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BUG: Fix IntervalTree handling of NaN #23353

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Oct 30, 2018
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BUG: Fix IntervalTree handling of NaN #23353

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4f13207
BUG: Fix IntervalTree handling of NaN
jschendel Oct 26, 2018
552accb
remove unnecesary test; parametrize leaf_size
jschendel Oct 29, 2018
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.24.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -1094,6 +1094,7 @@ Interval
- Bug in the ``IntervalIndex`` repr where a trailing comma was missing after the list of intervals (:issue:`20611`)
- Bug in :class:`Interval` where scalar arithmetic operations did not retain the ``closed`` value (:issue:`22313`)
- Bug in :class:`IntervalIndex` where indexing with datetime-like values raised a ``KeyError`` (:issue:`20636`)
- Bug in ``IntervalTree`` where data containing ``NaN`` triggered a warning and resulted in incorrect indexing queries with :class:`IntervalIndex` (:issue:`23352`)

Indexing
^^^^^^^^
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6 changes: 6 additions & 0 deletions pandas/_libs/intervaltree.pxi.in
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Expand Up @@ -72,6 +72,12 @@ cdef class IntervalTree(IntervalMixin):

self.closed = closed

# GH 23352: ensure no nan in nodes
mask = ~np.isnan(self.left)
self.left = self.left[mask]
self.right = self.right[mask]
indices = indices[mask]

node_cls = NODE_CLASSES[str(self.dtype), closed]
self.root = node_cls(self.left, self.right, indices, leaf_size)

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19 changes: 16 additions & 3 deletions pandas/tests/indexes/interval/test_interval_tree.py
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Expand Up @@ -13,14 +13,27 @@ def dtype(request):
return request.param


@pytest.fixture(scope='class')
def tree(dtype):
left = np.arange(5, dtype=dtype)
@pytest.fixture(scope='class', params=[
np.arange(5, dtype='int64'),
np.arange(5, dtype='int32'),
np.arange(5, dtype='uint64'),
np.arange(5, dtype='float64'),
np.arange(5, dtype='float32'),
np.array([0, 1, 2, 3, 4, np.nan], dtype='float64'),
np.array([0, 1, 2, 3, 4, np.nan], dtype='float32')])
def tree(request):
left = request.param
return IntervalTree(left, left + 2)


class TestIntervalTree(object):

def test_construction_nan(self):
# GH 23352
left, right = [0, 1, 2, np.nan], [1, 2, 3, np.nan]
with tm.assert_produces_warning(None):
IntervalTree(left, right, leaf_size=2)
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what exactly does this produce though? are the nan's valid? you are just dropping them.

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The intent here was to verify that the RuntimeWarning described in the issue is not occurring. I guess this test isn't really necessary, as at least one CI instance is set to fail if warnings are raised. Removed this test, and instead parametrized the tree fixture to include leaf_size that will trigger the warning and cause the invalid behavior described in the issue (so beyond just a warning would cause the asserts to fail if there's a regression).


def test_get_loc(self, tree):
tm.assert_numpy_array_equal(tree.get_loc(1),
np.array([0], dtype='int64'))
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