def test_binary_ops(self, opname, op_str):
def testit():
for f, f2 in [(self.frame, self.frame2), (self.mixed, self.mixed2)]:
if opname == "pow":
continue
op = getattr(operator, opname)
result = expr._can_use_numexpr(op, op_str, f, f, "evaluate")
assert result != f._is_mixed_type
result = expr.evaluate(op, op_str, f, f, use_numexpr=True)
expected = expr.evaluate(op, op_str, f, f, use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(result, expected.values)
result = expr._can_use_numexpr(op, op_str, f2, f2, "evaluate")
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_binary_ops(self, opname, op_str, left, right):
def testit():
if opname == "pow":
# TODO: get this working
return
op = getattr(operator, opname)
with warnings.catch_warnings():
# array has 0s
msg = "invalid value encountered in true_divide"
warnings.filterwarnings("ignore", msg, RuntimeWarning)
result = expr.evaluate(op, left, left, use_numexpr=True)
expected = expr.evaluate(op, left, left, use_numexpr=False)
tm.assert_numpy_array_equal(result, expected)
result = expr._can_use_numexpr(op, op_str, right, right,
"evaluate")
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def run_frame(self,
df,
other,
binary_comp=None,
run_binary=True,
**kwargs):
self.run_arithmetic(df,
other,
assert_frame_equal,
test_flex=False,
**kwargs)
self.run_arithmetic(df,
other,
assert_frame_equal,
test_flex=True,
**kwargs)
if run_binary:
if binary_comp is None:
expr.set_use_numexpr(False)
binary_comp = other + 1
expr.set_use_numexpr(True)
self.run_binary(df,
binary_comp,
assert_frame_equal,
test_flex=False,
**kwargs)
self.run_binary(df,
binary_comp,
assert_frame_equal,
test_flex=True,
**kwargs)
def run_binary(self, df, other, assert_func, test_flex=False,
numexpr_ops={'gt', 'lt', 'ge', 'le', 'eq', 'ne'}):
"""
tests solely that the result is the same whether or not numexpr is
enabled. Need to test whether the function does the correct thing
elsewhere.
"""
expr._MIN_ELEMENTS = 0
expr.set_test_mode(True)
operations = ['gt', 'lt', 'ge', 'le', 'eq', 'ne']
for arith in operations:
if test_flex:
op = lambda x, y: getattr(df, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, arith)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
expr.get_test_result()
result = op(df, other)
used_numexpr = expr.get_test_result()
try:
if arith in numexpr_ops:
assert used_numexpr, "Did not use numexpr as expected."
else:
assert not used_numexpr, "Used numexpr unexpectedly."
assert_func(expected, result)
except Exception:
pprint_thing("Failed test with operation %r" % arith)
pprint_thing("test_flex was %r" % test_flex)
raise
def test_python_semantics_with_numexpr_installed(self, op, box, scalar):
# https://github.com/pandas-dev/pandas/issues/36047
expr._MIN_ELEMENTS = 0
data = np.arange(-50, 50)
obj = box(data)
method = getattr(obj, op)
result = method(scalar)
# compare result with numpy
expr.set_use_numexpr(False)
expected = method(scalar)
expr.set_use_numexpr(True)
tm.assert_equal(result, expected)
# compare result element-wise with Python
for i, elem in enumerate(data):
if box == DataFrame:
scalar_result = result.iloc[i, 0]
else:
scalar_result = result[i]
try:
expected = getattr(int(elem), op)(scalar)
except ZeroDivisionError:
pass
else:
assert scalar_result == expected
def run_arithmetic(self, df, other, assert_func, check_dtype=False, test_flex=True):
expr._MIN_ELEMENTS = 0
operations = ["add", "sub", "mul", "mod", "truediv", "floordiv"]
for arith in operations:
operator_name = arith
if arith == "div":
operator_name = "truediv"
if test_flex:
op = lambda x, y: getattr(x, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, operator_name)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
result = op(df, other)
try:
if check_dtype:
if arith == "truediv":
assert expected.dtype.kind == "f"
assert_func(expected, result)
except Exception:
pprint_thing("Failed test with operator {op.__name__!r}".format(op=op))
raise
def test_comparison_ops(self, opname, op_str, left, right):
def testit():
f12 = left + 1
f22 = right + 1
op = getattr(operator, opname)
result = expr._can_use_numexpr(op, op_str, left, f12, "evaluate")
assert result != left._is_mixed_type
result = expr.evaluate(op, op_str, left, f12, use_numexpr=True)
expected = expr.evaluate(op, op_str, left, f12, use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(result, expected.values)
result = expr._can_use_numexpr(op, op_str, right, f22, "evaluate")
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_binary_ops(self, opname, op_str, left, right):
def testit():
if opname == "pow":
# TODO: get this working
return
op = getattr(operator, opname)
result = expr._can_use_numexpr(op, op_str, left, left, "evaluate")
assert result != left._is_mixed_type
result = expr.evaluate(op, op_str, left, left, use_numexpr=True)
expected = expr.evaluate(op, op_str, left, left, use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(result, expected.values)
result = expr._can_use_numexpr(op, op_str, right, right, "evaluate")
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def run_arithmetic(self,
df,
other,
assert_func,
check_dtype=False,
test_flex=True):
expr._MIN_ELEMENTS = 0
operations = ['add', 'sub', 'mul', 'mod', 'truediv', 'floordiv']
for arith in operations:
operator_name = arith
if arith == 'div':
operator_name = 'truediv'
if test_flex:
op = lambda x, y: getattr(df, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, operator_name)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
result = op(df, other)
try:
if check_dtype:
if arith == 'truediv':
assert expected.dtype.kind == 'f'
assert_func(expected, result)
except Exception:
pprint_thing("Failed test with operator %r" % op.__name__)
raise
def run_binary(self, df, other):
"""
tests solely that the result is the same whether or not numexpr is
enabled. Need to test whether the function does the correct thing
elsewhere.
"""
expr._MIN_ELEMENTS = 0
expr.set_test_mode(True)
operations = ["gt", "lt", "ge", "le", "eq", "ne"]
for test_flex in [True, False]:
for arith in operations:
if test_flex:
op = lambda x, y: getattr(x, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, arith)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
expr.get_test_result()
result = op(df, other)
used_numexpr = expr.get_test_result()
assert used_numexpr, "Did not use numexpr as expected."
tm.assert_equal(expected, result)
def run_arithmetic(self, df, other, assert_func, check_dtype=False,
test_flex=True):
expr._MIN_ELEMENTS = 0
operations = ['add', 'sub', 'mul', 'mod', 'truediv', 'floordiv']
for arith in operations:
operator_name = arith
if arith == 'div':
operator_name = 'truediv'
if test_flex:
op = lambda x, y: getattr(df, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, operator_name)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
result = op(df, other)
try:
if check_dtype:
if arith == 'truediv':
assert expected.dtype.kind == 'f'
assert_func(expected, result)
except Exception:
pprint_thing("Failed test with operator %r" % op.__name__)
raise
def test_comparison_ops(self, opname, op_str):
def testit():
for f, f2 in [(self.frame, self.frame2), (self.mixed, self.mixed2)]:
f11 = f
f12 = f + 1
f21 = f2
f22 = f2 + 1
op = getattr(operator, opname)
result = expr._can_use_numexpr(op, op_str, f11, f12, "evaluate")
assert result != f11._is_mixed_type
result = expr.evaluate(op, op_str, f11, f12, use_numexpr=True)
expected = expr.evaluate(op, op_str, f11, f12, use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(result, expected.values)
result = expr._can_use_numexpr(op, op_str, f21, f22, "evaluate")
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def setup(self, use_numexpr, threads):
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))
if threads != 'default':
expr.set_numexpr_threads(threads)
if not use_numexpr:
expr.set_use_numexpr(False)
def setup(self, use_numexpr, threads):
self.df = DataFrame(np.random.randn(20000, 100))
self.df2 = DataFrame(np.random.randn(20000, 100))
if threads != "default":
expr.set_numexpr_threads(threads)
if not use_numexpr:
expr.set_use_numexpr(False)
def run_frame(self, df, other, run_binary=True):
self.run_arithmetic(df, other)
if run_binary:
expr.set_use_numexpr(False)
binary_comp = other + 1
expr.set_use_numexpr(True)
self.run_binary(df, binary_comp)
for i in range(len(df.columns)):
self.run_arithmetic(df.iloc[:, i], other.iloc[:, i])
def test_binary_ops(self):
def testit():
for f, f2 in [(self.frame, self.frame2),
(self.mixed, self.mixed2)]:
for op, op_str in [
("add", "+"),
("sub", "-"),
("mul", "*"),
("div", "/"),
("pow", "**"),
]:
if op == "pow":
continue
if op == "div":
op = getattr(operator, "truediv", None)
else:
op = getattr(operator, op, None)
if op is not None:
result = expr._can_use_numexpr(op, op_str, f, f,
"evaluate")
assert result != f._is_mixed_type
result = expr.evaluate(op,
op_str,
f,
f,
use_numexpr=True)
expected = expr.evaluate(op,
op_str,
f,
f,
use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(
result, expected.values)
result = expr._can_use_numexpr(op, op_str, f2, f2,
"evaluate")
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_boolean_ops(self):
def testit():
for f, f2 in [(self.frame, self.frame2),
(self.mixed, self.mixed2)]:
f11 = f
f12 = f + 1
f21 = f2
f22 = f2 + 1
for op, op_str in [
("gt", ">"),
("lt", "<"),
("ge", ">="),
("le", "<="),
("eq", "=="),
("ne", "!="),
]:
op = getattr(operator, op)
result = expr._can_use_numexpr(op, op_str, f11, f12,
"evaluate")
assert result != f11._is_mixed_type
result = expr.evaluate(op,
op_str,
f11,
f12,
use_numexpr=True)
expected = expr.evaluate(op,
op_str,
f11,
f12,
use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(result, expected.values)
result = expr._can_use_numexpr(op, op_str, f21, f22,
"evaluate")
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_binary_ops(self):
def testit():
for f, f2 in [(self.frame, self.frame2),
(self.mixed, self.mixed2)]:
for op, op_str in [('add', '+'), ('sub', '-'), ('mul', '*'),
('div', '/'), ('pow', '**')]:
# numpy >= 1.11 doesn't handle integers
# raised to integer powers
# https://github.com/pandas-dev/pandas/issues/15363
if op == 'pow' and not _np_version_under1p11:
continue
if op == 'div':
op = getattr(operator, 'truediv', None)
else:
op = getattr(operator, op, None)
if op is not None:
result = expr._can_use_numexpr(op, op_str, f, f,
'evaluate')
self.assertNotEqual(result, f._is_mixed_type)
result = expr.evaluate(op,
op_str,
f,
f,
use_numexpr=True)
expected = expr.evaluate(op,
op_str,
f,
f,
use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(
result, expected.values)
result = expr._can_use_numexpr(op, op_str, f2, f2,
'evaluate')
self.assertFalse(result)
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def use_numexpr(use, min_elements=None):
from pandas.core.computation import expressions as expr
if min_elements is None:
min_elements = expr._MIN_ELEMENTS
olduse = expr.USE_NUMEXPR
oldmin = expr._MIN_ELEMENTS
expr.set_use_numexpr(use)
expr._MIN_ELEMENTS = min_elements
yield
expr._MIN_ELEMENTS = oldmin
expr.set_use_numexpr(olduse)
def test_binary_ops(self):
def testit():
for f, f2 in [(self.frame, self.frame2),
(self.mixed, self.mixed2)]:
for op, op_str in [('add', '+'), ('sub', '-'), ('mul', '*'),
('div', '/'), ('pow', '**')]:
if op == 'pow':
continue
if op == 'div':
op = getattr(operator, 'truediv', None)
else:
op = getattr(operator, op, None)
if op is not None:
result = expr._can_use_numexpr(op, op_str, f, f,
'evaluate')
assert result != f._is_mixed_type
result = expr.evaluate(op,
op_str,
f,
f,
use_numexpr=True)
expected = expr.evaluate(op,
op_str,
f,
f,
use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(
result, expected.values)
result = expr._can_use_numexpr(op, op_str, f2, f2,
'evaluate')
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_where(self, cond, df):
def testit():
c = np.empty(df.shape, dtype=np.bool_)
c.fill(cond)
result = expr.where(c, df.values, df.values + 1)
expected = np.where(c, df.values, df.values + 1)
tm.assert_numpy_array_equal(result, expected)
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def call_op(df, other, flex: bool, opname: str):
if flex:
op = lambda x, y: getattr(x, opname)(y)
op.__name__ = opname
else:
op = getattr(operator, opname)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
expr.get_test_result()
result = op(df, other)
return result, expected
def run_frame(self, df, other, binary_comp=None, run_binary=True,
**kwargs):
self.run_arithmetic(df, other, assert_frame_equal,
test_flex=False, **kwargs)
self.run_arithmetic(df, other, assert_frame_equal, test_flex=True,
**kwargs)
if run_binary:
if binary_comp is None:
expr.set_use_numexpr(False)
binary_comp = other + 1
expr.set_use_numexpr(True)
self.run_binary(df, binary_comp, assert_frame_equal,
test_flex=False, **kwargs)
self.run_binary(df, binary_comp, assert_frame_equal,
test_flex=True, **kwargs)
def test_binary_ops(self):
def testit():
for f, f2 in [(self.frame, self.frame2),
(self.mixed, self.mixed2)]:
for op, op_str in [('add', '+'), ('sub', '-'), ('mul', '*'),
('div', '/'), ('pow', '**')]:
# numpy >= 1.11 doesn't handle integers
# raised to integer powers
# https://github.com/pandas-dev/pandas/issues/15363
if op == 'pow' and not _np_version_under1p11:
continue
if op == 'div':
op = getattr(operator, 'truediv', None)
else:
op = getattr(operator, op, None)
if op is not None:
result = expr._can_use_numexpr(op, op_str, f, f,
'evaluate')
assert result != f._is_mixed_type
result = expr.evaluate(op, op_str, f, f,
use_numexpr=True)
expected = expr.evaluate(op, op_str, f, f,
use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(result,
expected.values)
result = expr._can_use_numexpr(op, op_str, f2, f2,
'evaluate')
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_boolean_ops(self):
def testit():
for f, f2 in [(self.frame, self.frame2),
(self.mixed, self.mixed2)]:
f11 = f
f12 = f + 1
f21 = f2
f22 = f2 + 1
for op, op_str in [('gt', '>'), ('lt', '<'), ('ge', '>='),
('le', '<='), ('eq', '=='), ('ne', '!=')]:
op = getattr(operator, op)
result = expr._can_use_numexpr(op, op_str, f11, f12,
'evaluate')
assert result != f11._is_mixed_type
result = expr.evaluate(op,
op_str,
f11,
f12,
use_numexpr=True)
expected = expr.evaluate(op,
op_str,
f11,
f12,
use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(result, expected.values)
result = expr._can_use_numexpr(op, op_str, f21, f22,
'evaluate')
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_where(self, cond):
def testit():
for f in [self.frame, self.frame2, self.mixed, self.mixed2]:
c = np.empty(f.shape, dtype=np.bool_)
c.fill(cond)
result = expr.where(c, f.values, f.values + 1)
expected = np.where(c, f.values, f.values + 1)
tm.assert_numpy_array_equal(result, expected)
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_where(self):
def testit():
for f in [self.frame, self.frame2, self.mixed, self.mixed2]:
for cond in [True, False]:
c = np.empty(f.shape, dtype=np.bool_)
c.fill(cond)
result = expr.where(c, f.values, f.values + 1)
expected = np.where(c, f.values, f.values + 1)
tm.assert_numpy_array_equal(result, expected)
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_frame_series_axis(self, axis, arith):
# GH#26736 Dataframe.floordiv(Series, axis=1) fails
df = self.frame
if axis == 1:
other = self.frame.iloc[0, :]
else:
other = self.frame.iloc[:, 0]
expr._MIN_ELEMENTS = 0
op_func = getattr(df, arith)
expr.set_use_numexpr(False)
expected = op_func(other, axis=axis)
expr.set_use_numexpr(True)
result = op_func(other, axis=axis)
tm.assert_frame_equal(expected, result)
def test_binary_ops(self):
def testit():
for f, f2 in [(self.frame, self.frame2),
(self.mixed, self.mixed2)]:
for op, op_str in [('add', '+'), ('sub', '-'), ('mul', '*'),
('div', '/'), ('pow', '**')]:
if op == 'pow':
continue
if op == 'div':
op = getattr(operator, 'truediv', None)
else:
op = getattr(operator, op, None)
if op is not None:
result = expr._can_use_numexpr(op, op_str, f, f,
'evaluate')
assert result != f._is_mixed_type
result = expr.evaluate(op, op_str, f, f,
use_numexpr=True)
expected = expr.evaluate(op, op_str, f, f,
use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(result,
expected.values)
result = expr._can_use_numexpr(op, op_str, f2, f2,
'evaluate')
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def run_binary(
self,
df,
other,
assert_func,
test_flex=False,
numexpr_ops={"gt", "lt", "ge", "le", "eq", "ne"},
):
"""
tests solely that the result is the same whether or not numexpr is
enabled. Need to test whether the function does the correct thing
elsewhere.
"""
expr._MIN_ELEMENTS = 0
expr.set_test_mode(True)
operations = ["gt", "lt", "ge", "le", "eq", "ne"]
for arith in operations:
if test_flex:
op = lambda x, y: getattr(df, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, arith)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
expr.get_test_result()
result = op(df, other)
used_numexpr = expr.get_test_result()
try:
if arith in numexpr_ops:
assert used_numexpr, "Did not use numexpr as expected."
else:
assert not used_numexpr, "Used numexpr unexpectedly."
assert_func(expected, result)
except Exception:
pprint_thing(
"Failed test with operation {arith!r}".format(arith=arith))
pprint_thing(
"test_flex was {test_flex!r}".format(test_flex=test_flex))
raise
def test_comparison_ops(self, opname, op_str, left, right):
def testit():
f12 = left + 1
f22 = right + 1
op = getattr(operator, opname)
result = expr.evaluate(op, left, f12, use_numexpr=True)
expected = expr.evaluate(op, left, f12, use_numexpr=False)
tm.assert_numpy_array_equal(result, expected)
result = expr._can_use_numexpr(op, op_str, right, f22, "evaluate")
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_frame_series_axis(self, axis, arith):
# GH#26736 Dataframe.floordiv(Series, axis=1) fails
if axis == 1 and arith == "floordiv":
pytest.xfail("'floordiv' does not succeed with axis=1 #27636")
df = self.frame
if axis == 1:
other = self.frame.iloc[0, :]
else:
other = self.frame.iloc[:, 0]
expr._MIN_ELEMENTS = 0
op_func = getattr(df, arith)
expr.set_use_numexpr(False)
expected = op_func(other, axis=axis)
expr.set_use_numexpr(True)
result = op_func(other, axis=axis)
assert_frame_equal(expected, result)
def run_arithmetic(self,
df,
other,
assert_func,
check_dtype=False,
test_flex=True):
expr._MIN_ELEMENTS = 0
operations = ['add', 'sub', 'mul', 'mod', 'truediv', 'floordiv', 'pow']
if not compat.PY3:
operations.append('div')
for arith in operations:
# numpy >= 1.11 doesn't handle integers
# raised to integer powers
# https://github.com/pandas-dev/pandas/issues/15363
if arith == 'pow' and not _np_version_under1p11:
continue
operator_name = arith
if arith == 'div':
operator_name = 'truediv'
if test_flex:
op = lambda x, y: getattr(df, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, operator_name)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
result = op(df, other)
try:
if check_dtype:
if arith == 'truediv':
assert expected.dtype.kind == 'f'
assert_func(expected, result)
except Exception:
pprint_thing("Failed test with operator %r" % op.__name__)
raise
def test_boolean_ops(self):
def testit():
for f, f2 in [(self.frame, self.frame2),
(self.mixed, self.mixed2)]:
f11 = f
f12 = f + 1
f21 = f2
f22 = f2 + 1
for op, op_str in [('gt', '>'), ('lt', '<'), ('ge', '>='),
('le', '<='), ('eq', '=='), ('ne', '!=')]:
op = getattr(operator, op)
result = expr._can_use_numexpr(op, op_str, f11, f12,
'evaluate')
assert result != f11._is_mixed_type
result = expr.evaluate(op, op_str, f11, f12,
use_numexpr=True)
expected = expr.evaluate(op, op_str, f11, f12,
use_numexpr=False)
if isinstance(result, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_numpy_array_equal(result, expected.values)
result = expr._can_use_numexpr(op, op_str, f21, f22,
'evaluate')
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def run_arithmetic(self, df, other):
expr._MIN_ELEMENTS = 0
operations = ["add", "sub", "mul", "mod", "truediv", "floordiv"]
for test_flex in [True, False]:
for arith in operations:
# TODO: share with run_binary
if test_flex:
op = lambda x, y: getattr(x, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, arith)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
result = op(df, other)
if arith == "truediv":
if expected.ndim == 1:
assert expected.dtype.kind == "f"
else:
assert all(x.kind == "f" for x in expected.dtypes.values)
tm.assert_equal(expected, result)
def test_binary_ops(self, opname, op_str, left, right):
def testit():
if opname == "pow":
# TODO: get this working
return
op = getattr(operator, opname)
result = expr.evaluate(op, left, left, use_numexpr=True)
expected = expr.evaluate(op, left, left, use_numexpr=False)
tm.assert_numpy_array_equal(result, expected)
result = expr._can_use_numexpr(op, op_str, right, right, "evaluate")
assert not result
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def run_arithmetic(self, df, other, assert_func, check_dtype=False,
test_flex=True):
expr._MIN_ELEMENTS = 0
operations = ['add', 'sub', 'mul', 'mod', 'truediv', 'floordiv', 'pow']
if not compat.PY3:
operations.append('div')
for arith in operations:
# numpy >= 1.11 doesn't handle integers
# raised to integer powers
# https://github.com/pandas-dev/pandas/issues/15363
if arith == 'pow' and not _np_version_under1p11:
continue
operator_name = arith
if arith == 'div':
operator_name = 'truediv'
if test_flex:
op = lambda x, y: getattr(df, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, operator_name)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
result = op(df, other)
try:
if check_dtype:
if arith == 'truediv':
assert expected.dtype.kind == 'f'
assert_func(expected, result)
except Exception:
pprint_thing("Failed test with operator %r" % op.__name__)
raise
def use_numexpr_cb(key):
from pandas.core.computation import expressions
expressions.set_use_numexpr(cf.get_option(key))
def teardown(self, use_numexpr, threads):
expr.set_use_numexpr(True)
expr.set_numexpr_threads()
def use_numexpr_cb(key):
from pandas.core.computation import expressions
expressions.set_use_numexpr(cf.get_option(key))