461 lines
14 KiB
Python
461 lines
14 KiB
Python
from collections import deque
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import re
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import string
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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import pandas as pd
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import pandas._testing as tm
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from pandas.arrays import SparseArray
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@pytest.fixture(params=[np.add, np.logaddexp])
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def ufunc(request):
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# dunder op
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return request.param
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@pytest.fixture(params=[True, False], ids=["sparse", "dense"])
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def sparse(request):
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return request.param
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@pytest.fixture
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def arrays_for_binary_ufunc():
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"""
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A pair of random, length-100 integer-dtype arrays, that are mostly 0.
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"""
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a1 = np.random.default_rng(2).integers(0, 10, 100, dtype="int64")
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a2 = np.random.default_rng(2).integers(0, 10, 100, dtype="int64")
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a1[::3] = 0
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a2[::4] = 0
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return a1, a2
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@pytest.mark.parametrize("ufunc", [np.positive, np.floor, np.exp])
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def test_unary_ufunc(ufunc, sparse):
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# Test that ufunc(pd.Series) == pd.Series(ufunc)
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arr = np.random.default_rng(2).integers(0, 10, 10, dtype="int64")
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arr[::2] = 0
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if sparse:
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arr = SparseArray(arr, dtype=pd.SparseDtype("int64", 0))
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index = list(string.ascii_letters[:10])
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name = "name"
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series = pd.Series(arr, index=index, name=name)
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result = ufunc(series)
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expected = pd.Series(ufunc(arr), index=index, name=name)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
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def test_binary_ufunc_with_array(flip, sparse, ufunc, arrays_for_binary_ufunc):
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# Test that ufunc(pd.Series(a), array) == pd.Series(ufunc(a, b))
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a1, a2 = arrays_for_binary_ufunc
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if sparse:
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a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
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a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
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name = "name" # op(pd.Series, array) preserves the name.
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series = pd.Series(a1, name=name)
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other = a2
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array_args = (a1, a2)
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series_args = (series, other) # ufunc(series, array)
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if flip:
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array_args = reversed(array_args)
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series_args = reversed(series_args) # ufunc(array, series)
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expected = pd.Series(ufunc(*array_args), name=name)
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result = ufunc(*series_args)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
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def test_binary_ufunc_with_index(flip, sparse, ufunc, arrays_for_binary_ufunc):
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# Test that
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# * func(pd.Series(a), pd.Series(b)) == pd.Series(ufunc(a, b))
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# * ufunc(Index, pd.Series) dispatches to pd.Series (returns a pd.Series)
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a1, a2 = arrays_for_binary_ufunc
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if sparse:
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a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
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a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
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name = "name" # op(pd.Series, array) preserves the name.
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series = pd.Series(a1, name=name)
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other = pd.Index(a2, name=name).astype("int64")
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array_args = (a1, a2)
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series_args = (series, other) # ufunc(series, array)
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if flip:
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array_args = reversed(array_args)
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series_args = reversed(series_args) # ufunc(array, series)
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expected = pd.Series(ufunc(*array_args), name=name)
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result = ufunc(*series_args)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("shuffle", [True, False], ids=["unaligned", "aligned"])
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@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
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def test_binary_ufunc_with_series(
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flip, shuffle, sparse, ufunc, arrays_for_binary_ufunc
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):
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# Test that
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# * func(pd.Series(a), pd.Series(b)) == pd.Series(ufunc(a, b))
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# with alignment between the indices
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a1, a2 = arrays_for_binary_ufunc
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if sparse:
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a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
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a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
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name = "name" # op(pd.Series, array) preserves the name.
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series = pd.Series(a1, name=name)
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other = pd.Series(a2, name=name)
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idx = np.random.default_rng(2).permutation(len(a1))
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if shuffle:
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other = other.take(idx)
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if flip:
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index = other.align(series)[0].index
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else:
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index = series.align(other)[0].index
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else:
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index = series.index
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array_args = (a1, a2)
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series_args = (series, other) # ufunc(series, array)
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if flip:
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array_args = tuple(reversed(array_args))
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series_args = tuple(reversed(series_args)) # ufunc(array, series)
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expected = pd.Series(ufunc(*array_args), index=index, name=name)
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result = ufunc(*series_args)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("flip", [True, False])
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def test_binary_ufunc_scalar(ufunc, sparse, flip, arrays_for_binary_ufunc):
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# Test that
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# * ufunc(pd.Series, scalar) == pd.Series(ufunc(array, scalar))
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# * ufunc(pd.Series, scalar) == ufunc(scalar, pd.Series)
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arr, _ = arrays_for_binary_ufunc
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if sparse:
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arr = SparseArray(arr)
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other = 2
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series = pd.Series(arr, name="name")
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series_args = (series, other)
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array_args = (arr, other)
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if flip:
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series_args = tuple(reversed(series_args))
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array_args = tuple(reversed(array_args))
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expected = pd.Series(ufunc(*array_args), name="name")
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result = ufunc(*series_args)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("ufunc", [np.divmod]) # TODO: np.modf, np.frexp
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@pytest.mark.parametrize("shuffle", [True, False])
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@pytest.mark.filterwarnings("ignore:divide by zero:RuntimeWarning")
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def test_multiple_output_binary_ufuncs(ufunc, sparse, shuffle, arrays_for_binary_ufunc):
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# Test that
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# the same conditions from binary_ufunc_scalar apply to
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# ufuncs with multiple outputs.
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a1, a2 = arrays_for_binary_ufunc
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# work around https://github.com/pandas-dev/pandas/issues/26987
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a1[a1 == 0] = 1
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a2[a2 == 0] = 1
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if sparse:
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a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
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a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
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s1 = pd.Series(a1)
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s2 = pd.Series(a2)
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if shuffle:
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# ensure we align before applying the ufunc
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s2 = s2.sample(frac=1)
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expected = ufunc(a1, a2)
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assert isinstance(expected, tuple)
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result = ufunc(s1, s2)
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assert isinstance(result, tuple)
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tm.assert_series_equal(result[0], pd.Series(expected[0]))
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tm.assert_series_equal(result[1], pd.Series(expected[1]))
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def test_multiple_output_ufunc(sparse, arrays_for_binary_ufunc):
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# Test that the same conditions from unary input apply to multi-output
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# ufuncs
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arr, _ = arrays_for_binary_ufunc
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if sparse:
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arr = SparseArray(arr)
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series = pd.Series(arr, name="name")
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result = np.modf(series)
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expected = np.modf(arr)
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assert isinstance(result, tuple)
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assert isinstance(expected, tuple)
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tm.assert_series_equal(result[0], pd.Series(expected[0], name="name"))
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tm.assert_series_equal(result[1], pd.Series(expected[1], name="name"))
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def test_binary_ufunc_drops_series_name(ufunc, sparse, arrays_for_binary_ufunc):
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# Drop the names when they differ.
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a1, a2 = arrays_for_binary_ufunc
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s1 = pd.Series(a1, name="a")
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s2 = pd.Series(a2, name="b")
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result = ufunc(s1, s2)
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assert result.name is None
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def test_object_series_ok():
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class Dummy:
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def __init__(self, value) -> None:
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self.value = value
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def __add__(self, other):
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return self.value + other.value
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arr = np.array([Dummy(0), Dummy(1)])
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ser = pd.Series(arr)
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tm.assert_series_equal(np.add(ser, ser), pd.Series(np.add(ser, arr)))
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tm.assert_series_equal(np.add(ser, Dummy(1)), pd.Series(np.add(ser, Dummy(1))))
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@pytest.fixture(
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params=[
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pd.array([1, 3, 2], dtype=np.int64),
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pd.array([1, 3, 2], dtype="Int64"),
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pd.array([1, 3, 2], dtype="Float32"),
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pd.array([1, 10, 2], dtype="Sparse[int]"),
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pd.to_datetime(["2000", "2010", "2001"]),
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pd.to_datetime(["2000", "2010", "2001"]).tz_localize("CET"),
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pd.to_datetime(["2000", "2010", "2001"]).to_period(freq="D"),
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pd.to_timedelta(["1 Day", "3 Days", "2 Days"]),
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pd.IntervalIndex([pd.Interval(0, 1), pd.Interval(2, 3), pd.Interval(1, 2)]),
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],
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ids=lambda x: str(x.dtype),
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)
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def values_for_np_reduce(request):
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# min/max tests assume that these are monotonic increasing
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return request.param
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class TestNumpyReductions:
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# TODO: cases with NAs, axis kwarg for DataFrame
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def test_multiply(self, values_for_np_reduce, box_with_array, request):
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box = box_with_array
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values = values_for_np_reduce
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with tm.assert_produces_warning(None):
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obj = box(values)
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if isinstance(values, pd.core.arrays.SparseArray):
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mark = pytest.mark.xfail(reason="SparseArray has no 'prod'")
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request.applymarker(mark)
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if values.dtype.kind in "iuf":
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result = np.multiply.reduce(obj)
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if box is pd.DataFrame:
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expected = obj.prod(numeric_only=False)
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tm.assert_series_equal(result, expected)
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elif box is pd.Index:
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# Index has no 'prod'
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expected = obj._values.prod()
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assert result == expected
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else:
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expected = obj.prod()
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assert result == expected
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else:
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msg = "|".join(
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[
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"does not support reduction",
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"unsupported operand type",
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"ufunc 'multiply' cannot use operands",
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]
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)
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with pytest.raises(TypeError, match=msg):
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np.multiply.reduce(obj)
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def test_add(self, values_for_np_reduce, box_with_array):
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box = box_with_array
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values = values_for_np_reduce
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with tm.assert_produces_warning(None):
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obj = box(values)
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if values.dtype.kind in "miuf":
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result = np.add.reduce(obj)
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if box is pd.DataFrame:
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expected = obj.sum(numeric_only=False)
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tm.assert_series_equal(result, expected)
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elif box is pd.Index:
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# Index has no 'sum'
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expected = obj._values.sum()
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assert result == expected
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else:
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expected = obj.sum()
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assert result == expected
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else:
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msg = "|".join(
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[
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"does not support reduction",
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"unsupported operand type",
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"ufunc 'add' cannot use operands",
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]
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)
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with pytest.raises(TypeError, match=msg):
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np.add.reduce(obj)
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def test_max(self, values_for_np_reduce, box_with_array):
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box = box_with_array
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values = values_for_np_reduce
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same_type = True
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if box is pd.Index and values.dtype.kind in ["i", "f"]:
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# ATM Index casts to object, so we get python ints/floats
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same_type = False
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with tm.assert_produces_warning(None):
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obj = box(values)
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result = np.maximum.reduce(obj)
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if box is pd.DataFrame:
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# TODO: cases with axis kwarg
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expected = obj.max(numeric_only=False)
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tm.assert_series_equal(result, expected)
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else:
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expected = values[1]
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assert result == expected
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if same_type:
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# check we have e.g. Timestamp instead of dt64
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assert type(result) == type(expected)
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def test_min(self, values_for_np_reduce, box_with_array):
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box = box_with_array
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values = values_for_np_reduce
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same_type = True
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if box is pd.Index and values.dtype.kind in ["i", "f"]:
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# ATM Index casts to object, so we get python ints/floats
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same_type = False
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with tm.assert_produces_warning(None):
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obj = box(values)
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result = np.minimum.reduce(obj)
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if box is pd.DataFrame:
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expected = obj.min(numeric_only=False)
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tm.assert_series_equal(result, expected)
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else:
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expected = values[0]
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assert result == expected
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if same_type:
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# check we have e.g. Timestamp instead of dt64
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assert type(result) == type(expected)
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@pytest.mark.parametrize("type_", [list, deque, tuple])
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def test_binary_ufunc_other_types(type_):
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a = pd.Series([1, 2, 3], name="name")
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b = type_([3, 4, 5])
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result = np.add(a, b)
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expected = pd.Series(np.add(a.to_numpy(), b), name="name")
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tm.assert_series_equal(result, expected)
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def test_object_dtype_ok():
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class Thing:
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def __init__(self, value) -> None:
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self.value = value
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def __add__(self, other):
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other = getattr(other, "value", other)
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return type(self)(self.value + other)
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def __eq__(self, other) -> bool:
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return type(other) is Thing and self.value == other.value
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def __repr__(self) -> str:
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return f"Thing({self.value})"
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s = pd.Series([Thing(1), Thing(2)])
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result = np.add(s, Thing(1))
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expected = pd.Series([Thing(2), Thing(3)])
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tm.assert_series_equal(result, expected)
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def test_outer():
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# https://github.com/pandas-dev/pandas/issues/27186
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ser = pd.Series([1, 2, 3])
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obj = np.array([1, 2, 3])
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with pytest.raises(NotImplementedError, match=""):
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np.subtract.outer(ser, obj)
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def test_np_matmul():
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# GH26650
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df1 = pd.DataFrame(data=[[-1, 1, 10]])
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df2 = pd.DataFrame(data=[-1, 1, 10])
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expected = pd.DataFrame(data=[102])
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result = np.matmul(df1, df2)
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tm.assert_frame_equal(expected, result)
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def test_array_ufuncs_for_many_arguments():
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# GH39853
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def add3(x, y, z):
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return x + y + z
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ufunc = np.frompyfunc(add3, 3, 1)
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ser = pd.Series([1, 2])
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result = ufunc(ser, ser, 1)
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expected = pd.Series([3, 5], dtype=object)
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tm.assert_series_equal(result, expected)
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df = pd.DataFrame([[1, 2]])
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msg = (
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"Cannot apply ufunc <ufunc 'add3 (vectorized)'> "
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"to mixed DataFrame and Series inputs."
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)
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with pytest.raises(NotImplementedError, match=re.escape(msg)):
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ufunc(ser, ser, df)
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# TODO(CoW) see https://github.com/pandas-dev/pandas/pull/51082
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@td.skip_copy_on_write_not_yet_implemented
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def test_np_fix():
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# np.fix is not a ufunc but is composed of several ufunc calls under the hood
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# with `out` and `where` keywords
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ser = pd.Series([-1.5, -0.5, 0.5, 1.5])
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result = np.fix(ser)
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expected = pd.Series([-1.0, -0.0, 0.0, 1.0])
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tm.assert_series_equal(result, expected)
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