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BUG: Modify get_indexer_non_unique to account for nan keys #52655

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BUG: Modify get_indexer_non_unique to account for nan keys #52655

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6 changes: 4 additions & 2 deletions pandas/_libs/index.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -360,6 +360,8 @@ cdef class IndexEngine:
values = self.values
stargets = set(targets)

na_in_stargets = any([checknull(t) for t in stargets])
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Suggested change
na_in_stargets = any([checknull(t) for t in stargets])
na_in_stargets = any(checknull(t) for t in stargets)


n = len(values)
n_t = len(targets)
if n > 10_000:
Expand All @@ -374,7 +376,7 @@ cdef class IndexEngine:
if (
stargets and
len(stargets) < 5 and
not any([checknull(t) for t in stargets]) and
not na_in_stargets and
self.is_monotonic_increasing
):
# if there are few enough stargets and the index is monotonically
Expand All @@ -396,7 +398,7 @@ cdef class IndexEngine:
# otherwise, map by iterating through all items in the index

# short-circuit na check
if values.dtype == object:
if na_in_stargets:
check_na_values = True
# keep track of nas in values
found_nas = set()
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26 changes: 26 additions & 0 deletions pandas/tests/indexes/base_class/test_indexing.py
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Expand Up @@ -37,6 +37,32 @@ def test_get_indexer_non_unique_dtype_mismatch(self):
tm.assert_numpy_array_equal(np.array([-1], dtype=np.intp), indexes)
tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), missing)

def test_get_indexer_non_unique_int_index(self):
indexes, missing = Index([np.nan, 100, 200, 100]).get_indexer_non_unique(
Index([np.nan])
)
tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), indexes)
tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)

indexes, missing = Index([np.nan, 100, 200, 100]).get_indexer_non_unique(
Index([np.nan, 100])
)
tm.assert_numpy_array_equal(np.array([0, 1, 3], dtype=np.intp), indexes)
tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)

def test_get_indexer_non_unique_float_index(self):
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Could you pytest.mark.parameterize this test with the one above?

indexes, missing = Index([np.nan, 100.0, 200.0, 100.0]).get_indexer_non_unique(
Index([np.nan])
)
tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), indexes)
tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)

indexes, missing = Index([np.nan, 100.0, 200.0, 100.0]).get_indexer_non_unique(
Index([np.nan, 100.0])
)
tm.assert_numpy_array_equal(np.array([0, 1, 3], dtype=np.intp), indexes)
tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)


class TestGetLoc:
@pytest.mark.slow # to_flat_index takes a while
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