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BUG: RollingGroupBy.quantile ignores interpolation keyword argument #29567

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Merged
jreback merged 6 commits into from
Nov 18, 2019
Merged

BUG: RollingGroupBy.quantile ignores interpolation keyword argument #29567

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352445c
BUG: `RollingGroupBy.quantile` ignores `interpolation` keyword argume…
pwsiegel Nov 12, 2019
8a5054d
Merge remote-tracking branch 'upstream/master' into rolling-groupby-q…
pwsiegel Nov 17, 2019
ed7aa3f
Create new test `test_rolling_quantile`
pwsiegel Nov 17, 2019
48cb4e5
Parametrize test_rolling_quantile with interpolation parameters
pwsiegel Nov 17, 2019
fde54ce
add parametrized test `test_expanding_quantile` and removed quantile
pwsiegel Nov 17, 2019
61c5142
Change format of method reference in bug description in whatsnew to be
pwsiegel Nov 17, 2019
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.0.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -436,6 +436,7 @@ Groupby/resample/rolling
- Bug in :meth:`DataFrame.groupby` not offering selection by column name when ``axis=1`` (:issue:`27614`)
- Bug in :meth:`DataFrameGroupby.agg` not able to use lambda function with named aggregation (:issue:`27519`)
- Bug in :meth:`DataFrame.groupby` losing column name information when grouping by a categorical column (:issue:`28787`)
- Bug in :meth:`DataFrameGroupBy.rolling().quantile()` ignoring ``interpolation`` keyword argument (:issue:`28779`)

Reshaping
^^^^^^^^^
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4 changes: 3 additions & 1 deletion pandas/core/window/rolling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1499,7 +1499,9 @@ def f(arg, *args, **kwargs):
interpolation,
)

return self._apply(f, "quantile", quantile=quantile, **kwargs)
return self._apply(
f, "quantile", quantile=quantile, interpolation=interpolation, **kwargs
)

_shared_docs[
"cov"
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25 changes: 20 additions & 5 deletions pandas/tests/window/test_grouper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,6 @@ def test_rolling(self):
r = g.rolling(window=4)

for f in ["sum", "mean", "min", "max", "count", "kurt", "skew"]:

result = getattr(r, f)()
expected = g.apply(lambda x: getattr(x.rolling(4), f)())
tm.assert_frame_equal(result, expected)
Expand All @@ -70,8 +69,16 @@ def test_rolling(self):
expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1))
tm.assert_frame_equal(result, expected)

result = r.quantile(0.5)
expected = g.apply(lambda x: x.rolling(4).quantile(0.5))
@pytest.mark.parametrize(
"interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
)
def test_rolling_quantile(self, interpolation):
g = self.frame.groupby("A")
r = g.rolling(window=4)
result = r.quantile(0.4, interpolation=interpolation)
expected = g.apply(
lambda x: x.rolling(4).quantile(0.4, interpolation=interpolation)
)
tm.assert_frame_equal(result, expected)

def test_rolling_corr_cov(self):
Expand Down Expand Up @@ -142,8 +149,16 @@ def test_expanding(self):
expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0))
tm.assert_frame_equal(result, expected)

result = r.quantile(0.5)
expected = g.apply(lambda x: x.expanding().quantile(0.5))
@pytest.mark.parametrize(
"interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
)
def test_expanding_quantile(self, interpolation):
g = self.frame.groupby("A")
r = g.expanding()
result = r.quantile(0.4, interpolation=interpolation)
expected = g.apply(
lambda x: x.expanding().quantile(0.4, interpolation=interpolation)
)
tm.assert_frame_equal(result, expected)

def test_expanding_corr_cov(self):
Expand Down