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BUG: quantile segfaults on invalid quantile values #27473

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BUG: quantile segfaults on invalid quantile values #27473

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0541e57
BUG: quantile segfaults on invalid quantil values
pilkibun Jul 19, 2019
264e9ca
move test
pilkibun Jul 25, 2019
14ac38b
Use algorithms.check_percentile
pilkibun Jul 25, 2019
3cde126
remove noise
pilkibun Jul 25, 2019
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4 changes: 4 additions & 0 deletions pandas/_libs/groupby.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -719,6 +719,10 @@ def group_quantile(ndarray[float64_t] out,
ndarray[int64_t] counts, non_na_counts, sort_arr

assert values.shape[0] == N

if not 0 <= q <= 1:
raise ValueError("Quantile values must lie in the interval [0, 1]")

inter_methods = {
'linear': INTERPOLATION_LINEAR,
'lower': INTERPOLATION_LOWER,
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12 changes: 12 additions & 0 deletions pandas/tests/groupby/test_transform.py
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Expand Up @@ -958,6 +958,18 @@ def test_groupby_transform_timezone_column(func):
tm.assert_frame_equal(result, expected)


def test_quantile_validation():
# GH#27470
df = pd.DataFrame(dict(a=[0, 0, 0, 1, 1, 1]))
g = df.groupby(np.repeat([0, 1], 3))
result = g.quantile(0.5)
expected = DataFrame(dict(a=[0.0, 1.0]))
tm.assert_frame_equal(result, expected)

with pytest.raises(ValueError, match="uantile values"):
g.quantile(1.1)


@pytest.mark.parametrize(
"func, values",
[
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