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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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2 changes: 2 additions & 0 deletions pandas/core/groupby/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1895,6 +1895,8 @@ def post_processor(vals: np.ndarray, inference: Optional[Type]) -> np.ndarray:

return vals

algorithms.check_percentile(q)

return self._get_cythonized_result(
"group_quantile",
self.grouper,
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12 changes: 12 additions & 0 deletions pandas/tests/groupby/test_function.py
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Expand Up @@ -1333,3 +1333,15 @@ def test_groupby_mean_no_overflow():
}
)
assert df.groupby("user")["connections"].mean()["A"] == 3689348814740003840


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="all be in the interval"):
g.quantile(1.1)