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Dec 4, 2021
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TST: Parameterize test_algos.py #44760

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142 changes: 65 additions & 77 deletions pandas/tests/test_algos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -779,7 +779,8 @@ def test_different_nans(self):
expected = np.array([np.nan])
tm.assert_numpy_array_equal(result, expected)

def test_first_nan_kept(self):
@pytest.mark.parametrize("el_type", [np.float64, object])
def test_first_nan_kept(self, el_type):
# GH 22295
# create different nans from bit-patterns:
bits_for_nan1 = 0xFFF8000000000001
Expand All @@ -788,13 +789,12 @@ def test_first_nan_kept(self):
NAN2 = struct.unpack("d", struct.pack("=Q", bits_for_nan2))[0]
assert NAN1 != NAN1
assert NAN2 != NAN2
for el_type in [np.float64, object]:
a = np.array([NAN1, NAN2], dtype=el_type)
result = pd.unique(a)
assert result.size == 1
# use bit patterns to identify which nan was kept:
result_nan_bits = struct.unpack("=Q", struct.pack("d", result[0]))[0]
assert result_nan_bits == bits_for_nan1
a = np.array([NAN1, NAN2], dtype=el_type)
result = pd.unique(a)
assert result.size == 1
# use bit patterns to identify which nan was kept:
result_nan_bits = struct.unpack("=Q", struct.pack("d", result[0]))[0]
assert result_nan_bits == bits_for_nan1

def test_do_not_mangle_na_values(self, unique_nulls_fixture, unique_nulls_fixture2):
# GH 22295
Expand Down Expand Up @@ -1261,21 +1261,20 @@ def test_dropna(self):
expected = Series([3, 2, 1], index=[5.0, 10.3, np.nan])
tm.assert_series_equal(result, expected)

def test_value_counts_normalized(self):
@pytest.mark.parametrize("dtype", (np.float64, object, "M8[ns]"))
def test_value_counts_normalized(self, dtype):
# GH12558
s = Series([1] * 2 + [2] * 3 + [np.nan] * 5)
dtypes = (np.float64, object, "M8[ns]")
for t in dtypes:
s_typed = s.astype(t)
result = s_typed.value_counts(normalize=True, dropna=False)
expected = Series(
[0.5, 0.3, 0.2], index=Series([np.nan, 2.0, 1.0], dtype=t)
)
tm.assert_series_equal(result, expected)
s_typed = s.astype(dtype)
result = s_typed.value_counts(normalize=True, dropna=False)
expected = Series(
[0.5, 0.3, 0.2], index=Series([np.nan, 2.0, 1.0], dtype=dtype)
)
tm.assert_series_equal(result, expected)

result = s_typed.value_counts(normalize=True, dropna=True)
expected = Series([0.6, 0.4], index=Series([2.0, 1.0], dtype=t))
tm.assert_series_equal(result, expected)
result = s_typed.value_counts(normalize=True, dropna=True)
expected = Series([0.6, 0.4], index=Series([2.0, 1.0], dtype=dtype))
tm.assert_series_equal(result, expected)

def test_value_counts_uint64(self):
arr = np.array([2 ** 63], dtype=np.uint64)
Expand Down Expand Up @@ -1479,13 +1478,10 @@ def test_datetime_likes(self):
res_false = s.duplicated(keep=False)
tm.assert_series_equal(res_false, Series(exp_false))

def test_unique_index(self):
cases = [Index([1, 2, 3]), pd.RangeIndex(0, 3)]
for case in cases:
assert case.is_unique is True
tm.assert_numpy_array_equal(
case.duplicated(), np.array([False, False, False])
)
@pytest.mark.parametrize("case", [Index([1, 2, 3]), pd.RangeIndex(0, 3)])
def test_unique_index(self, case):
assert case.is_unique is True
tm.assert_numpy_array_equal(case.duplicated(), np.array([False, False, False]))

@pytest.mark.parametrize(
"arr, uniques",
Expand Down Expand Up @@ -1744,20 +1740,25 @@ def test_unique_label_indices():

class TestRank:
@td.skip_if_no_scipy
def test_scipy_compat(self):
@pytest.mark.parametrize(
"arr",
[
[np.nan, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 3, np.nan],
[4.0, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 4.0, np.nan],
],
)
def test_scipy_compat(self, arr):
from scipy.stats import rankdata

def _check(arr):
mask = ~np.isfinite(arr)
arr = arr.copy()
result = libalgos.rank_1d(arr)
arr[mask] = np.inf
exp = rankdata(arr)
exp[mask] = np.nan
tm.assert_almost_equal(result, exp)
arr = np.array(arr)

_check(np.array([np.nan, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 3, np.nan]))
_check(np.array([4.0, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 4.0, np.nan]))
mask = ~np.isfinite(arr)
arr = arr.copy()
result = libalgos.rank_1d(arr)
arr[mask] = np.inf
exp = rankdata(arr)
exp[mask] = np.nan
tm.assert_almost_equal(result, exp)

@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
def test_basic(self, writable, dtype):
Expand All @@ -1769,12 +1770,12 @@ def test_basic(self, writable, dtype):
result = algos.rank(ser)
tm.assert_numpy_array_equal(result, exp)

def test_uint64_overflow(self):
@pytest.mark.parametrize("dtype", [np.float64, np.uint64])
def test_uint64_overflow(self, dtype):
exp = np.array([1, 2], dtype=np.float64)

for dtype in [np.float64, np.uint64]:
s = Series([1, 2 ** 63], dtype=dtype)
tm.assert_numpy_array_equal(algos.rank(s), exp)
s = Series([1, 2 ** 63], dtype=dtype)
tm.assert_numpy_array_equal(algos.rank(s), exp)

def test_too_many_ndims(self):
arr = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]])
Expand Down Expand Up @@ -1819,21 +1820,6 @@ def test_pad_backfill_object_segfault():


class TestTseriesUtil:
def test_combineFunc(self):
pass

def test_reindex(self):
pass

def test_isna(self):
pass

def test_groupby(self):
pass

def test_groupby_withnull(self):
pass

def test_backfill(self):
old = Index([1, 5, 10])
new = Index(list(range(12)))
Expand Down Expand Up @@ -2274,44 +2260,45 @@ def test_no_mode(self):
exp = Series([], dtype=np.float64, index=Index([], dtype=int))
tm.assert_series_equal(algos.mode([]), exp)

def test_mode_single(self):
@pytest.mark.parametrize("dt", np.typecodes["AllInteger"] + np.typecodes["Float"])
def test_mode_single(self, dt):
# GH 15714
exp_single = [1]
data_single = [1]

exp_multi = [1]
data_multi = [1, 1]

for dt in np.typecodes["AllInteger"] + np.typecodes["Float"]:
s = Series(data_single, dtype=dt)
exp = Series(exp_single, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)
s = Series(data_single, dtype=dt)
exp = Series(exp_single, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)

s = Series(data_multi, dtype=dt)
exp = Series(exp_multi, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)
s = Series(data_multi, dtype=dt)
exp = Series(exp_multi, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)

def test_mode_obj_int(self):
exp = Series([1], dtype=int)
tm.assert_series_equal(algos.mode([1]), exp)

exp = Series(["a", "b", "c"], dtype=object)
tm.assert_series_equal(algos.mode(["a", "b", "c"]), exp)

def test_number_mode(self):
@pytest.mark.parametrize("dt", np.typecodes["AllInteger"] + np.typecodes["Float"])
def test_number_mode(self, dt):
exp_single = [1]
data_single = [1] * 5 + [2] * 3

exp_multi = [1, 3]
data_multi = [1] * 5 + [2] * 3 + [3] * 5

for dt in np.typecodes["AllInteger"] + np.typecodes["Float"]:
s = Series(data_single, dtype=dt)
exp = Series(exp_single, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)
s = Series(data_single, dtype=dt)
exp = Series(exp_single, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)

s = Series(data_multi, dtype=dt)
exp = Series(exp_multi, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)
s = Series(data_multi, dtype=dt)
exp = Series(exp_multi, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)

def test_strobj_mode(self):
exp = ["b"]
Expand All @@ -2321,13 +2308,14 @@ def test_strobj_mode(self):
exp = Series(exp, dtype="c")
tm.assert_series_equal(algos.mode(s), exp)

@pytest.mark.parametrize("dt", [str, object])
def test_strobj_multi_char(self, dt):
exp = ["bar"]
data = ["foo"] * 2 + ["bar"] * 3

for dt in [str, object]:
s = Series(data, dtype=dt)
exp = Series(exp, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)
s = Series(data, dtype=dt)
exp = Series(exp, dtype=dt)
tm.assert_series_equal(algos.mode(s), exp)

def test_datelike_mode(self):
exp = Series(["1900-05-03", "2011-01-03", "2013-01-02"], dtype="M8[ns]")
Expand Down