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CLN: no need for suffices anymore in test_hashtable.py #44034

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Oct 15, 2021
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CLN: no need for suffices anymore in test_hashtable.py #44034

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108 changes: 46 additions & 62 deletions pandas/tests/libs/test_hashtable.py
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Original file line number Diff line number Diff line change
Expand Up @@ -370,96 +370,85 @@ def test_unique_for_nan_objects_tuple():
assert len(unique) == 2


def get_ht_function(fun_name, type_suffix):
return getattr(ht, fun_name)


@pytest.mark.parametrize(
"dtype, type_suffix",
"dtype",
[
(np.object_, "object"),
(np.complex128, "complex128"),
(np.int64, "int64"),
(np.uint64, "uint64"),
(np.float64, "float64"),
(np.complex64, "complex64"),
(np.int32, "int32"),
(np.uint32, "uint32"),
(np.float32, "float32"),
(np.int16, "int16"),
(np.uint16, "uint16"),
(np.int8, "int8"),
(np.uint8, "uint8"),
(np.intp, "intp"),
np.object_,
np.complex128,
np.int64,
np.uint64,
np.float64,
np.complex64,
np.int32,
np.uint32,
np.float32,
np.int16,
np.uint16,
np.int8,
np.uint8,
np.intp,
],
)
class TestHelpFunctions:
def test_value_count(self, dtype, type_suffix, writable):
def test_value_count(self, dtype, writable):
N = 43
value_count = get_ht_function("value_count", type_suffix)
expected = (np.arange(N) + N).astype(dtype)
values = np.repeat(expected, 5)
values.flags.writeable = writable
keys, counts = value_count(values, False)
keys, counts = ht.value_count(values, False)
tm.assert_numpy_array_equal(np.sort(keys), expected)
assert np.all(counts == 5)

def test_value_count_stable(self, dtype, type_suffix, writable):
def test_value_count_stable(self, dtype, writable):
# GH12679
value_count = get_ht_function("value_count", type_suffix)
values = np.array([2, 1, 5, 22, 3, -1, 8]).astype(dtype)
values.flags.writeable = writable
keys, counts = value_count(values, False)
keys, counts = ht.value_count(values, False)
tm.assert_numpy_array_equal(keys, values)
assert np.all(counts == 1)

def test_duplicated_first(self, dtype, type_suffix, writable):
def test_duplicated_first(self, dtype, writable):
N = 100
duplicated = get_ht_function("duplicated", type_suffix)
values = np.repeat(np.arange(N).astype(dtype), 5)
values.flags.writeable = writable
result = duplicated(values)
result = ht.duplicated(values)
expected = np.ones_like(values, dtype=np.bool_)
expected[::5] = False
tm.assert_numpy_array_equal(result, expected)

def test_ismember_yes(self, dtype, type_suffix, writable):
def test_ismember_yes(self, dtype, writable):
N = 127
ismember = get_ht_function("ismember", type_suffix)
arr = np.arange(N).astype(dtype)
values = np.arange(N).astype(dtype)
arr.flags.writeable = writable
values.flags.writeable = writable
result = ismember(arr, values)
result = ht.ismember(arr, values)
expected = np.ones_like(values, dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)

def test_ismember_no(self, dtype, type_suffix):
def test_ismember_no(self, dtype):
N = 17
ismember = get_ht_function("ismember", type_suffix)
arr = np.arange(N).astype(dtype)
values = (np.arange(N) + N).astype(dtype)
result = ismember(arr, values)
result = ht.ismember(arr, values)
expected = np.zeros_like(values, dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)

def test_mode(self, dtype, type_suffix, writable):
def test_mode(self, dtype, writable):
if dtype in (np.int8, np.uint8):
N = 53
else:
N = 11111
mode = get_ht_function("mode", type_suffix)
values = np.repeat(np.arange(N).astype(dtype), 5)
values[0] = 42
values.flags.writeable = writable
result = mode(values, False)
result = ht.mode(values, False)
assert result == 42

def test_mode_stable(self, dtype, type_suffix, writable):
mode = get_ht_function("mode", type_suffix)
def test_mode_stable(self, dtype, writable):
values = np.array([2, 1, 5, 22, 3, -1, 8]).astype(dtype)
values.flags.writeable = writable
keys = mode(values, False)
keys = ht.mode(values, False)
tm.assert_numpy_array_equal(keys, values)


Expand All @@ -482,52 +471,47 @@ def test_unique_label_indices_intp(writable):


@pytest.mark.parametrize(
"dtype, type_suffix",
"dtype",
[
(np.float64, "float64"),
(np.float32, "float32"),
(np.complex128, "complex128"),
(np.complex64, "complex64"),
np.float64,
np.float32,
np.complex128,
np.complex64,
],
)
class TestHelpFunctionsWithNans:
def test_value_count(self, dtype, type_suffix):
value_count = get_ht_function("value_count", type_suffix)
def test_value_count(self, dtype):
values = np.array([np.nan, np.nan, np.nan], dtype=dtype)
keys, counts = value_count(values, True)
keys, counts = ht.value_count(values, True)
assert len(keys) == 0
keys, counts = value_count(values, False)
keys, counts = ht.value_count(values, False)
assert len(keys) == 1 and np.all(np.isnan(keys))
assert counts[0] == 3

def test_duplicated_first(self, dtype, type_suffix):
duplicated = get_ht_function("duplicated", type_suffix)
def test_duplicated_first(self, dtype):
values = np.array([np.nan, np.nan, np.nan], dtype=dtype)
result = duplicated(values)
result = ht.duplicated(values)
expected = np.array([False, True, True])
tm.assert_numpy_array_equal(result, expected)

def test_ismember_yes(self, dtype, type_suffix):
ismember = get_ht_function("ismember", type_suffix)
def test_ismember_yes(self, dtype):
arr = np.array([np.nan, np.nan, np.nan], dtype=dtype)
values = np.array([np.nan, np.nan], dtype=dtype)
result = ismember(arr, values)
result = ht.ismember(arr, values)
expected = np.array([True, True, True], dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)

def test_ismember_no(self, dtype, type_suffix):
ismember = get_ht_function("ismember", type_suffix)
def test_ismember_no(self, dtype):
arr = np.array([np.nan, np.nan, np.nan], dtype=dtype)
values = np.array([1], dtype=dtype)
result = ismember(arr, values)
result = ht.ismember(arr, values)
expected = np.array([False, False, False], dtype=np.bool_)
tm.assert_numpy_array_equal(result, expected)

def test_mode(self, dtype, type_suffix):
mode = get_ht_function("mode", type_suffix)
def test_mode(self, dtype):
values = np.array([42, np.nan, np.nan, np.nan], dtype=dtype)
assert mode(values, True) == 42
assert np.isnan(mode(values, False))
assert ht.mode(values, True) == 42
assert np.isnan(ht.mode(values, False))


def test_ismember_tuple_with_nans():
Expand Down