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import operator
import re
import numpy as np
import pytest
from pandas import option_context
import pandas._testing as tm
from pandas.core.api import (
DataFrame,
Index,
Series,
)
from pandas.core.computation import expressions as expr
@pytest.fixture
def _frame():
return DataFrame(
np.random.default_rng(2).standard_normal((10001, 4)),
columns=list("ABCD"),
dtype="float64",
)
@pytest.fixture
def _frame2():
return DataFrame(
np.random.default_rng(2).standard_normal((100, 4)),
columns=list("ABCD"),
dtype="float64",
)
@pytest.fixture
def _mixed(_frame):
return DataFrame(
{
"A": _frame["A"].copy(),
"B": _frame["B"].astype("float32"),
"C": _frame["C"].astype("int64"),
"D": _frame["D"].astype("int32"),
}
)
@pytest.fixture
def _mixed2(_frame2):
return DataFrame(
{
"A": _frame2["A"].copy(),
"B": _frame2["B"].astype("float32"),
"C": _frame2["C"].astype("int64"),
"D": _frame2["D"].astype("int32"),
}
)
@pytest.fixture
def _integer():
return DataFrame(
np.random.default_rng(2).integers(1, 100, size=(10001, 4)),
columns=list("ABCD"),
dtype="int64",
)
@pytest.fixture
def _integer_integers(_integer):
# integers to get a case with zeros
return _integer * np.random.default_rng(2).integers(0, 2, size=np.shape(_integer))
@pytest.fixture
def _integer2():
return DataFrame(
np.random.default_rng(2).integers(1, 100, size=(101, 4)),
columns=list("ABCD"),
dtype="int64",
)
@pytest.fixture
def _array(_frame):
return _frame["A"].values.copy()
@pytest.fixture
def _array2(_frame2):
return _frame2["A"].values.copy()
@pytest.fixture
def _array_mixed(_mixed):
return _mixed["D"].values.copy()
@pytest.fixture
def _array_mixed2(_mixed2):
return _mixed2["D"].values.copy()
@pytest.mark.skipif(not expr.USE_NUMEXPR, reason="not using numexpr")
class TestExpressions:
@staticmethod
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)
with option_context("compute.use_numexpr", False):
expected = op(df, other)
expr.get_test_result()
result = op(df, other)
return result, expected
@pytest.mark.parametrize(
"fixture",
[
"_integer",
"_integer2",
"_integer_integers",
"_frame",
"_frame2",
"_mixed",
"_mixed2",
],
)
@pytest.mark.parametrize("flex", [True, False])
@pytest.mark.parametrize(
"arith", ["add", "sub", "mul", "mod", "truediv", "floordiv"]
)
def test_run_arithmetic(self, request, fixture, flex, arith, monkeypatch):
df = request.getfixturevalue(fixture)
with monkeypatch.context() as m:
m.setattr(expr, "_MIN_ELEMENTS", 0)
result, expected = self.call_op(df, df, flex, arith)
if arith == "truediv":
assert all(x.kind == "f" for x in expected.dtypes.values)
tm.assert_equal(expected, result)
for i in range(len(df.columns)):
result, expected = self.call_op(
df.iloc[:, i], df.iloc[:, i], flex, arith
)
if arith == "truediv":
assert expected.dtype.kind == "f"
tm.assert_equal(expected, result)
@pytest.mark.parametrize(
"fixture",
[
"_integer",
"_integer2",
"_integer_integers",
"_frame",
"_frame2",
"_mixed",
"_mixed2",
],
)
@pytest.mark.parametrize("flex", [True, False])
def test_run_binary(self, request, fixture, flex, comparison_op, monkeypatch):
"""
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.
"""
df = request.getfixturevalue(fixture)
arith = comparison_op.__name__
with option_context("compute.use_numexpr", False):
other = df.copy() + 1
with monkeypatch.context() as m:
m.setattr(expr, "_MIN_ELEMENTS", 0)
expr.set_test_mode(True)
result, expected = self.call_op(df, other, flex, arith)
used_numexpr = expr.get_test_result()
assert used_numexpr, "Did not use numexpr as expected."
tm.assert_equal(expected, result)
for i in range(len(df.columns)):
binary_comp = other.iloc[:, i] + 1
self.call_op(df.iloc[:, i], binary_comp, flex, "add")
def test_invalid(self):
array = np.random.default_rng(2).standard_normal(1_000_001)
array2 = np.random.default_rng(2).standard_normal(100)
# no op
result = expr._can_use_numexpr(operator.add, None, array, array, "evaluate")
assert not result
# min elements
result = expr._can_use_numexpr(operator.add, "+", array2, array2, "evaluate")
assert not result
# ok, we only check on first part of expression
result = expr._can_use_numexpr(operator.add, "+", array, array2, "evaluate")
assert result
@pytest.mark.filterwarnings("ignore:invalid value encountered in:RuntimeWarning")
@pytest.mark.parametrize(
"opname,op_str",
[("add", "+"), ("sub", "-"), ("mul", "*"), ("truediv", "/"), ("pow", "**")],
)
@pytest.mark.parametrize(
"left_fix,right_fix", [("_array", "_array2"), ("_array_mixed", "_array_mixed2")]
)
def test_binary_ops(self, request, opname, op_str, left_fix, right_fix):
left = request.getfixturevalue(left_fix)
right = request.getfixturevalue(right_fix)
def testit(left, right, opname, op_str):
if opname == "pow":
left = np.abs(left)
op = getattr(operator, opname)
# array has 0s
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
with option_context("compute.use_numexpr", False):
testit(left, right, opname, op_str)
expr.set_numexpr_threads(1)
testit(left, right, opname, op_str)
expr.set_numexpr_threads()
testit(left, right, opname, op_str)
@pytest.mark.parametrize(
"left_fix,right_fix", [("_array", "_array2"), ("_array_mixed", "_array_mixed2")]
)
def test_comparison_ops(self, request, comparison_op, left_fix, right_fix):
left = request.getfixturevalue(left_fix)
right = request.getfixturevalue(right_fix)
def testit():
f12 = left + 1
f22 = right + 1
op = comparison_op
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, right, f22, "evaluate")
assert not result
with option_context("compute.use_numexpr", False):
testit()
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
@pytest.mark.parametrize("cond", [True, False])
@pytest.mark.parametrize("fixture", ["_frame", "_frame2", "_mixed", "_mixed2"])
def test_where(self, request, cond, fixture):
df = request.getfixturevalue(fixture)
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)
with option_context("compute.use_numexpr", False):
testit()
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
@pytest.mark.parametrize(
"op_str,opname", [("/", "truediv"), ("//", "floordiv"), ("**", "pow")]
)
def test_bool_ops_raise_on_arithmetic(self, op_str, opname):
df = DataFrame(
{
"a": np.random.default_rng(2).random(10) > 0.5,
"b": np.random.default_rng(2).random(10) > 0.5,
}
)
msg = f"operator '{opname}' not implemented for bool dtypes"
f = getattr(operator, opname)
err_msg = re.escape(msg)
with pytest.raises(NotImplementedError, match=err_msg):
f(df, df)
with pytest.raises(NotImplementedError, match=err_msg):
f(df.a, df.b)
with pytest.raises(NotImplementedError, match=err_msg):
f(df.a, True)
with pytest.raises(NotImplementedError, match=err_msg):
f(False, df.a)
with pytest.raises(NotImplementedError, match=err_msg):
f(False, df)
with pytest.raises(NotImplementedError, match=err_msg):
f(df, True)
@pytest.mark.parametrize(
"op_str,opname", [("+", "add"), ("*", "mul"), ("-", "sub")]
)
def test_bool_ops_warn_on_arithmetic(self, op_str, opname):
n = 10
df = DataFrame(
{
"a": np.random.default_rng(2).random(n) > 0.5,
"b": np.random.default_rng(2).random(n) > 0.5,
}
)
subs = {"+": "|", "*": "&", "-": "^"}
sub_funcs = {"|": "or_", "&": "and_", "^": "xor"}
f = getattr(operator, opname)
fe = getattr(operator, sub_funcs[subs[op_str]])
if op_str == "-":
# raises TypeError
return
with tm.use_numexpr(True, min_elements=5):
with tm.assert_produces_warning():
r = f(df, df)
e = fe(df, df)
tm.assert_frame_equal(r, e)
with tm.assert_produces_warning():
r = f(df.a, df.b)
e = fe(df.a, df.b)
tm.assert_series_equal(r, e)
with tm.assert_produces_warning():
r = f(df.a, True)
e = fe(df.a, True)
tm.assert_series_equal(r, e)
with tm.assert_produces_warning():
r = f(False, df.a)
e = fe(False, df.a)
tm.assert_series_equal(r, e)
with tm.assert_produces_warning():
r = f(False, df)
e = fe(False, df)
tm.assert_frame_equal(r, e)
with tm.assert_produces_warning():
r = f(df, True)
e = fe(df, True)
tm.assert_frame_equal(r, e)
@pytest.mark.parametrize(
"test_input,expected",
[
(
DataFrame(
[[0, 1, 2, "aa"], [0, 1, 2, "aa"]], columns=["a", "b", "c", "dtype"]
),
DataFrame([[False, False], [False, False]], columns=["a", "dtype"]),
),
(
DataFrame(
[[0, 3, 2, "aa"], [0, 4, 2, "aa"], [0, 1, 1, "bb"]],
columns=["a", "b", "c", "dtype"],
),
DataFrame(
[[False, False], [False, False], [False, False]],
columns=["a", "dtype"],
),
),
],
)
def test_bool_ops_column_name_dtype(self, test_input, expected):
# GH 22383 - .ne fails if columns containing column name 'dtype'
result = test_input.loc[:, ["a", "dtype"]].ne(test_input.loc[:, ["a", "dtype"]])
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"arith", ("add", "sub", "mul", "mod", "truediv", "floordiv")
)
@pytest.mark.parametrize("axis", (0, 1))
def test_frame_series_axis(self, axis, arith, _frame, monkeypatch):
# GH#26736 Dataframe.floordiv(Series, axis=1) fails
df = _frame
if axis == 1:
other = df.iloc[0, :]
else:
other = df.iloc[:, 0]
with monkeypatch.context() as m:
m.setattr(expr, "_MIN_ELEMENTS", 0)
op_func = getattr(df, arith)
with option_context("compute.use_numexpr", False):
expected = op_func(other, axis=axis)
result = op_func(other, axis=axis)
tm.assert_frame_equal(expected, result)
@pytest.mark.parametrize(
"op",
[
"__mod__",
"__rmod__",
"__floordiv__",
"__rfloordiv__",
],
)
@pytest.mark.parametrize("box", [DataFrame, Series, Index])
@pytest.mark.parametrize("scalar", [-5, 5])
def test_python_semantics_with_numexpr_installed(
self, op, box, scalar, monkeypatch
):
# https://github.com/pandas-dev/pandas/issues/36047
with monkeypatch.context() as m:
m.setattr(expr, "_MIN_ELEMENTS", 0)
data = np.arange(-50, 50)
obj = box(data)
method = getattr(obj, op)
result = method(scalar)
# compare result with numpy
with option_context("compute.use_numexpr", False):
expected = method(scalar)
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
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