241 lines
8.1 KiB
Python
241 lines
8.1 KiB
Python
import operator
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import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays import FloatingArray
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# Basic test for the arithmetic array ops
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# -----------------------------------------------------------------------------
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@pytest.mark.parametrize(
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"opname, exp",
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[
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("add", [1.1, 2.2, None, None, 5.5]),
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("mul", [0.1, 0.4, None, None, 2.5]),
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("sub", [0.9, 1.8, None, None, 4.5]),
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("truediv", [10.0, 10.0, None, None, 10.0]),
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("floordiv", [9.0, 9.0, None, None, 10.0]),
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("mod", [0.1, 0.2, None, None, 0.0]),
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],
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ids=["add", "mul", "sub", "div", "floordiv", "mod"],
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)
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def test_array_op(dtype, opname, exp):
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a = pd.array([1.0, 2.0, None, 4.0, 5.0], dtype=dtype)
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b = pd.array([0.1, 0.2, 0.3, None, 0.5], dtype=dtype)
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op = getattr(operator, opname)
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result = op(a, b)
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expected = pd.array(exp, dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize("zero, negative", [(0, False), (0.0, False), (-0.0, True)])
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def test_divide_by_zero(dtype, zero, negative):
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# TODO pending NA/NaN discussion
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# https://github.com/pandas-dev/pandas/issues/32265/
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a = pd.array([0, 1, -1, None], dtype=dtype)
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result = a / zero
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expected = FloatingArray(
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np.array([np.nan, np.inf, -np.inf, np.nan], dtype=dtype.numpy_dtype),
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np.array([False, False, False, True]),
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)
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if negative:
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expected *= -1
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tm.assert_extension_array_equal(result, expected)
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def test_pow_scalar(dtype):
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a = pd.array([-1, 0, 1, None, 2], dtype=dtype)
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result = a**0
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expected = pd.array([1, 1, 1, 1, 1], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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result = a**1
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expected = pd.array([-1, 0, 1, None, 2], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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result = a**pd.NA
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expected = pd.array([None, None, 1, None, None], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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result = a**np.nan
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# TODO np.nan should be converted to pd.NA / missing before operation?
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expected = FloatingArray(
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np.array([np.nan, np.nan, 1, np.nan, np.nan], dtype=dtype.numpy_dtype),
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mask=a._mask,
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)
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tm.assert_extension_array_equal(result, expected)
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# reversed
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a = a[1:] # Can't raise integers to negative powers.
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result = 0**a
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expected = pd.array([1, 0, None, 0], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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result = 1**a
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expected = pd.array([1, 1, 1, 1], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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result = pd.NA**a
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expected = pd.array([1, None, None, None], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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result = np.nan**a
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expected = FloatingArray(
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np.array([1, np.nan, np.nan, np.nan], dtype=dtype.numpy_dtype), mask=a._mask
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)
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tm.assert_extension_array_equal(result, expected)
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def test_pow_array(dtype):
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a = pd.array([0, 0, 0, 1, 1, 1, None, None, None], dtype=dtype)
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b = pd.array([0, 1, None, 0, 1, None, 0, 1, None], dtype=dtype)
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result = a**b
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expected = pd.array([1, 0, None, 1, 1, 1, 1, None, None], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_rpow_one_to_na():
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# https://github.com/pandas-dev/pandas/issues/22022
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# https://github.com/pandas-dev/pandas/issues/29997
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arr = pd.array([np.nan, np.nan], dtype="Float64")
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result = np.array([1.0, 2.0]) ** arr
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expected = pd.array([1.0, np.nan], dtype="Float64")
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tm.assert_extension_array_equal(result, expected)
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@pytest.mark.parametrize("other", [0, 0.5])
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def test_arith_zero_dim_ndarray(other):
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arr = pd.array([1, None, 2], dtype="Float64")
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result = arr + np.array(other)
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expected = arr + other
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tm.assert_equal(result, expected)
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# Test generic characteristics / errors
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# -----------------------------------------------------------------------------
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def test_error_invalid_values(data, all_arithmetic_operators):
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op = all_arithmetic_operators
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s = pd.Series(data)
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ops = getattr(s, op)
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# invalid scalars
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msg = "|".join(
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[
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r"can only perform ops with numeric values",
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r"FloatingArray cannot perform the operation mod",
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"unsupported operand type",
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"not all arguments converted during string formatting",
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"can't multiply sequence by non-int of type 'float'",
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"ufunc 'subtract' cannot use operands with types dtype",
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r"can only concatenate str \(not \"float\"\) to str",
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"ufunc '.*' not supported for the input types, and the inputs could not",
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"ufunc '.*' did not contain a loop with signature matching types",
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"Concatenation operation is not implemented for NumPy arrays",
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"has no kernel",
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"not implemented",
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"not supported for dtype",
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"Can only string multiply by an integer",
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]
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)
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with pytest.raises(TypeError, match=msg):
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ops("foo")
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with pytest.raises(TypeError, match=msg):
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ops(pd.Timestamp("20180101"))
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# invalid array-likes
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with pytest.raises(TypeError, match=msg):
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ops(pd.Series("foo", index=s.index))
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msg = "|".join(
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[
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"can only perform ops with numeric values",
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"cannot perform .* with this index type: DatetimeArray",
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"Addition/subtraction of integers and integer-arrays "
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"with DatetimeArray is no longer supported. *",
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"unsupported operand type",
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"not all arguments converted during string formatting",
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"can't multiply sequence by non-int of type 'float'",
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"ufunc 'subtract' cannot use operands with types dtype",
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(
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"ufunc 'add' cannot use operands with types "
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rf"dtype\('{tm.ENDIAN}M8\[ns\]'\)"
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),
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r"ufunc 'add' cannot use operands with types dtype\('float\d{2}'\)",
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"cannot subtract DatetimeArray from ndarray",
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"has no kernel",
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"not implemented",
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"not supported for dtype",
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]
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)
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with pytest.raises(TypeError, match=msg):
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ops(pd.Series(pd.date_range("20180101", periods=len(s))))
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# Various
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# -----------------------------------------------------------------------------
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def test_cross_type_arithmetic():
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df = pd.DataFrame(
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{
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"A": pd.array([1, 2, np.nan], dtype="Float64"),
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"B": pd.array([1, np.nan, 3], dtype="Float32"),
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"C": np.array([1, 2, 3], dtype="float64"),
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}
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)
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result = df.A + df.C
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expected = pd.Series([2, 4, np.nan], dtype="Float64")
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tm.assert_series_equal(result, expected)
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result = (df.A + df.C) * 3 == 12
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expected = pd.Series([False, True, None], dtype="boolean")
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tm.assert_series_equal(result, expected)
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result = df.A + df.B
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expected = pd.Series([2, np.nan, np.nan], dtype="Float64")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"source, neg_target, abs_target",
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[
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([1.1, 2.2, 3.3], [-1.1, -2.2, -3.3], [1.1, 2.2, 3.3]),
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([1.1, 2.2, None], [-1.1, -2.2, None], [1.1, 2.2, None]),
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([-1.1, 0.0, 1.1], [1.1, 0.0, -1.1], [1.1, 0.0, 1.1]),
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],
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)
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def test_unary_float_operators(float_ea_dtype, source, neg_target, abs_target):
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# GH38794
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dtype = float_ea_dtype
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arr = pd.array(source, dtype=dtype)
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neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
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neg_target = pd.array(neg_target, dtype=dtype)
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abs_target = pd.array(abs_target, dtype=dtype)
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tm.assert_extension_array_equal(neg_result, neg_target)
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tm.assert_extension_array_equal(pos_result, arr)
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assert not tm.shares_memory(pos_result, arr)
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tm.assert_extension_array_equal(abs_result, abs_target)
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def test_bitwise(dtype):
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left = pd.array([1, None, 3, 4], dtype=dtype)
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right = pd.array([None, 3, 5, 4], dtype=dtype)
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with pytest.raises(TypeError, match="unsupported operand type"):
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left | right
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with pytest.raises(TypeError, match="unsupported operand type"):
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left & right
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with pytest.raises(TypeError, match="unsupported operand type"):
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left ^ right
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