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303 lines
9.2 KiB
303 lines
9.2 KiB
import operator
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import numpy as np
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import pytest
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from pandas.compat import np_version_under1p20
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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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import pandas.core.ops as ops
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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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[("add", [1, 3, None, None, 9]), ("mul", [0, 2, None, None, 20])],
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ids=["add", "mul"],
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)
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def test_add_mul(dtype, opname, exp):
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a = pd.array([0, 1, None, 3, 4], dtype=dtype)
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b = pd.array([1, 2, 3, None, 5], dtype=dtype)
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# array / array
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expected = pd.array(exp, dtype=dtype)
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op = getattr(operator, opname)
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result = op(a, b)
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tm.assert_extension_array_equal(result, expected)
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op = getattr(ops, "r" + opname)
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result = op(a, b)
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tm.assert_extension_array_equal(result, expected)
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def test_sub(dtype):
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a = pd.array([1, 2, 3, None, 5], dtype=dtype)
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b = pd.array([0, 1, None, 3, 4], dtype=dtype)
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result = a - b
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expected = pd.array([1, 1, None, None, 1], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_div(dtype):
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a = pd.array([1, 2, 3, None, 5], dtype=dtype)
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b = pd.array([0, 1, None, 3, 4], dtype=dtype)
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result = a / b
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expected = pd.array([np.inf, 2, None, None, 1.25], dtype="Float64")
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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(zero, negative):
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# https://github.com/pandas-dev/pandas/issues/27398, GH#22793
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a = pd.array([0, 1, -1, None], dtype="Int64")
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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, 1], dtype="float64"),
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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_floordiv(dtype):
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a = pd.array([1, 2, 3, None, 5], dtype=dtype)
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b = pd.array([0, 1, None, 3, 4], dtype=dtype)
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result = a // b
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# Series op sets 1//0 to np.inf, which IntegerArray does not do (yet)
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expected = pd.array([0, 2, None, None, 1], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_mod(dtype):
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a = pd.array([1, 2, 3, None, 5], dtype=dtype)
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b = pd.array([0, 1, None, 3, 4], dtype=dtype)
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result = a % b
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expected = pd.array([0, 0, None, None, 1], dtype=dtype)
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tm.assert_extension_array_equal(result, expected)
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def test_pow_scalar():
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a = pd.array([-1, 0, 1, None, 2], dtype="Int64")
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result = a**0
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expected = pd.array([1, 1, 1, 1, 1], dtype="Int64")
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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="Int64")
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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="Int64")
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tm.assert_extension_array_equal(result, expected)
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result = a**np.nan
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expected = FloatingArray(
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np.array([np.nan, np.nan, 1, np.nan, np.nan], dtype="float64"),
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np.array([False, False, False, True, False]),
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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="Int64")
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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="Int64")
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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="Int64")
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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="float64"),
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np.array([False, False, True, False]),
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)
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tm.assert_extension_array_equal(result, expected)
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def test_pow_array():
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a = pd.array([0, 0, 0, 1, 1, 1, None, None, None])
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b = pd.array([0, 1, None, 0, 1, None, 0, 1, None])
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result = a**b
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expected = pd.array([1, 0, None, 1, 1, 1, 1, None, None])
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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="Int64")
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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_numpy_zero_dim_ndarray(other):
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arr = pd.array([1, None, 2])
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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 = (
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r"(:?can only perform ops with numeric values)"
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r"|(:?IntegerArray cannot perform the operation mod)"
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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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]
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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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# TODO test unsigned overflow
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def test_arith_coerce_scalar(data, all_arithmetic_operators):
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op = tm.get_op_from_name(all_arithmetic_operators)
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s = pd.Series(data)
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other = 0.01
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result = op(s, other)
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expected = op(s.astype(float), other)
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expected = expected.astype("Float64")
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# rfloordiv results in nan instead of inf
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if all_arithmetic_operators == "__rfloordiv__" and np_version_under1p20:
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# for numpy 1.20 https://github.com/numpy/numpy/pull/16161
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# updated floordiv, now matches our behavior defined in core.ops
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mask = (
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((expected == np.inf) | (expected == -np.inf)).fillna(False).to_numpy(bool)
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)
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expected.array._data[mask] = np.nan
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# rmod results in NaN that wasn't NA in original nullable Series -> unmask it
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elif all_arithmetic_operators == "__rmod__":
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mask = (s == 0).fillna(False).to_numpy(bool)
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expected.array._mask[mask] = False
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("other", [1.0, np.array(1.0)])
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def test_arithmetic_conversion(all_arithmetic_operators, other):
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# if we have a float operand we should have a float result
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# if that is equal to an integer
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op = tm.get_op_from_name(all_arithmetic_operators)
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s = pd.Series([1, 2, 3], dtype="Int64")
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result = op(s, other)
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assert result.dtype == "Float64"
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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.Series([1, 2, np.nan], dtype="Int64"),
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"B": pd.Series([1, np.nan, 3], dtype="UInt8"),
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"C": [1, 2, 3],
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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="Int64")
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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="Int64")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("op", ["mean"])
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def test_reduce_to_float(op):
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# some reduce ops always return float, even if the result
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# is a rounded number
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df = pd.DataFrame(
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{
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"A": ["a", "b", "b"],
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"B": [1, None, 3],
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"C": pd.array([1, None, 3], dtype="Int64"),
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}
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)
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# op
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result = getattr(df.C, op)()
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assert isinstance(result, float)
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# groupby
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result = getattr(df.groupby("A"), op)()
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expected = pd.DataFrame(
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{"B": np.array([1.0, 3.0]), "C": pd.array([1, 3], dtype="Float64")},
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index=pd.Index(["a", "b"], name="A"),
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)
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tm.assert_frame_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, 2, 3], [-1, -2, -3], [1, 2, 3]),
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([1, 2, None], [-1, -2, None], [1, 2, None]),
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([-1, 0, 1], [1, 0, -1], [1, 0, 1]),
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],
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)
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def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
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dtype = any_signed_int_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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