BUG: Pandas changes dtypes of columns when no float (or other) assignments are done to this column #34573

doc/source/whatsnew/v1.1.0.rst

@@ -896,6 +896,7 @@ Indexing
 - Bug in :meth:`DataFrame.truncate` and :meth:`Series.truncate` where index was assumed to be monotone increasing (:issue:`33756`)
 - Indexing with a list of strings representing datetimes failed on :class:`DatetimeIndex` or :class:`PeriodIndex`(:issue:`11278`)
 - Bug in :meth:`Series.at` when used with a :class:`MultiIndex` would raise an exception on valid inputs (:issue:`26989`)
+- Bug in :meth:`DataFrame.loc` with dictionary of values changes columns with dtype of ``int`` to ``float`` (:issue:`34573`)
 - Bug in :meth:`Series.loc` when used with a :class:`MultiIndex` would raise an IndexingError when accessing a None value (:issue:`34318`)
 
 Missing

pandas/core/indexing.py

@@ -1836,7 +1836,10 @@ def _setitem_with_indexer_missing(self, indexer, value):
                 # append a Series
                 value = value.reindex(index=self.obj.columns, copy=True)
                 value.name = indexer
-
+            elif isinstance(value, dict):
+                value = Series(
+                    value, index=self.obj.columns, name=indexer, dtype=object
+                )
             else:
                 # a list-list
                 if is_list_like_indexer(value):

pandas/tests/frame/indexing/test_setitem.py

@@ -126,3 +126,27 @@ def test_setitem_with_unaligned_sparse_value(self):
         df["new_column"] = sp_series
         expected = Series(SparseArray([1, 0, 0]), name="new_column")
         tm.assert_series_equal(df["new_column"], expected)
+
+    def test_setitem_dict_preserves_dtypes(self):
+        # https://github.com/pandas-dev/pandas/issues/34573
+        expected = DataFrame(
+            {
+                "a": Series([0, 1, 2], dtype="int64"),
+                "b": Series([1, 2, 3], dtype=float),
+                "c": Series([1, 2, 3], dtype=float),
+            }
+        )
+        df = DataFrame(
+            {
+                "a": Series([], dtype="int64"),
+                "b": Series([], dtype=float),
+                "c": Series([], dtype=float),
+            }
+        )
+        for idx, b in enumerate([1, 2, 3]):
+            df.loc[df.shape[0]] = {
+                "a": int(idx),
+                "b": float(b),
+                "c": float(b),
+            }
+        tm.assert_frame_equal(df, expected)