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REGR: Outer merge failing with integer and NaN keys #43553

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Merged
merged 3 commits into from
Sep 14, 2021
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REGR: Outer merge failing with integer and NaN keys #43553

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REGR: Outer merge failing with integer and NaN keys
debnathshoham Sep 13, 2021
74f8fcf
REGR: Outer merge failing with integer and NaN keys
debnathshoham Sep 13, 2021
3aa7c39
changed test as per suggestion
debnathshoham Sep 14, 2021
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v1.3.4.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ including other versions of pandas.

Fixed regressions
~~~~~~~~~~~~~~~~~
-
- Fixed regression in :meth:`merge` with integer and ``NaN`` keys failing with ``outer`` merge (:issue:`43550`)
-

.. ---------------------------------------------------------------------------
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3 changes: 2 additions & 1 deletion pandas/core/reshape/merge.py
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Expand Up @@ -37,6 +37,7 @@
Substitution,
)

from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.common import (
ensure_float64,
ensure_int64,
Expand Down Expand Up @@ -912,7 +913,7 @@ def _maybe_add_join_keys(
result_dtype = lvals.dtype
else:
key_col = Index(lvals).where(~mask_left, rvals)
result_dtype = lvals.dtype
result_dtype = find_common_type([lvals.dtype, rvals.dtype])

if result._is_label_reference(name):
result[name] = Series(
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15 changes: 15 additions & 0 deletions pandas/tests/reshape/merge/test_merge.py
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Expand Up @@ -2571,3 +2571,18 @@ def test_mergeerror_on_left_index_mismatched_dtypes():
df_2 = DataFrame(data=["X"], columns=["C"], index=[999])
with pytest.raises(MergeError, match="Can only pass argument"):
merge(df_1, df_2, on=["C"], left_index=True)


def test_merge_outer_with_NaN():
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can you parameterize with Int64 as well

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Can we switch left and right too? Changing lvals to rvals above would have fixed the issue but caused the inverse to fail

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done and done

# GH#43550
left = DataFrame({"key": [1, 2], "col1": [1, 2]})
right = DataFrame({"key": [np.nan, np.nan], "col2": [3, 4]})
result = merge(left, right, on="key", how="outer")
expected = DataFrame(
{
"key": [1, 2, np.nan, np.nan],
"col1": [1, 2, np.nan, np.nan],
"col2": [np.nan, np.nan, 3, 4],
}
)
tm.assert_frame_equal(result, expected)