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Regression in loc.setitem raising ValueError with unordered MultiIndex columns and scalar indexer #39071

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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.2.1.rst
Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.any` and :meth:`DataFrame.all` not returning a result for tz-aware ``datetime64`` columns (:issue:`38723`)
- Fixed regression in :meth:`DataFrame.__setitem__` raising ``ValueError`` when expanding :class:`DataFrame` and new column is from type ``"0 - name"`` (:issue:`39010`)
- Fixed regression in :meth:`.GroupBy.sem` where the presence of non-numeric columns would cause an error instead of being dropped (:issue:`38774`)
- Fixed regression in :meth:`DataFrame.loc.__setitem__` raising ``ValueError`` when :class:`DataFrame` has unsorted :class:`MultiIndex` columns and indexer is a scalar (:issue:`38601`)
- Fixed regression in :func:`read_excel` with non-rawbyte file handles (:issue:`38788`)
- Bug in :meth:`read_csv` with ``float_precision="high"`` caused segfault or wrong parsing of long exponent strings. This resulted in a regression in some cases as the default for ``float_precision`` was changed in pandas 1.2.0 (:issue:`38753`)
- Fixed regression in :meth:`Rolling.skew` and :meth:`Rolling.kurt` modifying the object inplace (:issue:`38908`)
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9 changes: 6 additions & 3 deletions pandas/core/indexing.py
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@

from pandas.core.dtypes.common import (
is_array_like,
is_bool_dtype,
is_hashable,
is_integer,
is_iterator,
Expand Down Expand Up @@ -1933,12 +1934,14 @@ def _ensure_iterable_column_indexer(self, column_indexer):
"""
Ensure that our column indexer is something that can be iterated over.
"""
# Ensure we have something we can iterate over
if is_integer(column_indexer):
ilocs = [column_indexer]
elif isinstance(column_indexer, slice):
ri = Index(range(len(self.obj.columns)))
ilocs = ri[column_indexer]
ilocs = np.arange(len(self.obj.columns))[column_indexer]
elif isinstance(column_indexer, np.ndarray) and is_bool_dtype(
column_indexer.dtype
):
ilocs = np.arange(len(column_indexer))[column_indexer]
else:
ilocs = column_indexer
return ilocs
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15 changes: 15 additions & 0 deletions pandas/tests/frame/indexing/test_indexing.py
Original file line number Diff line number Diff line change
Expand Up @@ -1685,6 +1685,21 @@ def test_getitem_interval_index_partial_indexing(self):
res = df.loc[:, 0.5]
tm.assert_series_equal(res, expected)

@pytest.mark.parametrize("indexer", ["A", ["A"], ("A", slice(None))])
def test_setitem_unsorted_multiindex_columns(self, indexer):
# GH#38601
mi = MultiIndex.from_tuples([("A", 4), ("B", "3"), ("A", "2")])
df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=mi)
obj = df.copy()
obj.loc[:, indexer] = np.zeros((2, 2), dtype=int)
expected = DataFrame([[0, 2, 0], [0, 5, 0]], columns=mi)
tm.assert_frame_equal(obj, expected)

df = df.sort_index(1)
df.loc[:, indexer] = np.zeros((2, 2), dtype=int)
expected = expected.sort_index(1)
tm.assert_frame_equal(df, expected)


class TestDataFrameIndexingUInt64:
def test_setitem(self, uint64_frame):
Expand Down