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CLN: Use more pytest idioms in test_momemts_ewm.py #36801

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Oct 2, 2020
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CLN: Use more pytest idioms in test_momemts_ewm.py #36801

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CLN: Use more pytest idioms in test_momemts_ewm.py
Oct 2, 2020
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Oct 2, 2020
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194 changes: 141 additions & 53 deletions pandas/tests/window/moments/test_moments_ewm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,21 +7,19 @@
import pandas._testing as tm


def check_ew(name=None, preserve_nan=False, series=None, frame=None, nan_locs=None):
@pytest.mark.parametrize("name", ["var", "vol", "mean"])
def test_ewma_series(series, name):
series_result = getattr(series.ewm(com=10), name)()
assert isinstance(series_result, Series)

frame_result = getattr(frame.ewm(com=10), name)()
assert type(frame_result) == DataFrame

result = getattr(series.ewm(com=10), name)()
if preserve_nan:
assert result[nan_locs].isna().all()

@pytest.mark.parametrize("name", ["var", "vol", "mean"])
def test_ewma_frame(frame, name):
frame_result = getattr(frame.ewm(com=10), name)()
assert isinstance(frame_result, DataFrame)

def test_ewma(series, frame, nan_locs):
check_ew(name="mean", frame=frame, series=series, nan_locs=nan_locs)

def test_ewma_adjust():
vals = pd.Series(np.zeros(1000))
vals[5] = 1
result = vals.ewm(span=100, adjust=False).mean().sum()
Expand Down Expand Up @@ -53,63 +51,153 @@ def test_ewma_nan_handling():
result = s.ewm(com=5).mean()
tm.assert_series_equal(result, Series([np.nan] * 2 + [1.0] * 4))

# GH 7603
s0 = Series([np.nan, 1.0, 101.0])
s1 = Series([1.0, np.nan, 101.0])
s2 = Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan])
s3 = Series([1.0, np.nan, 101.0, 50.0])
com = 2.0
alpha = 1.0 / (1.0 + com)

def simple_wma(s, w):
return (s.multiply(w).cumsum() / w.cumsum()).fillna(method="ffill")

for (s, adjust, ignore_na, w) in [
(s0, True, False, [np.nan, (1.0 - alpha), 1.0]),
(s0, True, True, [np.nan, (1.0 - alpha), 1.0]),
(s0, False, False, [np.nan, (1.0 - alpha), alpha]),
(s0, False, True, [np.nan, (1.0 - alpha), alpha]),
(s1, True, False, [(1.0 - alpha) ** 2, np.nan, 1.0]),
(s1, True, True, [(1.0 - alpha), np.nan, 1.0]),
(s1, False, False, [(1.0 - alpha) ** 2, np.nan, alpha]),
(s1, False, True, [(1.0 - alpha), np.nan, alpha]),
(s2, True, False, [np.nan, (1.0 - alpha) ** 3, np.nan, np.nan, 1.0, np.nan]),
(s2, True, True, [np.nan, (1.0 - alpha), np.nan, np.nan, 1.0, np.nan]),
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@simonjayhawkins simonjayhawkins Oct 2, 2020 •
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i'm not sure that the parameterisation is clearer.

maybe parameterise s, adjust and ignore_na independently (maybe with fixtures) and create a w fixture with a dictionary lookup. i.e. pass s, adjust and ignore_na to w fixture

maybe use a class just for namespacing and use class level fixtures.

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I'll consider doing this change in a follow up PR. The main motivation with this pass is to ensure that parameterized test failures are more easily visible within pytest


@pytest.mark.parametrize(
"s, adjust, ignore_na, w",
[
(
Series([np.nan, 1.0, 101.0]),
True,
False,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), 1.0],
),
(
Series([np.nan, 1.0, 101.0]),
True,
True,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), 1.0],
),
(
Series([np.nan, 1.0, 101.0]),
False,
False,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), (1.0 / (1.0 + 2.0))],
),
(
Series([np.nan, 1.0, 101.0]),
False,
True,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), (1.0 / (1.0 + 2.0))],
),
(
Series([1.0, np.nan, 101.0]),
True,
False,
[(1.0 - (1.0 / (1.0 + 2.0))) ** 2, np.nan, 1.0],
),
(
s2,
Series([1.0, np.nan, 101.0]),
True,
True,
[(1.0 - (1.0 / (1.0 + 2.0))), np.nan, 1.0],
),
(
Series([1.0, np.nan, 101.0]),
False,
False,
[(1.0 - (1.0 / (1.0 + 2.0))) ** 2, np.nan, (1.0 / (1.0 + 2.0))],
),
(
Series([1.0, np.nan, 101.0]),
False,
True,
[(1.0 - (1.0 / (1.0 + 2.0))), np.nan, (1.0 / (1.0 + 2.0))],
),
(
Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
True,
False,
[np.nan, (1.0 - alpha) ** 3, np.nan, np.nan, alpha, np.nan],
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))) ** 3, np.nan, np.nan, 1.0, np.nan],
),
(
Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
True,
True,
[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), np.nan, np.nan, 1.0, np.nan],
),
(s2, False, True, [np.nan, (1.0 - alpha), np.nan, np.nan, alpha, np.nan]),
(s3, True, False, [(1.0 - alpha) ** 3, np.nan, (1.0 - alpha), 1.0]),
(s3, True, True, [(1.0 - alpha) ** 2, np.nan, (1.0 - alpha), 1.0]),
(
s3,
Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
False,
False,
[
(1.0 - alpha) ** 3,
np.nan,
(1.0 - alpha) * alpha,
alpha * ((1.0 - alpha) ** 2 + alpha),
(1.0 - (1.0 / (1.0 + 2.0))) ** 3,
np.nan,
np.nan,
(1.0 / (1.0 + 2.0)),
np.nan,
],
),
(s3, False, True, [(1.0 - alpha) ** 2, np.nan, (1.0 - alpha) * alpha, alpha]),
]:
expected = simple_wma(s, Series(w))
result = s.ewm(com=com, adjust=adjust, ignore_na=ignore_na).mean()
(
Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
False,
True,
[
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))),
np.nan,
np.nan,
(1.0 / (1.0 + 2.0)),
np.nan,
],
),
(
Series([1.0, np.nan, 101.0, 50.0]),
True,
False,
[
(1.0 - (1.0 / (1.0 + 2.0))) ** 3,
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))),
1.0,
],
),
(
Series([1.0, np.nan, 101.0, 50.0]),
True,
True,
[
(1.0 - (1.0 / (1.0 + 2.0))) ** 2,
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))),
1.0,
],
),
(
Series([1.0, np.nan, 101.0, 50.0]),
False,
False,
[
(1.0 - (1.0 / (1.0 + 2.0))) ** 3,
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))) * (1.0 / (1.0 + 2.0)),
(1.0 / (1.0 + 2.0))
* ((1.0 - (1.0 / (1.0 + 2.0))) ** 2 + (1.0 / (1.0 + 2.0))),
],
),
(
Series([1.0, np.nan, 101.0, 50.0]),
False,
True,
[
(1.0 - (1.0 / (1.0 + 2.0))) ** 2,
np.nan,
(1.0 - (1.0 / (1.0 + 2.0))) * (1.0 / (1.0 + 2.0)),
(1.0 / (1.0 + 2.0)),
],
),
],
)
def test_ewma_nan_handling_cases(s, adjust, ignore_na, w):
# GH 7603
expected = (s.multiply(w).cumsum() / Series(w).cumsum()).fillna(method="ffill")
result = s.ewm(com=2.0, adjust=adjust, ignore_na=ignore_na).mean()

tm.assert_series_equal(result, expected)
if ignore_na is False:
# check that ignore_na defaults to False
result = s.ewm(com=2.0, adjust=adjust).mean()
tm.assert_series_equal(result, expected)
if ignore_na is False:
# check that ignore_na defaults to False
result = s.ewm(com=com, adjust=adjust).mean()
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("name", ["var", "vol"])
def test_ewmvar_ewmvol(series, frame, nan_locs, name):
check_ew(name=name, frame=frame, series=series, nan_locs=nan_locs)


def test_ewma_span_com_args(series):
Expand Down