Additional tests for ufunc(Series)

doc/source/whatsnew/v0.25.0.rst

@@ -773,7 +773,7 @@ Sparse
 - Bug in :class:`SparseFrame` constructor where passing ``None`` as the data would cause ``default_fill_value`` to be ignored (:issue:`16807`)
 - Bug in :class:`SparseDataFrame` when adding a column in which the length of values does not match length of index, ``AssertionError`` is raised instead of raising ``ValueError`` (:issue:`25484`)
 - Introduce a better error message in :meth:`Series.sparse.from_coo` so it returns a ``TypeError`` for inputs that are not coo matrices (:issue:`26554`)
-- Bug in :func:`numpy.modf` on a :class:`SparseArray`. Now a tuple of :class:`SparseArray` is returned.
+- Bug in :func:`numpy.modf` on a :class:`SparseArray`. Now a tuple of :class:`SparseArray` is returned (:issue:`26946`).
 
 Other
 ^^^^^

pandas/tests/series/test_ufunc.py

@@ -0,0 +1,253 @@
+import string
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas.util.testing as tm
+
+UNARY_UFUNCS = [np.positive, np.floor, np.exp]
+BINARY_UFUNCS = [
+    np.add,  # dunder op
+    np.logaddexp,
+]
+SPARSE = [
+    pytest.param(True,
+                 marks=pytest.mark.xfail(reason="Series.__array_ufunc__")),
+    False,
+]
+SPARSE_IDS = ['sparse', 'dense']
+SHUFFLE = [
+    pytest.param(True, marks=pytest.mark.xfail(reason="GH-26945",
+                                               strict=False)),
+    False
+]
+
+
+@pytest.fixture
+def arrays_for_binary_ufunc():
+    """
+    A pair of random, length-100 integer-dtype arrays, that are mostly 0.
+    """
+    a1 = np.random.randint(0, 10, 100, dtype='int64')
+    a2 = np.random.randint(0, 10, 100, dtype='int64')
+    a1[::3] = 0
+    a2[::4] = 0
+    return a1, a2
+
+
+@pytest.mark.parametrize("ufunc", UNARY_UFUNCS)
+@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
+def test_unary_ufunc(ufunc, sparse):
+    # Test that ufunc(Series) == Series(ufunc)
+    array = np.random.randint(0, 10, 10, dtype='int64')
+    array[::2] = 0
+    if sparse:
+        array = pd.SparseArray(array, dtype=pd.SparseDtype('int', 0))
+
+    index = list(string.ascii_letters[:10])
+    name = "name"
+    series = pd.Series(array, index=index, name=name)
+
+    result = ufunc(series)
+    expected = pd.Series(ufunc(array), index=index, name=name)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
+@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
+@pytest.mark.parametrize("flip", [True, False], ids=['flipped', 'straight'])
+def test_binary_ufunc_with_array(flip, sparse, ufunc, arrays_for_binary_ufunc):
+    # Test that ufunc(Series(a), array) == Series(ufunc(a, b))
+    a1, a2 = arrays_for_binary_ufunc
+    if sparse:
+        a1 = pd.SparseArray(a1, dtype=pd.SparseDtype('int', 0))
+        a2 = pd.SparseArray(a2, dtype=pd.SparseDtype('int', 0))
+
+    name = "name"  # op(Series, array) preserves the name.
+    series = pd.Series(a1, name=name)
+    other = a2
+
+    array_args = (a1, a2)
+    series_args = (series, other)            # ufunc(series, array)
+
+    if flip:
+        array_args = reversed(array_args)
+        series_args = reversed(series_args)  # ufunc(array, series)
+
+    expected = pd.Series(ufunc(*array_args), name=name)
+    result = ufunc(*series_args)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
+@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
+@pytest.mark.parametrize("flip", [
+    pytest.param(True, marks=pytest.mark.xfail(reason="Index should defer")),
+    False
+], ids=['flipped', 'straight'])
+def test_binary_ufunc_with_index(flip, sparse, ufunc, arrays_for_binary_ufunc):
+    # Test that
+    #   * func(Series(a), Series(b)) == Series(ufunc(a, b))
+    #   * ufunc(Index, Series) dispatches to Series (returns a Series)
+    a1, a2 = arrays_for_binary_ufunc
+    if sparse:
+        a1 = pd.SparseArray(a1, dtype=pd.SparseDtype('int', 0))
+        a2 = pd.SparseArray(a2, dtype=pd.SparseDtype('int', 0))
+
+    name = "name"  # op(Series, array) preserves the name.
+    series = pd.Series(a1, name=name)
+    other = pd.Index(a2, name=name).astype("int64")
+
+    array_args = (a1, a2)
+    series_args = (series, other)            # ufunc(series, array)
+
+    if flip:
+        array_args = reversed(array_args)
+        series_args = reversed(series_args)  # ufunc(array, series)
+
+    expected = pd.Series(ufunc(*array_args), name=name)
+    result = ufunc(*series_args)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
+@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
+@pytest.mark.parametrize("shuffle", [True, False], ids=['unaligned',
+                                                        'aligned'])
+@pytest.mark.parametrize("flip", [True, False], ids=['flipped', 'straight'])
+def test_binary_ufunc_with_series(flip, shuffle, sparse, ufunc,
+                                  arrays_for_binary_ufunc):
+    # Test that
+    #   * func(Series(a), Series(b)) == Series(ufunc(a, b))
+    #   with alignment between the indices
+
+    if flip and shuffle:
+        pytest.xfail(reason="Fix with Series.__array_ufunc__")
+
+    a1, a2 = arrays_for_binary_ufunc
+    if sparse:
+        a1 = pd.SparseArray(a1, dtype=pd.SparseDtype('int', 0))
+        a2 = pd.SparseArray(a2, dtype=pd.SparseDtype('int', 0))
+
+    name = "name"  # op(Series, array) preserves the name.
+    series = pd.Series(a1, name=name)
+    other = pd.Series(a2, name=name)
+
+    idx = np.random.permutation(len(a1))
+
+    if shuffle:
+        other = other.take(idx)
+        a2 = a2.take(idx)
+        # alignment, so the expected index is the first index in the op.
+        if flip:
+            index = other.align(series)[0].index
+        else:
+            index = series.align(other)[0].index
+    else:
+        index = series.index
+
+    array_args = (a1, a2)
+    series_args = (series, other)  # ufunc(series, array)
+
+    if flip:
+        array_args = tuple(reversed(array_args))
+        series_args = tuple(reversed(series_args))  # ufunc(array, series)
+
+    expected = pd.Series(ufunc(*array_args), index=index, name=name)
+    result = ufunc(*series_args)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
+@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
+@pytest.mark.parametrize("flip", [True, False])
+def test_binary_ufunc_scalar(ufunc, sparse, flip, arrays_for_binary_ufunc):
+    # Test that
+    #   * ufunc(Series, scalar) == Series(ufunc(array, scalar))
+    #   * ufunc(Series, scalar) == ufunc(scalar, Series)
+    array, _ = arrays_for_binary_ufunc
+    if sparse:
+        array = pd.SparseArray(array)
+    other = 2
+    series = pd.Series(array, name="name")
+
+    series_args = (series, other)
+    array_args = (array, other)
+
+    if flip:
+        series_args = tuple(reversed(series_args))
+        array_args = tuple(reversed(array_args))
+
+    expected = pd.Series(ufunc(*array_args), name="name")
+    result = ufunc(*series_args)
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", [np.divmod])  # any others?
+@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
+@pytest.mark.parametrize("shuffle", SHUFFLE)
+@pytest.mark.filterwarnings("ignore:divide by zero:RuntimeWarning")
+def test_multiple_ouput_binary_ufuncs(ufunc, sparse, shuffle,
+                                      arrays_for_binary_ufunc):
+    # Test that
+    #  the same conditions from binary_ufunc_scalar apply to
+    #  ufuncs with multiple outputs.
+    if sparse and ufunc is np.divmod:
+        pytest.skip("sparse divmod not implemented.")
+
+    a1, a2 = arrays_for_binary_ufunc
+
+    if sparse:
+        a1 = pd.SparseArray(a1, dtype=pd.SparseDtype('int', 0))
+        a2 = pd.SparseArray(a2, dtype=pd.SparseDtype('int', 0))
+
+    s1 = pd.Series(a1)
+    s2 = pd.Series(a2)
+
+    if shuffle:
+        # ensure we align before applying the ufunc
+        s2 = s2.sample(frac=1)
+
+    expected = ufunc(a1, a2)
+    assert isinstance(expected, tuple)
+
+    result = ufunc(s1, s2)
+    assert isinstance(result, tuple)
+    tm.assert_series_equal(result[0], pd.Series(expected[0]))
+    tm.assert_series_equal(result[1], pd.Series(expected[1]))
+
+
+@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
+def test_multiple_ouput_ufunc(sparse, arrays_for_binary_ufunc):
+    # Test that the same conditions from unary input apply to multi-output
+    # ufuncs
+    array, _ = arrays_for_binary_ufunc
+
+    if sparse:
+        array = pd.SparseArray(array)
+
+    series = pd.Series(array, name="name")
+    result = np.modf(series)
+    expected = np.modf(array)
+
+    assert isinstance(result, tuple)
+    assert isinstance(expected, tuple)
+
+    tm.assert_series_equal(result[0], pd.Series(expected[0], name="name"))
+    tm.assert_series_equal(result[1], pd.Series(expected[1], name="name"))
+
+
+@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
+@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
+@pytest.mark.xfail(reason="Series.__array_ufunc__")
+def test_binary_ufunc_drops_series_name(ufunc, sparse,
+                                        arrays_for_binary_ufunc):
+    # Drop the names when they differ.
+    a1, a2 = arrays_for_binary_ufunc
+    s1 = pd.Series(a1, name='a')
+    s2 = pd.Series(a2, name='b')
+
+    result = ufunc(s1, s2)
+    assert result.name is None