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Version:
0.21.1 ▾
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import warnings
from warnings import catch_warnings
import operator
from itertools import product
import pytest
from numpy.random import randn, rand, randint
import numpy as np
from pandas.core.dtypes.common import is_bool, is_list_like, is_scalar
import pandas as pd
from pandas.core import common as com
from pandas.errors import PerformanceWarning
from pandas import DataFrame, Series, Panel, date_range
from pandas.util.testing import makeCustomDataframe as mkdf
from pandas.core.computation import pytables
from pandas.core.computation.engines import _engines, NumExprClobberingError
from pandas.core.computation.expr import PythonExprVisitor, PandasExprVisitor
from pandas.core.computation.expressions import (
_USE_NUMEXPR, _NUMEXPR_INSTALLED)
from pandas.core.computation.ops import (
_binary_ops_dict,
_special_case_arith_ops_syms,
_arith_ops_syms, _bool_ops_syms,
_unary_math_ops, _binary_math_ops)
import pandas.core.computation.expr as expr
import pandas.util.testing as tm
from pandas.util.testing import (assert_frame_equal, randbool,
assert_numpy_array_equal, assert_series_equal,
assert_produces_warning)
from pandas.compat import PY3, reduce
_series_frame_incompatible = _bool_ops_syms
_scalar_skip = 'in', 'not in'
@pytest.fixture(params=(
pytest.param(engine,
marks=pytest.mark.skipif(
engine == 'numexpr' and not _USE_NUMEXPR,
reason='numexpr enabled->{enabled}, '
'installed->{installed}'.format(
enabled=_USE_NUMEXPR,
installed=_NUMEXPR_INSTALLED)))
for engine in _engines)) # noqa
def engine(request):
return request.param
@pytest.fixture(params=expr._parsers)
def parser(request):
return request.param
def engine_has_neg_frac(engine):
return _engines[engine].has_neg_frac
def _eval_single_bin(lhs, cmp1, rhs, engine):
c = _binary_ops_dict[cmp1]
if engine_has_neg_frac(engine):
try:
return c(lhs, rhs)
except ValueError as e:
if str(e).startswith('negative number cannot be '
'raised to a fractional power'):
return np.nan
raise
return c(lhs, rhs)
def _series_and_2d_ndarray(lhs, rhs):
return ((isinstance(lhs, Series) and
isinstance(rhs, np.ndarray) and rhs.ndim > 1) or
(isinstance(rhs, Series) and
isinstance(lhs, np.ndarray) and lhs.ndim > 1))
def _series_and_frame(lhs, rhs):
return ((isinstance(lhs, Series) and isinstance(rhs, DataFrame)) or
(isinstance(rhs, Series) and isinstance(lhs, DataFrame)))
def _bool_and_frame(lhs, rhs):
return isinstance(lhs, bool) and isinstance(rhs, pd.core.generic.NDFrame)
def _is_py3_complex_incompat(result, expected):
return (PY3 and isinstance(expected, (complex, np.complexfloating)) and
np.isnan(result))
_good_arith_ops = com.difference(_arith_ops_syms, _special_case_arith_ops_syms)
class TestEvalNumexprPandas(object):
@classmethod
def setup_class(cls):
tm.skip_if_no_ne()
import numexpr as ne
cls.ne = ne
cls.engine = 'numexpr'
cls.parser = 'pandas'
@classmethod
def teardown_class(cls):
del cls.engine, cls.parser
if hasattr(cls, 'ne'):
del cls.ne
def setup_data(self):
nan_df1 = DataFrame(rand(10, 5))
nan_df1[nan_df1 > 0.5] = np.nan
nan_df2 = DataFrame(rand(10, 5))
nan_df2[nan_df2 > 0.5] = np.nan
self.pandas_lhses = (DataFrame(randn(10, 5)), Series(randn(5)),
Series([1, 2, np.nan, np.nan, 5]), nan_df1)
self.pandas_rhses = (DataFrame(randn(10, 5)), Series(randn(5)),
Series([1, 2, np.nan, np.nan, 5]), nan_df2)
self.scalar_lhses = randn(),
self.scalar_rhses = randn(),
self.lhses = self.pandas_lhses + self.scalar_lhses
self.rhses = self.pandas_rhses + self.scalar_rhses
def setup_ops(self):
self.cmp_ops = expr._cmp_ops_syms
self.cmp2_ops = self.cmp_ops[::-1]
self.bin_ops = expr._bool_ops_syms
self.special_case_ops = _special_case_arith_ops_syms
self.arith_ops = _good_arith_ops
self.unary_ops = '-', '~', 'not '
def setup_method(self, method):
self.setup_ops()
self.setup_data()
self.current_engines = filter(lambda x: x != self.engine, _engines)
def teardown_method(self, method):
del self.lhses, self.rhses, self.scalar_rhses, self.scalar_lhses
del self.pandas_rhses, self.pandas_lhses, self.current_engines
@pytest.mark.slow
def test_complex_cmp_ops(self):
cmp_ops = ('!=', '==', '<=', '>=', '<', '>')
cmp2_ops = ('>', '<')
for lhs, cmp1, rhs, binop, cmp2 in product(self.lhses, cmp_ops,
self.rhses, self.bin_ops,
cmp2_ops):
self.check_complex_cmp_op(lhs, cmp1, rhs, binop, cmp2)
def test_simple_cmp_ops(self):
bool_lhses = (DataFrame(randbool(size=(10, 5))),
Series(randbool((5,))), randbool())
bool_rhses = (DataFrame(randbool(size=(10, 5))),
Series(randbool((5,))), randbool())
for lhs, rhs, cmp_op in product(bool_lhses, bool_rhses, self.cmp_ops):
self.check_simple_cmp_op(lhs, cmp_op, rhs)
@pytest.mark.slow
def test_binary_arith_ops(self):
for lhs, op, rhs in product(self.lhses, self.arith_ops, self.rhses):
self.check_binary_arith_op(lhs, op, rhs)
def test_modulus(self):
for lhs, rhs in product(self.lhses, self.rhses):
self.check_modulus(lhs, '%', rhs)
def test_floor_division(self):
for lhs, rhs in product(self.lhses, self.rhses):
self.check_floor_division(lhs, '//', rhs)
def test_pow(self):
tm._skip_if_windows()
# odd failure on win32 platform, so skip
for lhs, rhs in product(self.lhses, self.rhses):
self.check_pow(lhs, '**', rhs)
@pytest.mark.slow
def test_single_invert_op(self):
for lhs, op, rhs in product(self.lhses, self.cmp_ops, self.rhses):
self.check_single_invert_op(lhs, op, rhs)
@pytest.mark.slow
def test_compound_invert_op(self):
for lhs, op, rhs in product(self.lhses, self.cmp_ops, self.rhses):
self.check_compound_invert_op(lhs, op, rhs)
@pytest.mark.slow
def test_chained_cmp_op(self):
mids = self.lhses
cmp_ops = '<', '>'
for lhs, cmp1, mid, cmp2, rhs in product(self.lhses, cmp_ops,
mids, cmp_ops, self.rhses):
self.check_chained_cmp_op(lhs, cmp1, mid, cmp2, rhs)
def check_equal(self, result, expected):
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
elif isinstance(result, Series):
tm.assert_series_equal(result, expected)
elif isinstance(result, np.ndarray):
tm.assert_numpy_array_equal(result, expected)
else:
assert result == expected
def check_complex_cmp_op(self, lhs, cmp1, rhs, binop, cmp2):
skip_these = _scalar_skip
ex = '(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)'.format(cmp1=cmp1,
binop=binop,
cmp2=cmp2)
scalar_with_in_notin = (is_scalar(rhs) and (cmp1 in skip_these or
cmp2 in skip_these))
if scalar_with_in_notin:
with pytest.raises(TypeError):
pd.eval(ex, engine=self.engine, parser=self.parser)
with pytest.raises(TypeError):
pd.eval(ex, engine=self.engine, parser=self.parser,
local_dict={'lhs': lhs, 'rhs': rhs})
else:
lhs_new = _eval_single_bin(lhs, cmp1, rhs, self.engine)
rhs_new = _eval_single_bin(lhs, cmp2, rhs, self.engine)
if (isinstance(lhs_new, Series) and
isinstance(rhs_new, DataFrame) and
binop in _series_frame_incompatible):
pass
# TODO: the code below should be added back when left and right
# hand side bool ops are fixed.
#
# try:
# pytest.raises(Exception, pd.eval, ex,
# local_dict={'lhs': lhs, 'rhs': rhs},
# engine=self.engine, parser=self.parser)
# except AssertionError:
# import ipdb
#
# ipdb.set_trace()
# raise
else:
expected = _eval_single_bin(
lhs_new, binop, rhs_new, self.engine)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
self.check_equal(result, expected)
def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs):
def check_operands(left, right, cmp_op):
return _eval_single_bin(left, cmp_op, right, self.engine)
lhs_new = check_operands(lhs, mid, cmp1)
rhs_new = check_operands(mid, rhs, cmp2)
if lhs_new is not None and rhs_new is not None:
ex1 = 'lhs {0} mid {1} rhs'.format(cmp1, cmp2)
ex2 = 'lhs {0} mid and mid {1} rhs'.format(cmp1, cmp2)
ex3 = '(lhs {0} mid) & (mid {1} rhs)'.format(cmp1, cmp2)
expected = _eval_single_bin(lhs_new, '&', rhs_new, self.engine)
for ex in (ex1, ex2, ex3):
result = pd.eval(ex, engine=self.engine,
parser=self.parser)
tm.assert_almost_equal(result, expected)
def check_simple_cmp_op(self, lhs, cmp1, rhs):
ex = 'lhs {0} rhs'.format(cmp1)
if cmp1 in ('in', 'not in') and not is_list_like(rhs):
pytest.raises(TypeError, pd.eval, ex, engine=self.engine,
parser=self.parser, local_dict={'lhs': lhs,
'rhs': rhs})
else:
expected = _eval_single_bin(lhs, cmp1, rhs, self.engine)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
self.check_equal(result, expected)
def check_binary_arith_op(self, lhs, arith1, rhs):
ex = 'lhs {0} rhs'.format(arith1)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
expected = _eval_single_bin(lhs, arith1, rhs, self.engine)
tm.assert_almost_equal(result, expected)
ex = 'lhs {0} rhs {0} rhs'.format(arith1)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
nlhs = _eval_single_bin(lhs, arith1, rhs,
self.engine)
self.check_alignment(result, nlhs, rhs, arith1)
def check_alignment(self, result, nlhs, ghs, op):
try:
nlhs, ghs = nlhs.align(ghs)
except (ValueError, TypeError, AttributeError):
# ValueError: series frame or frame series align
# TypeError, AttributeError: series or frame with scalar align
pass
else:
# direct numpy comparison
expected = self.ne.evaluate('nlhs {0} ghs'.format(op))
tm.assert_numpy_array_equal(result.values, expected)
# modulus, pow, and floor division require special casing
def check_modulus(self, lhs, arith1, rhs):
ex = 'lhs {0} rhs'.format(arith1)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
expected = lhs % rhs
tm.assert_almost_equal(result, expected)
expected = self.ne.evaluate('expected {0} rhs'.format(arith1))
if isinstance(result, (DataFrame, Series)):
tm.assert_almost_equal(result.values, expected)
else:
tm.assert_almost_equal(result, expected.item())
def check_floor_division(self, lhs, arith1, rhs):
ex = 'lhs {0} rhs'.format(arith1)
if self.engine == 'python':
res = pd.eval(ex, engine=self.engine, parser=self.parser)
expected = lhs // rhs
self.check_equal(res, expected)
else:
pytest.raises(TypeError, pd.eval, ex,
local_dict={'lhs': lhs, 'rhs': rhs},
engine=self.engine, parser=self.parser)
def get_expected_pow_result(self, lhs, rhs):
try:
expected = _eval_single_bin(lhs, '**', rhs, self.engine)
except ValueError as e:
if str(e).startswith('negative number cannot be '
'raised to a fractional power'):
if self.engine == 'python':
pytest.skip(str(e))
else:
expected = np.nan
else:
raise
return expected
def check_pow(self, lhs, arith1, rhs):
ex = 'lhs {0} rhs'.format(arith1)
expected = self.get_expected_pow_result(lhs, rhs)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
if (is_scalar(lhs) and is_scalar(rhs) and
_is_py3_complex_incompat(result, expected)):
pytest.raises(AssertionError, tm.assert_numpy_array_equal,
result, expected)
else:
tm.assert_almost_equal(result, expected)
ex = '(lhs {0} rhs) {0} rhs'.format(arith1)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
expected = self.get_expected_pow_result(
self.get_expected_pow_result(lhs, rhs), rhs)
tm.assert_almost_equal(result, expected)
def check_single_invert_op(self, lhs, cmp1, rhs):
# simple
for el in (lhs, rhs):
try:
elb = el.astype(bool)
except AttributeError:
elb = np.array([bool(el)])
expected = ~elb
result = pd.eval('~elb', engine=self.engine, parser=self.parser)
tm.assert_almost_equal(expected, result)
for engine in self.current_engines:
tm.skip_if_no_ne(engine)
tm.assert_almost_equal(result, pd.eval('~elb', engine=engine,
parser=self.parser))
def check_compound_invert_op(self, lhs, cmp1, rhs):
skip_these = 'in', 'not in'
ex = '~(lhs {0} rhs)'.format(cmp1)
if is_scalar(rhs) and cmp1 in skip_these:
pytest.raises(TypeError, pd.eval, ex, engine=self.engine,
parser=self.parser, local_dict={'lhs': lhs,
'rhs': rhs})
else:
# compound
if is_scalar(lhs) and is_scalar(rhs):
lhs, rhs = map(lambda x: np.array([x]), (lhs, rhs))
expected = _eval_single_bin(lhs, cmp1, rhs, self.engine)
if is_scalar(expected):
expected = not expected
else:
expected = ~expected
result = pd.eval(ex, engine=self.engine, parser=self.parser)
tm.assert_almost_equal(expected, result)
# make sure the other engines work the same as this one
for engine in self.current_engines:
tm.skip_if_no_ne(engine)
ev = pd.eval(ex, engine=self.engine, parser=self.parser)
tm.assert_almost_equal(ev, result)
def ex(self, op, var_name='lhs'):
return '{0}{1}'.format(op, var_name)
def test_frame_invert(self):
expr = self.ex('~')
# ~ ##
# frame
# float always raises
lhs = DataFrame(randn(5, 2))
if self.engine == 'numexpr':
with pytest.raises(NotImplementedError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
with pytest.raises(TypeError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
# int raises on numexpr
lhs = DataFrame(randint(5, size=(5, 2)))
if self.engine == 'numexpr':
with pytest.raises(NotImplementedError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = ~lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
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)
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):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
with pytest.raises(TypeError):
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':
with pytest.raises(NotImplementedError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
with pytest.raises(TypeError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
# int raises on numexpr
lhs = Series(randint(5, size=5))
if self.engine == 'numexpr':
with pytest.raises(NotImplementedError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = ~lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
# bool
lhs = Series(rand(5) > 0.5)
expect = ~lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
# float
# int
# bool
# object
lhs = Series(['a', 1, 2.0])
if self.engine == 'numexpr':
with pytest.raises(ValueError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
with pytest.raises(TypeError):
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)
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)
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':
with pytest.raises(NotImplementedError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = -lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
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)
assert_series_equal(expect, result)
# int
lhs = Series(randint(5, size=5))
expect = -lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
# bool doesn't work with numexpr but works elsewhere
lhs = Series(rand(5) > 0.5)
if self.engine == 'numexpr':
with pytest.raises(NotImplementedError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = -lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
def test_frame_pos(self):
expr = self.ex('+')
# float
lhs = DataFrame(randn(5, 2))
if self.engine == 'python':
with pytest.raises(TypeError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_frame_equal(expect, result)
# int
lhs = DataFrame(randint(5, size=(5, 2)))
if self.engine == 'python':
with pytest.raises(TypeError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_frame_equal(expect, result)
# bool doesn't work with numexpr but works elsewhere
lhs = DataFrame(rand(5, 2) > 0.5)
if self.engine == 'python':
with pytest.raises(TypeError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_frame_equal(expect, result)
def test_series_pos(self):
expr = self.ex('+')
# float
lhs = Series(randn(5))
if self.engine == 'python':
with pytest.raises(TypeError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
# int
lhs = Series(randint(5, size=5))
if self.engine == 'python':
with pytest.raises(TypeError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
# bool doesn't work with numexpr but works elsewhere
lhs = Series(rand(5) > 0.5)
if self.engine == 'python':
with pytest.raises(TypeError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
def test_scalar_unary(self):
with pytest.raises(TypeError):
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
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_))
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:
with pytest.raises(NotImplementedError):
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("A < %.4f" % cutoff)
assert result.empty
cutoff = 1000000000.0010
result = df.query("A > %.4f" % cutoff)
expected = df.loc[[1, 2], :]
tm.assert_frame_equal(expected, result)
exact = 1000000000.0011
result = df.query('A == %.4f' % exact)
expected = df.loc[[1], :]
tm.assert_frame_equal(expected, result)
class TestEvalNumexprPython(TestEvalNumexprPandas):
@classmethod
def setup_class(cls):
super(TestEvalNumexprPython, cls).setup_class()
tm.skip_if_no_ne()
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 = 'lhs {0} mid {1} rhs'.format(cmp1, cmp2)
with pytest.raises(NotImplementedError):
pd.eval(ex1, engine=self.engine, parser=self.parser)
class TestEvalPythonPython(TestEvalNumexprPython):
@classmethod
def setup_class(cls):
super(TestEvalPythonPython, cls).setup_class()
cls.engine = 'python'
cls.parser = 'python'
def check_modulus(self, lhs, arith1, rhs):
ex = 'lhs {0} rhs'.format(arith1)
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('nlhs {0} ghs'.format(op))
tm.assert_almost_equal(result, expected)
class TestEvalPythonPandas(TestEvalPythonPython):
@classmethod
def setup_class(cls):
super(TestEvalPythonPandas, cls).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(object):
@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 = mkdf(5, 3, data_gen_f=f, dtype=dt)
s = 'df {} 3'.format(op)
res = pd.eval(s, engine=engine, parser=parser)
assert df.values.dtype == dt
assert res.values.dtype == dt
assert_frame_equal(res, eval(s))
s = '3 {} df'.format(op)
res = pd.eval(s, engine=engine, parser=parser)
assert df.values.dtype == dt
assert res.values.dtype == dt
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(object):
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 = mkdf(5, 3, data_gen_f=f)
res = pd.eval(s, engine=engine, parser=parser)
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 = mkdf(10, 10, data_gen_f=f, r_idx_type=lr_idx_type,
c_idx_type=c_idx_type)
df2 = mkdf(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)
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 = mkdf(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)
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)
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 = mkdf(3, 2, data_gen_f=f, r_idx_type=r1, c_idx_type=c1)
df2 = mkdf(4, 2, data_gen_f=f, r_idx_type=r2, c_idx_type=c2)
df3 = mkdf(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)
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 = mkdf(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
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 = mkdf(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
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):
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):
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 = mkdf(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 = 's {0} df'.format(op)
rhs = 'df {0} s'.format(op)
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':
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 = mkdf(m1, n, data_gen_f=f, r_idx_type=r1, c_idx_type=c1)
df2 = mkdf(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
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 assert_produces_warning(seen):
pd.eval('df + s', engine=engine, parser=parser)
s = Series(randn(1000))
with assert_produces_warning(False):
pd.eval('df + s', engine=engine, parser=parser)
df = DataFrame(randn(10, 10000))
s = Series(randn(10000))
with 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 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)
expected = ("Alignment difference on axis {0} is larger"
" than an order of magnitude on term {1!r}, "
"by more than {2:.4g}; performance may suffer"
"".format(1, 'df', np.log10(s.size - df.shape[1])))
assert msg == expected
# ------------------------------------
# Slightly more complex ops
class TestOperationsNumExprPandas(object):
@classmethod
def setup_class(cls):
tm.skip_if_no_ne()
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 = '1 {0} 1'.format(op)
ex2 = 'x {0} 1'.format(op)
ex3 = '1 {0} (x + 1)'.format(op)
if op in ('in', 'not in'):
pytest.raises(TypeError, 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 = '{0} {1} {2}'.format(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 = '{0} {1} {2}'.format(lhs, op, rhs)
res = self.eval(ex)
exp = eval(ex)
assert res == exp
def test_panel_fails(self):
with catch_warnings(record=True):
x = Panel(randn(3, 4, 5))
y = Series(randn(10))
with pytest.raises(NotImplementedError):
self.eval('x + y',
local_dict={'x': x, 'y': y})
def test_4d_ndarray_fails(self):
x = randn(3, 4, 5, 6)
y = Series(randn(10))
with pytest.raises(NotImplementedError):
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})
assert_frame_equal(df, df2)
def test_truediv(self):
s = np.array([1])
ex = 's / 1'
d = {'s': s} # noqa
if PY3:
res = self.eval(ex, truediv=False)
tm.assert_numpy_array_equal(res, np.array([1.0]))
res = self.eval(ex, truediv=True)
tm.assert_numpy_array_equal(res, np.array([1.0]))
res = self.eval('1 / 2', truediv=True)
expec = 0.5
assert res == expec
res = self.eval('1 / 2', truediv=False)
expec = 0.5
assert res == expec
res = self.eval('s / 2', truediv=False)
expec = 0.5
assert res == expec
res = self.eval('s / 2', truediv=True)
expec = 0.5
assert res == expec
else:
res = self.eval(ex, truediv=False)
tm.assert_numpy_array_equal(res, np.array([1]))
res = self.eval(ex, truediv=True)
tm.assert_numpy_array_equal(res, np.array([1.0]))
res = self.eval('1 / 2', truediv=True)
expec = 0.5
assert res == expec
res = self.eval('1 / 2', truediv=False)
expec = 0
assert res == expec
res = self.eval('s / 2', truediv=False)
expec = 0
assert res == expec
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):
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]
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):
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'
pytest.raises(ValueError, 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
pytest.raises(SyntaxError, df.eval, 'd c = a + b')
# invalid assignees
pytest.raises(SyntaxError, df.eval, 'd,c = a + b')
pytest.raises(SyntaxError, 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)
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)
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']
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
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)
pytest.raises(SyntaxError, 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']
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])
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)
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)
assert_frame_equal(expected, df)
assert ans is None
# multi-line not valid if not all assignments
with pytest.raises(ValueError):
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)
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)
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)
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()
with pytest.raises(ValueError):
df.query('a = 1')
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)
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 tm.assert_raises_regex(ValueError, msg):
self.eval(expression, target=invalid_target, inplace=True)
if hasattr(invalid_target, "copy"):
with tm.assert_raises_regex(ValueError, 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 tm.assert_raises_regex(ValueError, 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 tm.assert_raises_regex(ValueError, msg):
self.eval(expression, target=target, inplace=True)
def test_basic_period_index_boolean_expression(self):
df = mkdf(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
assert_frame_equal(r, e)
assert_frame_equal(x, e)
def test_basic_period_index_subscript_expression(self):
df = mkdf(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]
assert_frame_equal(r, e)
def test_nested_period_index_subscript_expression(self):
df = mkdf(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
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'
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:
with pytest.raises(NotImplementedError):
pd.eval('1 in [1, 2]', engine=self.engine, parser=self.parser)
with pytest.raises(NotImplementedError):
pd.eval('2 in (1, 2)', engine=self.engine, parser=self.parser)
with pytest.raises(NotImplementedError):
pd.eval('3 in (1, 2)', engine=self.engine, parser=self.parser)
with pytest.raises(NotImplementedError):
pd.eval('3 not in (1, 2)', engine=self.engine,
parser=self.parser)
with pytest.raises(NotImplementedError):
pd.eval('[(3,)] in (1, 2, [(3,)])', engine=self.engine,
parser=self.parser)
with pytest.raises(NotImplementedError):
pd.eval('[3] not in (1, 2, [[3]])', engine=self.engine,
parser=self.parser)
class TestOperationsNumExprPython(TestOperationsNumExprPandas):
@classmethod
def setup_class(cls):
super(TestOperationsNumExprPython, cls).setup_class()
cls.engine = 'numexpr'
cls.parser = 'python'
tm.skip_if_no_ne(cls.engine)
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))
pytest.raises(NotImplementedError, 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))
pytest.raises(NotImplementedError, 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))
pytest.raises(NotImplementedError, 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)'
with pytest.raises(NotImplementedError):
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)'
with pytest.raises(NotImplementedError):
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 = '{0} {1} {2}'.format(lhs, op, rhs)
if op in ('and', 'or'):
with pytest.raises(NotImplementedError):
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 = 'lhs {0} rhs'.format(op)
if op in ('and', 'or'):
with pytest.raises(NotImplementedError):
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(TestOperationsPythonPython, cls).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(TestOperationsPythonPandas, cls).setup_class()
cls.engine = 'python'
cls.parser = 'pandas'
cls.arith_ops = expr._arith_ops_syms + expr._cmp_ops_syms
class TestMathPythonPython(object):
@classmethod
def setup_class(cls):
tm.skip_if_no_ne()
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):
df = DataFrame({'a': np.random.randn(10)})
a = df.a
for fn in self.unary_fns:
expr = "{0}(a)".format(fn)
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_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 = "{0}(a, b)".format(fn)
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)
def test_result_types2(self):
# xref https://github.com/pandas-dev/pandas/issues/12293
pytest.skip("unreliable tests on complex128")
# 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)})
with tm.assert_raises_regex(
ValueError, "\"mysin\" is not a supported function"):
df.eval("mysin(a)",
engine=self.engine,
parser=self.parser)
def test_keyword_arg(self):
df = DataFrame({'a': np.random.randn(10)})
with tm.assert_raises_regex(TypeError,
"Function \"sin\" does not support "
"keyword arguments"):
df.eval("sin(x=a)",
engine=self.engine,
parser=self.parser)
class TestMathPythonPandas(TestMathPythonPython):
@classmethod
def setup_class(cls):
super(TestMathPythonPandas, cls).setup_class()
cls.engine = 'python'
cls.parser = 'pandas'
class TestMathNumExprPandas(TestMathPythonPython):
@classmethod
def setup_class(cls):
super(TestMathNumExprPandas, cls).setup_class()
cls.engine = 'numexpr'
cls.parser = 'pandas'
class TestMathNumExprPython(TestMathPythonPython):
@classmethod
def setup_class(cls):
super(TestMathNumExprPython, cls).setup_class()
cls.engine = 'numexpr'
cls.parser = 'python'
_var_s = randn(10)
class TestScope(object):
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 # noqa
lcls = locals().copy()
pd.eval('x + 1', local_dict=lcls, engine=engine, parser=parser)
lcls2 = locals().copy()
lcls2.pop('lcls')
assert lcls == lcls2
def test_no_new_globals(self, engine, parser):
x = 1 # noqa
gbls = globals().copy()
pd.eval('x + 1', engine=engine, parser=parser)
gbls2 = globals().copy()
assert gbls == gbls2
def test_invalid_engine():
tm.skip_if_no_ne()
tm.assert_raises_regex(KeyError, 'Invalid engine \'asdf\' passed',
pd.eval, 'x + y', local_dict={'x': 1, 'y': 2},
engine='asdf')
def test_invalid_parser():
tm.skip_if_no_ne()
tm.assert_raises_regex(KeyError, 'Invalid parser \'asdf\' passed',
pd.eval, 'x + y', local_dict={'x': 1, 'y': 2},
parser='asdf')
_parsers = {'python': PythonExprVisitor, 'pytables': pytables.ExprVisitor,
'pandas': PandasExprVisitor}
@pytest.mark.parametrize('engine', _parsers)
@pytest.mark.parametrize('parser', _parsers)
def test_disallowed_nodes(engine, parser):
tm.skip_if_no_ne(engine)
VisitorClass = _parsers[parser]
uns_ops = VisitorClass.unsupported_nodes
inst = VisitorClass('x + 1', engine, parser)
for ops in uns_ops:
with pytest.raises(NotImplementedError):
getattr(inst, ops)()
def test_syntax_error_exprs(engine, parser):
e = 's +'
with pytest.raises(SyntaxError):
pd.eval(e, engine=engine, parser=parser)
def test_name_error_exprs(engine, parser):
e = 's + t'
with pytest.raises(NameError):
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 tm.assert_raises_regex(SyntaxError,
"The '@' prefix is only"):
pd.eval(_expr, engine=engine, parser=parser)
else:
with tm.assert_raises_regex(SyntaxError,
"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':
with tm.assert_raises_regex(NumExprClobberingError,
'Variables in expression .+'):
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 tm.assert_raises_regex(TypeError, 'Resolver of type .+'):
pd.eval('1 + 2', resolvers=cannot_resolve, engine=engine,
parser=parser)
def test_empty_string_raises(engine, parser):
# GH 13139
with tm.assert_raises_regex(ValueError,
'expr cannot be an empty string'):
pd.eval('', engine=engine, parser=parser)
def test_more_than_one_expression_raises(engine, parser):
with tm.assert_raises_regex(SyntaxError,
'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 = 'lhs {0} mid {1} rhs'.format(cmp, cmp)
ex2 = 'lhs {0} mid and mid {1} rhs'.format(cmp, cmp)
ex3 = '(lhs {0} mid) & (mid {1} rhs)'.format(cmp, cmp)
for ex in (ex1, ex2, ex3):
with pytest.raises(NotImplementedError):
pd.eval(ex, engine=engine, parser=parser)
def test_inf(engine, parser):
s = 'inf + 1'
expected = np.inf
result = pd.eval(s, engine=engine, parser=parser)
assert result == expected
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':
with pytest.raises(NotImplementedError):
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(object):
def test_validate_bool_args(self):
invalid_values = [1, "True", [1, 2, 3], 5.0]
for value in invalid_values:
with pytest.raises(ValueError):
pd.eval("2+2", inplace=value)