BUG: iloc.__setitem__ with dict value and mixed dtypes

doc/source/whatsnew/v1.3.0.rst

@@ -127,7 +127,7 @@ Interval
 
 Indexing
 ^^^^^^^^
-
+- Bug in :meth:`DataFrame.iloc.__setitem__` with mixed dtypes when setting with a dictionary value (:issue:`38335`)
 -
 -
 

pandas/core/indexing.py

@@ -1649,8 +1649,10 @@ def _setitem_with_indexer_split_path(self, indexer, value, name: str):
         if isinstance(indexer[0], np.ndarray) and indexer[0].ndim > 2:
             raise ValueError(r"Cannot set values with ndim > 2")
 
-        if isinstance(value, ABCSeries) and name != "iloc":
-            value = self._align_series(indexer, value)
+        if (isinstance(value, ABCSeries) and name != "iloc") or isinstance(value, dict):
+            from pandas import Series
+
+            value = self._align_series(indexer, Series(value))
 
         # Ensure we have something we can iterate over
         info_axis = indexer[1]

pandas/tests/indexing/test_iloc.py

@@ -869,6 +869,12 @@ def test_iloc_setitem_dictionary_value(self):
         expected = DataFrame({"x": [1, 9], "y": [2, 99]})
         tm.assert_frame_equal(df, expected)
 
+        # GH#38335 same thing, mixed dtypes
+        df = DataFrame({"x": [1, 2], "y": [2.0, 2.0]})
+        df.iloc[1] = rhs
+        expected = DataFrame({"x": [1, 9], "y": [2.0, 99.0]})
+        tm.assert_frame_equal(df, expected)
+
 
 class TestILocErrors:
     # NB: this test should work for _any_ Series we can pass as

pandas/tests/indexing/test_indexing.py

@@ -741,13 +741,21 @@ def test_slice_with_zero_step_raises(self):
             s.loc[::0]
 
     def test_indexing_assignment_dict_already_exists(self):
-        df = DataFrame({"x": [1, 2, 6], "y": [2, 2, 8], "z": [-5, 0, 5]}).set_index("z")
+        index = Index([-5, 0, 5], name="z")
+        df = DataFrame({"x": [1, 2, 6], "y": [2, 2, 8]}, index=index)
         expected = df.copy()
         rhs = {"x": 9, "y": 99}
         df.loc[5] = rhs
         expected.loc[5] = [9, 99]
         tm.assert_frame_equal(df, expected)
 
+        # GH#38335 same thing, mixed dtypes
+        df = DataFrame({"x": [1, 2, 6], "y": [2.0, 2.0, 8.0]}, index=index)
+        expected = df.copy()
+        df.loc[5] = rhs
+        expected = DataFrame({"x": [1, 2, 9], "y": [2.0, 2.0, 99.0]}, index=index)
+        tm.assert_frame_equal(df, expected)
+
     def test_indexing_dtypes_on_empty(self):
         # Check that .iloc returns correct dtypes GH9983
         df = DataFrame({"a": [1, 2, 3], "b": ["b", "b2", "b3"]})