300 lines
11 KiB
Python
300 lines
11 KiB
Python
from __future__ import annotations
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from typing import final
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import numpy as np
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import pytest
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from pandas._config import using_pyarrow_string_dtype
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from pandas.core.dtypes.common import is_string_dtype
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import pandas as pd
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import pandas._testing as tm
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from pandas.core import ops
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class BaseOpsUtil:
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series_scalar_exc: type[Exception] | None = TypeError
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frame_scalar_exc: type[Exception] | None = TypeError
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series_array_exc: type[Exception] | None = TypeError
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divmod_exc: type[Exception] | None = TypeError
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def _get_expected_exception(
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self, op_name: str, obj, other
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) -> type[Exception] | None:
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# Find the Exception, if any we expect to raise calling
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# obj.__op_name__(other)
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# The self.obj_bar_exc pattern isn't great in part because it can depend
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# on op_name or dtypes, but we use it here for backward-compatibility.
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if op_name in ["__divmod__", "__rdivmod__"]:
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result = self.divmod_exc
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elif isinstance(obj, pd.Series) and isinstance(other, pd.Series):
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result = self.series_array_exc
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elif isinstance(obj, pd.Series):
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result = self.series_scalar_exc
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else:
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result = self.frame_scalar_exc
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if using_pyarrow_string_dtype() and result is not None:
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import pyarrow as pa
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result = ( # type: ignore[assignment]
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result,
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pa.lib.ArrowNotImplementedError,
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NotImplementedError,
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)
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return result
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def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
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# In _check_op we check that the result of a pointwise operation
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# (found via _combine) matches the result of the vectorized
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# operation obj.__op_name__(other).
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# In some cases pandas dtype inference on the scalar result may not
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# give a matching dtype even if both operations are behaving "correctly".
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# In these cases, do extra required casting here.
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return pointwise_result
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def get_op_from_name(self, op_name: str):
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return tm.get_op_from_name(op_name)
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# Subclasses are not expected to need to override check_opname, _check_op,
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# _check_divmod_op, or _combine.
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# Ideally any relevant overriding can be done in _cast_pointwise_result,
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# get_op_from_name, and the specification of `exc`. If you find a use
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# case that still requires overriding _check_op or _combine, please let
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# us know at github.com/pandas-dev/pandas/issues
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@final
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def check_opname(self, ser: pd.Series, op_name: str, other):
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exc = self._get_expected_exception(op_name, ser, other)
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op = self.get_op_from_name(op_name)
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self._check_op(ser, op, other, op_name, exc)
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# see comment on check_opname
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@final
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def _combine(self, obj, other, op):
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if isinstance(obj, pd.DataFrame):
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if len(obj.columns) != 1:
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raise NotImplementedError
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expected = obj.iloc[:, 0].combine(other, op).to_frame()
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else:
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expected = obj.combine(other, op)
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return expected
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# see comment on check_opname
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@final
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def _check_op(
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self, ser: pd.Series, op, other, op_name: str, exc=NotImplementedError
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):
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# Check that the Series/DataFrame arithmetic/comparison method matches
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# the pointwise result from _combine.
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if exc is None:
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result = op(ser, other)
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expected = self._combine(ser, other, op)
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expected = self._cast_pointwise_result(op_name, ser, other, expected)
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assert isinstance(result, type(ser))
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tm.assert_equal(result, expected)
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else:
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with pytest.raises(exc):
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op(ser, other)
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# see comment on check_opname
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@final
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def _check_divmod_op(self, ser: pd.Series, op, other):
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# check that divmod behavior matches behavior of floordiv+mod
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if op is divmod:
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exc = self._get_expected_exception("__divmod__", ser, other)
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else:
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exc = self._get_expected_exception("__rdivmod__", ser, other)
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if exc is None:
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result_div, result_mod = op(ser, other)
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if op is divmod:
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expected_div, expected_mod = ser // other, ser % other
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else:
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expected_div, expected_mod = other // ser, other % ser
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tm.assert_series_equal(result_div, expected_div)
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tm.assert_series_equal(result_mod, expected_mod)
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else:
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with pytest.raises(exc):
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divmod(ser, other)
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class BaseArithmeticOpsTests(BaseOpsUtil):
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"""
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Various Series and DataFrame arithmetic ops methods.
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Subclasses supporting various ops should set the class variables
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to indicate that they support ops of that kind
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* series_scalar_exc = TypeError
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* frame_scalar_exc = TypeError
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* series_array_exc = TypeError
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* divmod_exc = TypeError
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"""
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series_scalar_exc: type[Exception] | None = TypeError
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frame_scalar_exc: type[Exception] | None = TypeError
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series_array_exc: type[Exception] | None = TypeError
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divmod_exc: type[Exception] | None = TypeError
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def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
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# series & scalar
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if all_arithmetic_operators == "__rmod__" and is_string_dtype(data.dtype):
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pytest.skip("Skip testing Python string formatting")
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op_name = all_arithmetic_operators
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ser = pd.Series(data)
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self.check_opname(ser, op_name, ser.iloc[0])
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def test_arith_frame_with_scalar(self, data, all_arithmetic_operators):
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# frame & scalar
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if all_arithmetic_operators == "__rmod__" and is_string_dtype(data.dtype):
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pytest.skip("Skip testing Python string formatting")
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op_name = all_arithmetic_operators
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df = pd.DataFrame({"A": data})
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self.check_opname(df, op_name, data[0])
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def test_arith_series_with_array(self, data, all_arithmetic_operators):
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# ndarray & other series
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op_name = all_arithmetic_operators
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ser = pd.Series(data)
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self.check_opname(ser, op_name, pd.Series([ser.iloc[0]] * len(ser)))
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def test_divmod(self, data):
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ser = pd.Series(data)
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self._check_divmod_op(ser, divmod, 1)
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self._check_divmod_op(1, ops.rdivmod, ser)
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def test_divmod_series_array(self, data, data_for_twos):
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ser = pd.Series(data)
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self._check_divmod_op(ser, divmod, data)
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other = data_for_twos
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self._check_divmod_op(other, ops.rdivmod, ser)
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other = pd.Series(other)
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self._check_divmod_op(other, ops.rdivmod, ser)
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def test_add_series_with_extension_array(self, data):
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# Check adding an ExtensionArray to a Series of the same dtype matches
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# the behavior of adding the arrays directly and then wrapping in a
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# Series.
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ser = pd.Series(data)
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exc = self._get_expected_exception("__add__", ser, data)
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if exc is not None:
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with pytest.raises(exc):
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ser + data
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return
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result = ser + data
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expected = pd.Series(data + data)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("box", [pd.Series, pd.DataFrame, pd.Index])
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@pytest.mark.parametrize(
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"op_name",
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[
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x
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for x in tm.arithmetic_dunder_methods + tm.comparison_dunder_methods
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if not x.startswith("__r")
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],
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)
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def test_direct_arith_with_ndframe_returns_not_implemented(
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self, data, box, op_name
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):
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# EAs should return NotImplemented for ops with Series/DataFrame/Index
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# Pandas takes care of unboxing the series and calling the EA's op.
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other = box(data)
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if hasattr(data, op_name):
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result = getattr(data, op_name)(other)
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assert result is NotImplemented
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class BaseComparisonOpsTests(BaseOpsUtil):
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"""Various Series and DataFrame comparison ops methods."""
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def _compare_other(self, ser: pd.Series, data, op, other):
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if op.__name__ in ["eq", "ne"]:
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# comparison should match point-wise comparisons
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result = op(ser, other)
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expected = ser.combine(other, op)
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expected = self._cast_pointwise_result(op.__name__, ser, other, expected)
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tm.assert_series_equal(result, expected)
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else:
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exc = None
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try:
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result = op(ser, other)
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except Exception as err:
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exc = err
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if exc is None:
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# Didn't error, then should match pointwise behavior
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expected = ser.combine(other, op)
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expected = self._cast_pointwise_result(
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op.__name__, ser, other, expected
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)
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tm.assert_series_equal(result, expected)
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else:
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with pytest.raises(type(exc)):
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ser.combine(other, op)
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def test_compare_scalar(self, data, comparison_op):
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ser = pd.Series(data)
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self._compare_other(ser, data, comparison_op, 0)
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def test_compare_array(self, data, comparison_op):
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ser = pd.Series(data)
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other = pd.Series([data[0]] * len(data), dtype=data.dtype)
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self._compare_other(ser, data, comparison_op, other)
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class BaseUnaryOpsTests(BaseOpsUtil):
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def test_invert(self, data):
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ser = pd.Series(data, name="name")
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try:
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# 10 is an arbitrary choice here, just avoid iterating over
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# the whole array to trim test runtime
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[~x for x in data[:10]]
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except TypeError:
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# scalars don't support invert -> we don't expect the vectorized
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# operation to succeed
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with pytest.raises(TypeError):
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~ser
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with pytest.raises(TypeError):
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~data
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else:
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# Note we do not reuse the pointwise result to construct expected
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# because python semantics for negating bools are weird see GH#54569
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result = ~ser
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expected = pd.Series(~data, name="name")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("ufunc", [np.positive, np.negative, np.abs])
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def test_unary_ufunc_dunder_equivalence(self, data, ufunc):
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# the dunder __pos__ works if and only if np.positive works,
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# same for __neg__/np.negative and __abs__/np.abs
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attr = {np.positive: "__pos__", np.negative: "__neg__", np.abs: "__abs__"}[
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ufunc
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]
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exc = None
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try:
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result = getattr(data, attr)()
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except Exception as err:
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exc = err
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# if __pos__ raised, then so should the ufunc
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with pytest.raises((type(exc), TypeError)):
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ufunc(data)
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else:
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alt = ufunc(data)
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tm.assert_extension_array_equal(result, alt)
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