409 lines
13 KiB
Python
409 lines
13 KiB
Python
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import operator
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import re
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import warnings
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import numpy as np
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import pytest
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from pandas import option_context
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import pandas._testing as tm
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from pandas.core.api import (
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DataFrame,
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Index,
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Series,
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)
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from pandas.core.computation import expressions as expr
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_frame = DataFrame(np.random.randn(10001, 4), columns=list("ABCD"), dtype="float64")
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_frame2 = DataFrame(np.random.randn(100, 4), columns=list("ABCD"), dtype="float64")
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_mixed = DataFrame(
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{
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"A": _frame["A"].copy(),
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"B": _frame["B"].astype("float32"),
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"C": _frame["C"].astype("int64"),
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"D": _frame["D"].astype("int32"),
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}
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)
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_mixed2 = DataFrame(
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{
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"A": _frame2["A"].copy(),
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"B": _frame2["B"].astype("float32"),
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"C": _frame2["C"].astype("int64"),
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"D": _frame2["D"].astype("int32"),
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}
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)
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_integer = DataFrame(
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np.random.randint(1, 100, size=(10001, 4)), columns=list("ABCD"), dtype="int64"
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)
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_integer2 = DataFrame(
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np.random.randint(1, 100, size=(101, 4)), columns=list("ABCD"), dtype="int64"
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)
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_array = _frame["A"].values.copy()
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_array2 = _frame2["A"].values.copy()
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_array_mixed = _mixed["D"].values.copy()
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_array_mixed2 = _mixed2["D"].values.copy()
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@pytest.mark.skipif(not expr.USE_NUMEXPR, reason="not using numexpr")
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class TestExpressions:
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def setup_method(self):
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self._MIN_ELEMENTS = expr._MIN_ELEMENTS
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def teardown_method(self):
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expr._MIN_ELEMENTS = self._MIN_ELEMENTS
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@staticmethod
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def call_op(df, other, flex: bool, opname: str):
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if flex:
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op = lambda x, y: getattr(x, opname)(y)
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op.__name__ = opname
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else:
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op = getattr(operator, opname)
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with option_context("compute.use_numexpr", False):
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expected = op(df, other)
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expr.get_test_result()
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result = op(df, other)
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return result, expected
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@pytest.mark.parametrize(
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"df",
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[
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_integer,
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_integer2,
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# randint to get a case with zeros
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_integer * np.random.randint(0, 2, size=np.shape(_integer)),
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_frame,
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_frame2,
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_mixed,
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_mixed2,
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],
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)
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@pytest.mark.parametrize("flex", [True, False])
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@pytest.mark.parametrize(
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"arith", ["add", "sub", "mul", "mod", "truediv", "floordiv"]
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)
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def test_run_arithmetic(self, df, flex, arith):
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expr._MIN_ELEMENTS = 0
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result, expected = self.call_op(df, df, flex, arith)
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if arith == "truediv":
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assert all(x.kind == "f" for x in expected.dtypes.values)
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tm.assert_equal(expected, result)
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for i in range(len(df.columns)):
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result, expected = self.call_op(df.iloc[:, i], df.iloc[:, i], flex, arith)
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if arith == "truediv":
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assert expected.dtype.kind == "f"
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tm.assert_equal(expected, result)
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@pytest.mark.parametrize(
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"df",
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[
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_integer,
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_integer2,
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# randint to get a case with zeros
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_integer * np.random.randint(0, 2, size=np.shape(_integer)),
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_frame,
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_frame2,
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_mixed,
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_mixed2,
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],
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)
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@pytest.mark.parametrize("flex", [True, False])
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def test_run_binary(self, df, flex, comparison_op):
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"""
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tests solely that the result is the same whether or not numexpr is
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enabled. Need to test whether the function does the correct thing
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elsewhere.
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"""
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arith = comparison_op.__name__
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with option_context("compute.use_numexpr", False):
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other = df.copy() + 1
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expr._MIN_ELEMENTS = 0
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expr.set_test_mode(True)
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result, expected = self.call_op(df, other, flex, arith)
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used_numexpr = expr.get_test_result()
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assert used_numexpr, "Did not use numexpr as expected."
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tm.assert_equal(expected, result)
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# FIXME: dont leave commented-out
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# series doesn't uses vec_compare instead of numexpr...
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# for i in range(len(df.columns)):
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# binary_comp = other.iloc[:, i] + 1
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# self.run_binary(df.iloc[:, i], binary_comp, flex)
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def test_invalid(self):
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array = np.random.randn(1_000_001)
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array2 = np.random.randn(100)
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# no op
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result = expr._can_use_numexpr(operator.add, None, array, array, "evaluate")
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assert not result
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# min elements
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result = expr._can_use_numexpr(operator.add, "+", array2, array2, "evaluate")
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assert not result
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# ok, we only check on first part of expression
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result = expr._can_use_numexpr(operator.add, "+", array, array2, "evaluate")
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assert result
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@pytest.mark.parametrize(
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"opname,op_str",
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[("add", "+"), ("sub", "-"), ("mul", "*"), ("truediv", "/"), ("pow", "**")],
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)
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@pytest.mark.parametrize(
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"left,right", [(_array, _array2), (_array_mixed, _array_mixed2)]
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)
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def test_binary_ops(self, opname, op_str, left, right):
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def testit():
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if opname == "pow":
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# TODO: get this working
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return
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op = getattr(operator, opname)
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with warnings.catch_warnings():
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# array has 0s
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msg = "invalid value encountered in true_divide"
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warnings.filterwarnings("ignore", msg, RuntimeWarning)
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result = expr.evaluate(op, left, left, use_numexpr=True)
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expected = expr.evaluate(op, left, left, use_numexpr=False)
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tm.assert_numpy_array_equal(result, expected)
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result = expr._can_use_numexpr(op, op_str, right, right, "evaluate")
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assert not result
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with option_context("compute.use_numexpr", False):
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testit()
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expr.set_numexpr_threads(1)
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testit()
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expr.set_numexpr_threads()
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testit()
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@pytest.mark.parametrize(
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"opname,op_str",
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[
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("gt", ">"),
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("lt", "<"),
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("ge", ">="),
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("le", "<="),
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("eq", "=="),
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("ne", "!="),
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],
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)
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@pytest.mark.parametrize(
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"left,right", [(_array, _array2), (_array_mixed, _array_mixed2)]
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)
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def test_comparison_ops(self, opname, op_str, left, right):
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def testit():
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f12 = left + 1
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f22 = right + 1
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op = getattr(operator, opname)
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result = expr.evaluate(op, left, f12, use_numexpr=True)
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expected = expr.evaluate(op, left, f12, use_numexpr=False)
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tm.assert_numpy_array_equal(result, expected)
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result = expr._can_use_numexpr(op, op_str, right, f22, "evaluate")
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assert not result
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with option_context("compute.use_numexpr", False):
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testit()
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expr.set_numexpr_threads(1)
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testit()
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expr.set_numexpr_threads()
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testit()
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@pytest.mark.parametrize("cond", [True, False])
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@pytest.mark.parametrize("df", [_frame, _frame2, _mixed, _mixed2])
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def test_where(self, cond, df):
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def testit():
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c = np.empty(df.shape, dtype=np.bool_)
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c.fill(cond)
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result = expr.where(c, df.values, df.values + 1)
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expected = np.where(c, df.values, df.values + 1)
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tm.assert_numpy_array_equal(result, expected)
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with option_context("compute.use_numexpr", False):
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testit()
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expr.set_numexpr_threads(1)
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testit()
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expr.set_numexpr_threads()
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testit()
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@pytest.mark.parametrize(
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"op_str,opname", [("/", "truediv"), ("//", "floordiv"), ("**", "pow")]
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)
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def test_bool_ops_raise_on_arithmetic(self, op_str, opname):
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df = DataFrame({"a": np.random.rand(10) > 0.5, "b": np.random.rand(10) > 0.5})
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msg = f"operator '{opname}' not implemented for bool dtypes"
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f = getattr(operator, opname)
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err_msg = re.escape(msg)
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with pytest.raises(NotImplementedError, match=err_msg):
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f(df, df)
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with pytest.raises(NotImplementedError, match=err_msg):
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f(df.a, df.b)
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with pytest.raises(NotImplementedError, match=err_msg):
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f(df.a, True)
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with pytest.raises(NotImplementedError, match=err_msg):
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f(False, df.a)
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with pytest.raises(NotImplementedError, match=err_msg):
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f(False, df)
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with pytest.raises(NotImplementedError, match=err_msg):
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f(df, True)
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@pytest.mark.parametrize(
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"op_str,opname", [("+", "add"), ("*", "mul"), ("-", "sub")]
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)
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def test_bool_ops_warn_on_arithmetic(self, op_str, opname):
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n = 10
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df = DataFrame({"a": np.random.rand(n) > 0.5, "b": np.random.rand(n) > 0.5})
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subs = {"+": "|", "*": "&", "-": "^"}
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sub_funcs = {"|": "or_", "&": "and_", "^": "xor"}
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f = getattr(operator, opname)
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fe = getattr(operator, sub_funcs[subs[op_str]])
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if op_str == "-":
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# raises TypeError
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return
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with tm.use_numexpr(True, min_elements=5):
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with tm.assert_produces_warning():
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r = f(df, df)
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e = fe(df, df)
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tm.assert_frame_equal(r, e)
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with tm.assert_produces_warning():
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r = f(df.a, df.b)
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e = fe(df.a, df.b)
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tm.assert_series_equal(r, e)
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with tm.assert_produces_warning():
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r = f(df.a, True)
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e = fe(df.a, True)
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tm.assert_series_equal(r, e)
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with tm.assert_produces_warning():
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r = f(False, df.a)
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e = fe(False, df.a)
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tm.assert_series_equal(r, e)
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with tm.assert_produces_warning():
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r = f(False, df)
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e = fe(False, df)
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tm.assert_frame_equal(r, e)
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with tm.assert_produces_warning():
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r = f(df, True)
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e = fe(df, True)
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tm.assert_frame_equal(r, e)
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@pytest.mark.parametrize(
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"test_input,expected",
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[
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(
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DataFrame(
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[[0, 1, 2, "aa"], [0, 1, 2, "aa"]], columns=["a", "b", "c", "dtype"]
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),
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DataFrame([[False, False], [False, False]], columns=["a", "dtype"]),
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),
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(
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DataFrame(
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[[0, 3, 2, "aa"], [0, 4, 2, "aa"], [0, 1, 1, "bb"]],
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columns=["a", "b", "c", "dtype"],
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),
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DataFrame(
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[[False, False], [False, False], [False, False]],
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columns=["a", "dtype"],
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),
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),
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],
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)
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def test_bool_ops_column_name_dtype(self, test_input, expected):
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# GH 22383 - .ne fails if columns containing column name 'dtype'
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result = test_input.loc[:, ["a", "dtype"]].ne(test_input.loc[:, ["a", "dtype"]])
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"arith", ("add", "sub", "mul", "mod", "truediv", "floordiv")
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)
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@pytest.mark.parametrize("axis", (0, 1))
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def test_frame_series_axis(self, axis, arith):
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# GH#26736 Dataframe.floordiv(Series, axis=1) fails
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df = _frame
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if axis == 1:
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other = df.iloc[0, :]
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else:
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other = df.iloc[:, 0]
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expr._MIN_ELEMENTS = 0
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op_func = getattr(df, arith)
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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):
|
||
|
|
# 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
|
||
|
|
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
|