignment
|
|
df.eval("c = a + b", inplace=True)
|
|
msg = "can only assign a single expression"
|
|
with pytest.raises(SyntaxError, match=msg):
|
|
df.eval("c = a = b")
|
|
|
|
def test_assignment_explicit(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
# explicit targets
|
|
self.eval("c = df.a + df.b", local_dict={"df": df}, target=df, inplace=True)
|
|
expected = df.copy()
|
|
expected["c"] = expected["a"] + expected["b"]
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_column_in(self):
|
|
# GH 11235
|
|
df = DataFrame({"a": [11], "b": [-32]})
|
|
result = df.eval("a in [11, -32]")
|
|
expected = Series([True])
|
|
# TODO: 2022-01-29: Name check failed with numexpr 2.7.3 in CI
|
|
# but cannot reproduce locally
|
|
tm.assert_series_equal(result, expected, check_names=False)
|
|
|
|
@pytest.mark.xfail(reason="Unknown: Omitted test_ in name prior.")
|
|
def test_assignment_not_inplace(self):
|
|
# see gh-9297
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab")
|
|
)
|
|
|
|
actual = df.eval("c = a + b", inplace=False)
|
|
assert actual is not None
|
|
|
|
expected = df.copy()
|
|
expected["c"] = expected["a"] + expected["b"]
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_multi_line_expression(self, warn_copy_on_write):
|
|
# GH 11149
|
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
|
expected = df.copy()
|
|
|
|
expected["c"] = expected["a"] + expected["b"]
|
|
expected["d"] = expected["c"] + expected["b"]
|
|
answer = df.eval(
|
|
"""
|
|
c = a + b
|
|
d = c + b""",
|
|
inplace=True,
|
|
)
|
|
tm.assert_frame_equal(expected, df)
|
|
assert answer is None
|
|
|
|
expected["a"] = expected["a"] - 1
|
|
expected["e"] = expected["a"] + 2
|
|
answer = df.eval(
|
|
"""
|
|
a = a - 1
|
|
e = a + 2""",
|
|
inplace=True,
|
|
)
|
|
tm.assert_frame_equal(expected, df)
|
|
assert answer is None
|
|
|
|
# multi-line not valid if not all assignments
|
|
msg = "Multi-line expressions are only valid if all expressions contain"
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.eval(
|
|
"""
|
|
a = b + 2
|
|
b - 2""",
|
|
inplace=False,
|
|
)
|
|
|
|
def test_multi_line_expression_not_inplace(self):
|
|
# GH 11149
|
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
|
expected = df.copy()
|
|
|
|
expected["c"] = expected["a"] + expected["b"]
|
|
expected["d"] = expected["c"] + expected["b"]
|
|
df = df.eval(
|
|
"""
|
|
c = a + b
|
|
d = c + b""",
|
|
inplace=False,
|
|
)
|
|
tm.assert_frame_equal(expected, df)
|
|
|
|
expected["a"] = expected["a"] - 1
|
|
expected["e"] = expected["a"] + 2
|
|
df = df.eval(
|
|
"""
|
|
a = a - 1
|
|
e = a + 2""",
|
|
inplace=False,
|
|
)
|
|
tm.assert_frame_equal(expected, df)
|
|
|
|
def test_multi_line_expression_local_variable(self):
|
|
# GH 15342
|
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
|
expected = df.copy()
|
|
|
|
local_var = 7
|
|
expected["c"] = expected["a"] * local_var
|
|
expected["d"] = expected["c"] + local_var
|
|
answer = df.eval(
|
|
"""
|
|
c = a * @local_var
|
|
d = c + @local_var
|
|
""",
|
|
inplace=True,
|
|
)
|
|
tm.assert_frame_equal(expected, df)
|
|
assert answer is None
|
|
|
|
def test_multi_line_expression_callable_local_variable(self):
|
|
# 26426
|
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
|
|
|
def local_func(a, b):
|
|
return b
|
|
|
|
expected = df.copy()
|
|
expected["c"] = expected["a"] * local_func(1, 7)
|
|
expected["d"] = expected["c"] + local_func(1, 7)
|
|
answer = df.eval(
|
|
"""
|
|
c = a * @local_func(1, 7)
|
|
d = c + @local_func(1, 7)
|
|
""",
|
|
inplace=True,
|
|
)
|
|
tm.assert_frame_equal(expected, df)
|
|
assert answer is None
|
|
|
|
def test_multi_line_expression_callable_local_variable_with_kwargs(self):
|
|
# 26426
|
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
|
|
|
def local_func(a, b):
|
|
return b
|
|
|
|
expected = df.copy()
|
|
expected["c"] = expected["a"] * local_func(b=7, a=1)
|
|
expected["d"] = expected["c"] + local_func(b=7, a=1)
|
|
answer = df.eval(
|
|
"""
|
|
c = a * @local_func(b=7, a=1)
|
|
d = c + @local_func(b=7, a=1)
|
|
""",
|
|
inplace=True,
|
|
)
|
|
tm.assert_frame_equal(expected, df)
|
|
assert answer is None
|
|
|
|
def test_assignment_in_query(self):
|
|
# GH 8664
|
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
|
df_orig = df.copy()
|
|
msg = "cannot assign without a target object"
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.query("a = 1")
|
|
tm.assert_frame_equal(df, df_orig)
|
|
|
|
def test_query_inplace(self):
|
|
# see gh-11149
|
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
|
expected = df.copy()
|
|
expected = expected[expected["a"] == 2]
|
|
df.query("a == 2", inplace=True)
|
|
tm.assert_frame_equal(expected, df)
|
|
|
|
df = {}
|
|
expected = {"a": 3}
|
|
|
|
self.eval("a = 1 + 2", target=df, inplace=True)
|
|
tm.assert_dict_equal(df, expected)
|
|
|
|
@pytest.mark.parametrize("invalid_target", [1, "cat", [1, 2], np.array([]), (1, 3)])
|
|
def test_cannot_item_assign(self, invalid_target):
|
|
msg = "Cannot assign expression output to target"
|
|
expression = "a = 1 + 2"
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
self.eval(expression, target=invalid_target, inplace=True)
|
|
|
|
if hasattr(invalid_target, "copy"):
|
|
with pytest.raises(ValueError, match=msg):
|
|
self.eval(expression, target=invalid_target, inplace=False)
|
|
|
|
@pytest.mark.parametrize("invalid_target", [1, "cat", (1, 3)])
|
|
def test_cannot_copy_item(self, invalid_target):
|
|
msg = "Cannot return a copy of the target"
|
|
expression = "a = 1 + 2"
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
self.eval(expression, target=invalid_target, inplace=False)
|
|
|
|
@pytest.mark.parametrize("target", [1, "cat", [1, 2], np.array([]), (1, 3), {1: 2}])
|
|
def test_inplace_no_assignment(self, target):
|
|
expression = "1 + 2"
|
|
|
|
assert self.eval(expression, target=target, inplace=False) == 3
|
|
|
|
msg = "Cannot operate inplace if there is no assignment"
|
|
with pytest.raises(ValueError, match=msg):
|
|
self.eval(expression, target=target, inplace=True)
|
|
|
|
def test_basic_period_index_boolean_expression(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((2, 2)),
|
|
columns=period_range("2020-01-01", freq="D", periods=2),
|
|
)
|
|
e = df < 2
|
|
r = self.eval("df < 2", local_dict={"df": df})
|
|
x = df < 2
|
|
|
|
tm.assert_frame_equal(r, e)
|
|
tm.assert_frame_equal(x, e)
|
|
|
|
def test_basic_period_index_subscript_expression(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((2, 2)),
|
|
columns=period_range("2020-01-01", freq="D", periods=2),
|
|
)
|
|
r = self.eval("df[df < 2 + 3]", local_dict={"df": df})
|
|
e = df[df < 2 + 3]
|
|
tm.assert_frame_equal(r, e)
|
|
|
|
def test_nested_period_index_subscript_expression(self):
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((2, 2)),
|
|
columns=period_range("2020-01-01", freq="D", periods=2),
|
|
)
|
|
r = self.eval("df[df[df < 2] < 2] + df * 2", local_dict={"df": df})
|
|
e = df[df[df < 2] < 2] + df * 2
|
|
tm.assert_frame_equal(r, e)
|
|
|
|
def test_date_boolean(self, engine, parser):
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
|
|
df["dates1"] = date_range("1/1/2012", periods=5)
|
|
res = self.eval(
|
|
"df.dates1 < 20130101",
|
|
local_dict={"df": df},
|
|
engine=engine,
|
|
parser=parser,
|
|
)
|
|
expec = df.dates1 < "20130101"
|
|
tm.assert_series_equal(res, expec, check_names=False)
|
|
|
|
def test_simple_in_ops(self, engine, parser):
|
|
if parser != "python":
|
|
res = pd.eval("1 in [1, 2]", engine=engine, parser=parser)
|
|
assert res
|
|
|
|
res = pd.eval("2 in (1, 2)", engine=engine, parser=parser)
|
|
assert res
|
|
|
|
res = pd.eval("3 in (1, 2)", engine=engine, parser=parser)
|
|
assert not res
|
|
|
|
res = pd.eval("3 not in (1, 2)", engine=engine, parser=parser)
|
|
assert res
|
|
|
|
res = pd.eval("[3] not in (1, 2)", engine=engine, parser=parser)
|
|
assert res
|
|
|
|
res = pd.eval("[3] in ([3], 2)", engine=engine, parser=parser)
|
|
assert res
|
|
|
|
res = pd.eval("[[3]] in [[[3]], 2]", engine=engine, parser=parser)
|
|
assert res
|
|
|
|
res = pd.eval("(3,) in [(3,), 2]", engine=engine, parser=parser)
|
|
assert res
|
|
|
|
res = pd.eval("(3,) not in [(3,), 2]", engine=engine, parser=parser)
|
|
assert not res
|
|
|
|
res = pd.eval("[(3,)] in [[(3,)], 2]", engine=engine, parser=parser)
|
|
assert res
|
|
else:
|
|
msg = "'In' nodes are not implemented"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval("1 in [1, 2]", engine=engine, parser=parser)
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval("2 in (1, 2)", engine=engine, parser=parser)
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval("3 in (1, 2)", engine=engine, parser=parser)
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval("[(3,)] in (1, 2, [(3,)])", engine=engine, parser=parser)
|
|
msg = "'NotIn' nodes are not implemented"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval("3 not in (1, 2)", engine=engine, parser=parser)
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval("[3] not in (1, 2, [[3]])", engine=engine, parser=parser)
|
|
|
|
def test_check_many_exprs(self, engine, parser):
|
|
a = 1 # noqa: F841
|
|
expr = " * ".join("a" * 33)
|
|
expected = 1
|
|
res = pd.eval(expr, engine=engine, parser=parser)
|
|
assert res == expected
|
|
|
|
@pytest.mark.parametrize(
|
|
"expr",
|
|
[
|
|
"df > 2 and df > 3",
|
|
"df > 2 or df > 3",
|
|
"not df > 2",
|
|
],
|
|
)
|
|
def test_fails_and_or_not(self, expr, engine, parser):
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)))
|
|
if parser == "python":
|
|
msg = "'BoolOp' nodes are not implemented"
|
|
if "not" in expr:
|
|
msg = "'Not' nodes are not implemented"
|
|
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval(
|
|
expr,
|
|
local_dict={"df": df},
|
|
parser=parser,
|
|
engine=engine,
|
|
)
|
|
else:
|
|
# smoke-test, should not raise
|
|
pd.eval(
|
|
expr,
|
|
local_dict={"df": df},
|
|
parser=parser,
|
|
engine=engine,
|
|
)
|
|
|
|
@pytest.mark.parametrize("char", ["|", "&"])
|
|
def test_fails_ampersand_pipe(self, char, engine, parser):
|
|
df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) # noqa: F841
|
|
ex = f"(df + 2)[df > 1] > 0 {char} (df > 0)"
|
|
if parser == "python":
|
|
msg = "cannot evaluate scalar only bool ops"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval(ex, parser=parser, engine=engine)
|
|
else:
|
|
# smoke-test, should not raise
|
|
pd.eval(ex, parser=parser, engine=engine)
|
|
|
|
|
|
class TestMath:
|
|
def eval(self, *args, **kwargs):
|
|
kwargs["level"] = kwargs.pop("level", 0) + 1
|
|
return pd.eval(*args, **kwargs)
|
|
|
|
@pytest.mark.skipif(
|
|
not NUMEXPR_INSTALLED, reason="Unary ops only implemented for numexpr"
|
|
)
|
|
@pytest.mark.parametrize("fn", _unary_math_ops)
|
|
def test_unary_functions(self, fn):
|
|
df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
|
|
a = df.a
|
|
|
|
expr = f"{fn}(a)"
|
|
got = self.eval(expr)
|
|
with np.errstate(all="ignore"):
|
|
expect = getattr(np, fn)(a)
|
|
tm.assert_series_equal(got, expect, check_names=False)
|
|
|
|
@pytest.mark.parametrize("fn", _binary_math_ops)
|
|
def test_binary_functions(self, fn):
|
|
df = DataFrame(
|
|
{
|
|
"a": np.random.default_rng(2).standard_normal(10),
|
|
"b": np.random.default_rng(2).standard_normal(10),
|
|
}
|
|
)
|
|
a = df.a
|
|
b = df.b
|
|
|
|
expr = f"{fn}(a, b)"
|
|
got = self.eval(expr)
|
|
with np.errstate(all="ignore"):
|
|
expect = getattr(np, fn)(a, b)
|
|
tm.assert_almost_equal(got, expect, check_names=False)
|
|
|
|
def test_df_use_case(self, engine, parser):
|
|
df = DataFrame(
|
|
{
|
|
"a": np.random.default_rng(2).standard_normal(10),
|
|
"b": np.random.default_rng(2).standard_normal(10),
|
|
}
|
|
)
|
|
df.eval(
|
|
"e = arctan2(sin(a), b)",
|
|
engine=engine,
|
|
parser=parser,
|
|
inplace=True,
|
|
)
|
|
got = df.e
|
|
expect = np.arctan2(np.sin(df.a), df.b)
|
|
tm.assert_series_equal(got, expect, check_names=False)
|
|
|
|
def test_df_arithmetic_subexpression(self, engine, parser):
|
|
df = DataFrame(
|
|
{
|
|
"a": np.random.default_rng(2).standard_normal(10),
|
|
"b": np.random.default_rng(2).standard_normal(10),
|
|
}
|
|
)
|
|
df.eval("e = sin(a + b)", engine=engine, parser=parser, inplace=True)
|
|
got = df.e
|
|
expect = np.sin(df.a + df.b)
|
|
tm.assert_series_equal(got, expect, check_names=False)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dtype, expect_dtype",
|
|
[
|
|
(np.int32, np.float64),
|
|
(np.int64, np.float64),
|
|
(np.float32, np.float32),
|
|
(np.float64, np.float64),
|
|
pytest.param(np.complex128, np.complex128, marks=td.skip_if_windows),
|
|
],
|
|
)
|
|
def test_result_types(self, dtype, expect_dtype, engine, parser):
|
|
# xref https://github.com/pandas-dev/pandas/issues/12293
|
|
# this fails on Windows, apparently a floating point precision issue
|
|
|
|
# Did not test complex64 because DataFrame is converting it to
|
|
# complex128. Due to https://github.com/pandas-dev/pandas/issues/10952
|
|
df = DataFrame(
|
|
{"a": np.random.default_rng(2).standard_normal(10).astype(dtype)}
|
|
)
|
|
assert df.a.dtype == dtype
|
|
df.eval("b = sin(a)", engine=engine, parser=parser, inplace=True)
|
|
got = df.b
|
|
expect = np.sin(df.a)
|
|
assert expect.dtype == got.dtype
|
|
assert expect_dtype == got.dtype
|
|
tm.assert_series_equal(got, expect, check_names=False)
|
|
|
|
def test_undefined_func(self, engine, parser):
|
|
df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
|
|
msg = '"mysin" is not a supported function'
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.eval("mysin(a)", engine=engine, parser=parser)
|
|
|
|
def test_keyword_arg(self, engine, parser):
|
|
df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})
|
|
msg = 'Function "sin" does not support keyword arguments'
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.eval("sin(x=a)", engine=engine, parser=parser)
|
|
|
|
|
|
_var_s = np.random.default_rng(2).standard_normal(10)
|
|
|
|
|
|
class TestScope:
|
|
def test_global_scope(self, engine, parser):
|
|
e = "_var_s * 2"
|
|
tm.assert_numpy_array_equal(
|
|
_var_s * 2, pd.eval(e, engine=engine, parser=parser)
|
|
)
|
|
|
|
def test_no_new_locals(self, engine, parser):
|
|
x = 1
|
|
lcls = locals().copy()
|
|
pd.eval("x + 1", local_dict=lcls, engine=engine, parser=parser)
|
|
lcls2 = locals().copy()
|
|
lcls2.pop("lcls")
|
|
assert lcls == lcls2
|
|
|
|
def test_no_new_globals(self, engine, parser):
|
|
x = 1 # noqa: F841
|
|
gbls = globals().copy()
|
|
pd.eval("x + 1", engine=engine, parser=parser)
|
|
gbls2 = globals().copy()
|
|
assert gbls == gbls2
|
|
|
|
def test_empty_locals(self, engine, parser):
|
|
# GH 47084
|
|
x = 1 # noqa: F841
|
|
msg = "name 'x' is not defined"
|
|
with pytest.raises(UndefinedVariableError, match=msg):
|
|
pd.eval("x + 1", engine=engine, parser=parser, local_dict={})
|
|
|
|
def test_empty_globals(self, engine, parser):
|
|
# GH 47084
|
|
msg = "name '_var_s' is not defined"
|
|
e = "_var_s * 2"
|
|
with pytest.raises(UndefinedVariableError, match=msg):
|
|
pd.eval(e, engine=engine, parser=parser, global_dict={})
|
|
|
|
|
|
@td.skip_if_no("numexpr")
|
|
def test_invalid_engine():
|
|
msg = "Invalid engine 'asdf' passed"
|
|
with pytest.raises(KeyError, match=msg):
|
|
pd.eval("x + y", local_dict={"x": 1, "y": 2}, engine="asdf")
|
|
|
|
|
|
@td.skip_if_no("numexpr")
|
|
@pytest.mark.parametrize(
|
|
("use_numexpr", "expected"),
|
|
(
|
|
(True, "numexpr"),
|
|
(False, "python"),
|
|
),
|
|
)
|
|
def test_numexpr_option_respected(use_numexpr, expected):
|
|
# GH 32556
|
|
from pandas.core.computation.eval import _check_engine
|
|
|
|
with pd.option_context("compute.use_numexpr", use_numexpr):
|
|
result = _check_engine(None)
|
|
assert result == expected
|
|
|
|
|
|
@td.skip_if_no("numexpr")
|
|
def test_numexpr_option_incompatible_op():
|
|
# GH 32556
|
|
with pd.option_context("compute.use_numexpr", False):
|
|
df = DataFrame(
|
|
{"A": [True, False, True, False, None, None], "B": [1, 2, 3, 4, 5, 6]}
|
|
)
|
|
result = df.query("A.isnull()")
|
|
expected = DataFrame({"A": [None, None], "B": [5, 6]}, index=[4, 5])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@td.skip_if_no("numexpr")
|
|
def test_invalid_parser():
|
|
msg = "Invalid parser 'asdf' passed"
|
|
with pytest.raises(KeyError, match=msg):
|
|
pd.eval("x + y", local_dict={"x": 1, "y": 2}, parser="asdf")
|
|
|
|
|
|
_parsers: dict[str, type[BaseExprVisitor]] = {
|
|
"python": PythonExprVisitor,
|
|
"pytables": pytables.PyTablesExprVisitor,
|
|
"pandas": PandasExprVisitor,
|
|
}
|
|
|
|
|
|
@pytest.mark.parametrize("engine", ENGINES)
|
|
@pytest.mark.parametrize("parser", _parsers)
|
|
def test_disallowed_nodes(engine, parser):
|
|
VisitorClass = _parsers[parser]
|
|
inst = VisitorClass("x + 1", engine, parser)
|
|
|
|
for ops in VisitorClass.unsupported_nodes:
|
|
msg = "nodes are not implemented"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
getattr(inst, ops)()
|
|
|
|
|
|
def test_syntax_error_exprs(engine, parser):
|
|
e = "s +"
|
|
with pytest.raises(SyntaxError, match="invalid syntax"):
|
|
pd.eval(e, engine=engine, parser=parser)
|
|
|
|
|
|
def test_name_error_exprs(engine, parser):
|
|
e = "s + t"
|
|
msg = "name 's' is not defined"
|
|
with pytest.raises(NameError, match=msg):
|
|
pd.eval(e, engine=engine, parser=parser)
|
|
|
|
|
|
@pytest.mark.parametrize("express", ["a + @b", "@a + b", "@a + @b"])
|
|
def test_invalid_local_variable_reference(engine, parser, express):
|
|
a, b = 1, 2 # noqa: F841
|
|
|
|
if parser != "pandas":
|
|
with pytest.raises(SyntaxError, match="The '@' prefix is only"):
|
|
pd.eval(express, engine=engine, parser=parser)
|
|
else:
|
|
with pytest.raises(SyntaxError, match="The '@' prefix is not"):
|
|
pd.eval(express, engine=engine, parser=parser)
|
|
|
|
|
|
def test_numexpr_builtin_raises(engine, parser):
|
|
sin, dotted_line = 1, 2
|
|
if engine == "numexpr":
|
|
msg = "Variables in expression .+"
|
|
with pytest.raises(NumExprClobberingError, match=msg):
|
|
pd.eval("sin + dotted_line", engine=engine, parser=parser)
|
|
else:
|
|
res = pd.eval("sin + dotted_line", engine=engine, parser=parser)
|
|
assert res == sin + dotted_line
|
|
|
|
|
|
def test_bad_resolver_raises(engine, parser):
|
|
cannot_resolve = 42, 3.0
|
|
with pytest.raises(TypeError, match="Resolver of type .+"):
|
|
pd.eval("1 + 2", resolvers=cannot_resolve, engine=engine, parser=parser)
|
|
|
|
|
|
def test_empty_string_raises(engine, parser):
|
|
# GH 13139
|
|
with pytest.raises(ValueError, match="expr cannot be an empty string"):
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pd.eval("", engine=engine, parser=parser)
|
|
|
|
|
|
def test_more_than_one_expression_raises(engine, parser):
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|
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"))
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|
@pytest.mark.parametrize("lhs", (int, float))
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|
@pytest.mark.parametrize("rhs", (int, float))
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|
def test_bool_ops_fails_on_scalars(lhs, cmp, rhs, engine, parser):
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|
gen = {
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|
int: lambda: np.random.default_rng(2).integers(10),
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|
float: np.random.default_rng(2).standard_normal,
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|
}
|
|
|
|
mid = gen[lhs]() # noqa: F841
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|
lhs = gen[lhs]()
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|
rhs = gen[rhs]()
|
|
|
|
ex1 = f"lhs {cmp} mid {cmp} rhs"
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|
ex2 = f"lhs {cmp} mid and mid {cmp} rhs"
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|
ex3 = f"(lhs {cmp} mid) & (mid {cmp} rhs)"
|
|
for ex in (ex1, ex2, ex3):
|
|
msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
pd.eval(ex, engine=engine, parser=parser)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"other",
|
|
[
|
|
"'x'",
|
|
"...",
|
|
],
|
|
)
|
|
def test_equals_various(other):
|
|
df = DataFrame({"A": ["a", "b", "c"]}, dtype=object)
|
|
result = df.eval(f"A == {other}")
|
|
expected = Series([False, False, False], name="A")
|
|
if USE_NUMEXPR:
|
|
# https://github.com/pandas-dev/pandas/issues/10239
|
|
# lose name with numexpr engine. Remove when that's fixed.
|
|
expected.name = None
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_inf(engine, parser):
|
|
s = "inf + 1"
|
|
expected = np.inf
|
|
result = pd.eval(s, engine=engine, parser=parser)
|
|
assert result == expected
|
|
|
|
|
|
@pytest.mark.parametrize("column", ["Temp(°C)", "Capacitance(μF)"])
|
|
def test_query_token(engine, column):
|
|
# See: https://github.com/pandas-dev/pandas/pull/42826
|
|
df = DataFrame(
|
|
np.random.default_rng(2).standard_normal((5, 2)), columns=[column, "b"]
|
|
)
|
|
expected = df[df[column] > 5]
|
|
query_string = f"`{column}` > 5"
|
|
result = df.query(query_string, engine=engine)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_negate_lt_eq_le(engine, parser):
|
|
df = DataFrame([[0, 10], [1, 20]], columns=["cat", "count"])
|
|
expected = df[~(df.cat > 0)]
|
|
|
|
result = df.query("~(cat > 0)", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
if parser == "python":
|
|
msg = "'Not' nodes are not implemented"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
df.query("not (cat > 0)", engine=engine, parser=parser)
|
|
else:
|
|
result = df.query("not (cat > 0)", engine=engine, parser=parser)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"column",
|
|
DEFAULT_GLOBALS.keys(),
|
|
)
|
|
def test_eval_no_support_column_name(request, column):
|
|
# GH 44603
|
|
if column in ["True", "False", "inf", "Inf"]:
|
|
request.applymarker(
|
|
pytest.mark.xfail(
|
|
raises=KeyError,
|
|
reason=f"GH 47859 DataFrame eval not supported with {column}",
|
|
)
|
|
)
|
|
|
|
df = DataFrame(
|
|
np.random.default_rng(2).integers(0, 100, size=(10, 2)),
|
|
columns=[column, "col1"],
|
|
)
|
|
expected = df[df[column] > 6]
|
|
result = df.query(f"{column}>6")
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_set_inplace(using_copy_on_write, warn_copy_on_write):
|
|
# https://github.com/pandas-dev/pandas/issues/47449
|
|
# Ensure we don't only update the DataFrame inplace, but also the actual
|
|
# column values, such that references to this column also get updated
|
|
df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
|
|
result_view = df[:]
|
|
ser = df["A"]
|
|
with tm.assert_cow_warning(warn_copy_on_write):
|
|
df.eval("A = B + C", inplace=True)
|
|
expected = DataFrame({"A": [11, 13, 15], "B": [4, 5, 6], "C": [7, 8, 9]})
|
|
tm.assert_frame_equal(df, expected)
|
|
if not using_copy_on_write:
|
|
tm.assert_series_equal(ser, expected["A"])
|
|
tm.assert_series_equal(result_view["A"], expected["A"])
|
|
else:
|
|
expected = Series([1, 2, 3], name="A")
|
|
tm.assert_series_equal(ser, expected)
|
|
tm.assert_series_equal(result_view["A"], expected)
|
|
|
|
|
|
class TestValidate:
|
|
@pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
|
|
def test_validate_bool_args(self, value):
|
|
msg = 'For argument "inplace" expected type bool, received type'
|
|
with pytest.raises(ValueError, match=msg):
|
|
pd.eval("2+2", inplace=value)
|