BUG: Bug in DataFrame.loc for setitem changed dtype when setting more than one column

doc/source/whatsnew/v1.3.0.rst

@@ -223,6 +223,7 @@ Indexing
 ^^^^^^^^
 - Bug in :meth:`CategoricalIndex.get_indexer` failing to raise ``InvalidIndexError`` when non-unique (:issue:`38372`)
 - Bug in inserting many new columns into a :class:`DataFrame` causing incorrect subsequent indexing behavior (:issue:`38380`)
+- Bug in :meth:`DataFrame.loc` converted ``float32`` to ``float64`` when setting values for more than one column (:issue:`18415`)
 - Bug in :meth:`DataFrame.iloc.__setitem__` and :meth:`DataFrame.loc.__setitem__` with mixed dtypes when setting with a dictionary value (:issue:`38335`)
 -
 -

pandas/core/indexing.py

@@ -1728,15 +1728,19 @@ def _setitem_with_indexer_2d_value(self, indexer, value):
         ilocs = self._ensure_iterable_column_indexer(indexer[1])
 
         # GH#7551 Note that this coerces the dtype if we are mixed
-        value = np.array(value, dtype=object)
+        if not isinstance(value, np.ndarray):
+            value = np.array(value, dtype=object)
         if len(ilocs) != value.shape[1]:
             raise ValueError(
                 "Must have equal len keys and value when setting with an ndarray"
             )
 
         for i, loc in enumerate(ilocs):
             # setting with a list, re-coerces
-            self._setitem_single_column(loc, value[:, i].tolist(), pi)
+            values = value[:, i]
+            if value.dtype == "object":
+                values = values.tolist()
+            self._setitem_single_column(loc, values, pi)
 
     def _setitem_with_indexer_frame_value(self, indexer, value: "DataFrame", name: str):
         ilocs = self._ensure_iterable_column_indexer(indexer[1])

pandas/tests/frame/indexing/test_setitem.py

@@ -9,6 +9,7 @@
     DataFrame,
     Index,
     Interval,
+    MultiIndex,
     NaT,
     Period,
     PeriodIndex,
@@ -324,6 +325,14 @@ def test_setitem_complete_column_with_array(self):
         )
         tm.assert_frame_equal(df, expected)
 
+    def test_loc_setitem_complete_column_float32(self):
+        # GH#18415
+        mi = MultiIndex.from_tuples([(1, 2), (1, 3), (2, 2)])
+        df = DataFrame([[0, 0, 0], [0, 0, 0]], dtype=np.float32, columns=mi)
+        df.loc[:, (1, slice(2, 3))] = np.ones((2, 2), dtype=np.float32)
+        expected = DataFrame([[1, 1, 0], [1, 1, 0]], dtype=np.float32, columns=mi)
+        tm.assert_frame_equal(df, expected)
+
     @pytest.mark.parametrize("dtype", ["f8", "i8", "u8"])
     def test_setitem_bool_with_numeric_index(self, dtype):
         # GH#36319

pandas/tests/reshape/test_pivot.py

@@ -962,12 +962,14 @@ def test_margins_dtype(self):
         # GH 17013
 
         df = self.data.copy()
-        df[["D", "E", "F"]] = np.arange(len(df) * 3).reshape(len(df), 3)
+        df[["D", "E", "F"]] = np.arange(len(df) * 3, dtype="int64").reshape(len(df), 3)
 
         mi_val = list(product(["bar", "foo"], ["one", "two"])) + [("All", "")]
         mi = MultiIndex.from_tuples(mi_val, names=("A", "B"))
         expected = DataFrame(
-            {"dull": [12, 21, 3, 9, 45], "shiny": [33, 0, 36, 51, 120]}, index=mi
+            {"dull": [12, 21, 3, 9, 45], "shiny": [33, 0, 36, 51, 120]},
+            index=mi,
+            dtype="int64",
         ).rename_axis("C", axis=1)
         expected["All"] = expected["dull"] + expected["shiny"]