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BUG: Fixes pd.merge issue with columns of dtype numpy.uintc on windows (
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pandas-dev#60145)

* bug fix for numpy.uintc in merge operations on windows

Added pytest test case to verify correct behavior with numpy.uintc dtype

* Formatting changes after running pre-commit

* Added tests for numpy.intc

* added whatsnew note

* pre-commit automatic changes and also made changes to test_merge.py file to make pandas namespace consistent

* removed comment

* added the deleted whatsnew note back

* better whatsnew note

Co-authored-by: Matthew Roeschke <[email protected]>

---------

Co-authored-by: Matthew Roeschke <[email protected]>
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AbhishekChaudharii and mroeschke authored Nov 1, 2024
1 parent d48235f commit a3f14bf
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
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Expand Up @@ -739,6 +739,7 @@ Reshaping
- Bug in :meth:`DataFrame.join` when a :class:`DataFrame` with a :class:`MultiIndex` would raise an ``AssertionError`` when :attr:`MultiIndex.names` contained ``None``. (:issue:`58721`)
- Bug in :meth:`DataFrame.merge` where merging on a column containing only ``NaN`` values resulted in an out-of-bounds array access (:issue:`59421`)
- Bug in :meth:`DataFrame.unstack` producing incorrect results when ``sort=False`` (:issue:`54987`, :issue:`55516`)
- Bug in :meth:`DataFrame.merge` when merging two :class:`DataFrame` on ``intc`` or ``uintc`` types on Windows (:issue:`60091`, :issue:`58713`)
- Bug in :meth:`DataFrame.pivot_table` incorrectly subaggregating results when called without an ``index`` argument (:issue:`58722`)
- Bug in :meth:`DataFrame.unstack` producing incorrect results when manipulating empty :class:`DataFrame` with an :class:`ExtentionDtype` (:issue:`59123`)

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12 changes: 11 additions & 1 deletion pandas/core/reshape/merge.py
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Expand Up @@ -123,7 +123,17 @@

# See https://github.com/pandas-dev/pandas/issues/52451
if np.intc is not np.int32:
_factorizers[np.intc] = libhashtable.Int64Factorizer
if np.dtype(np.intc).itemsize == 4:
_factorizers[np.intc] = libhashtable.Int32Factorizer
else:
_factorizers[np.intc] = libhashtable.Int64Factorizer

if np.uintc is not np.uint32:
if np.dtype(np.uintc).itemsize == 4:
_factorizers[np.uintc] = libhashtable.UInt32Factorizer
else:
_factorizers[np.uintc] = libhashtable.UInt64Factorizer


_known = (np.ndarray, ExtensionArray, Index, ABCSeries)

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35 changes: 35 additions & 0 deletions pandas/tests/reshape/merge/test_merge.py
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Expand Up @@ -1843,6 +1843,41 @@ def test_merge_empty(self, left_empty, how, exp):

tm.assert_frame_equal(result, expected)

def test_merge_with_uintc_columns(self):
df1 = DataFrame({"a": ["foo", "bar"], "b": np.array([1, 2], dtype=np.uintc)})
df2 = DataFrame({"a": ["foo", "baz"], "b": np.array([3, 4], dtype=np.uintc)})
result = df1.merge(df2, how="outer")
expected = DataFrame(
{
"a": ["bar", "baz", "foo", "foo"],
"b": np.array([2, 4, 1, 3], dtype=np.uintc),
}
)
tm.assert_frame_equal(result.reset_index(drop=True), expected)

def test_merge_with_intc_columns(self):
df1 = DataFrame({"a": ["foo", "bar"], "b": np.array([1, 2], dtype=np.intc)})
df2 = DataFrame({"a": ["foo", "baz"], "b": np.array([3, 4], dtype=np.intc)})
result = df1.merge(df2, how="outer")
expected = DataFrame(
{
"a": ["bar", "baz", "foo", "foo"],
"b": np.array([2, 4, 1, 3], dtype=np.intc),
}
)
tm.assert_frame_equal(result.reset_index(drop=True), expected)

def test_merge_intc_non_monotonic(self):
df = DataFrame({"join_key": Series([0, 2, 1], dtype=np.intc)})
df_details = DataFrame(
{"join_key": Series([0, 1, 2], dtype=np.intc), "value": ["a", "b", "c"]}
)
merged = df.merge(df_details, on="join_key", how="left")
expected = DataFrame(
{"join_key": np.array([0, 2, 1], dtype=np.intc), "value": ["a", "c", "b"]}
)
tm.assert_frame_equal(merged.reset_index(drop=True), expected)


@pytest.fixture
def left():
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