diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 0bcaa83c49628..e15d8a7b0e323 100755
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -654,7 +654,6 @@ def can_do_equal_len():
                         for i, idx in enumerate(indexer)
                         if i != info_axis
                     )
-                    and item_labels.is_unique
                 ):
                     self.obj[item_labels[indexer[info_axis]]] = value
                     return
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index c5254aaa4af5f..128016718dfed 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -1075,6 +1075,12 @@ def value_getitem(placement):
         blknos = self._blknos[loc]
         blklocs = self._blklocs[loc].copy()
 
+        if not self.axes[0].is_unique:
+            if not isinstance(blknos, np.ndarray):
+                blknos = [blknos]
+            if not isinstance(blklocs, np.ndarray):
+                blklocs = [blklocs]
+
         unfit_mgr_locs = []
         unfit_val_locs = []
         removed_blknos = []
diff --git a/pandas/tests/indexing/test_iloc.py b/pandas/tests/indexing/test_iloc.py
index 760d8c70b9434..429bb33466f15 100644
--- a/pandas/tests/indexing/test_iloc.py
+++ b/pandas/tests/indexing/test_iloc.py
@@ -462,6 +462,17 @@ def test_iloc_setitem_dups(self):
         df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(drop=True)
         tm.assert_frame_equal(df, expected)
 
+    def test_iloc_change_dtype_dups(self):
+        # GH 22035 and GH 24798
+
+        df = DataFrame([['0', 1, '2']], columns=['A', 'B', 'B'])
+        df.iloc[:, 0] = df.iloc[:, 0].astype(int)
+        assert df.dtypes.tolist() == ['int', 'int', 'O']
+
+        df = DataFrame([['0', '1', '2']], columns=['A', 'B', 'B'])
+        df.iloc[:, 0] = df.iloc[:, 0].astype(int)
+        assert df.dtypes.tolist() == ['int', 'O', 'O']
+
     def test_iloc_getitem_frame(self):
         df = DataFrame(
             np.random.randn(10, 4), index=range(0, 20, 2), columns=range(0, 8, 2)