BUG: frame.iloc[i] with a single EA column

doc/source/whatsnew/v1.5.0.rst

@@ -151,6 +151,7 @@ Interval
 
 Indexing
 ^^^^^^^^
+- Bug in :meth:`DataFrame.iloc` where indexing a single row on a :class:`DataFrame` with a single ExtensionDtype column gave a copy instead of a view on the underlying data (:issue:`45241`)
 - Bug in :meth:`Series.__setitem__` with a non-integer :class:`Index` when using an integer key to set a value that cannot be set inplace where a ``ValueError`` was raised insead of casting to a common dtype (:issue:`45070`)
 -
 

pandas/core/internals/blocks.py

@@ -1509,9 +1509,13 @@ def iget(self, i: int | tuple[int, int] | tuple[slice, int]):
             if not com.is_null_slice(col) and col != 0:
                 raise IndexError(f"{self} only contains one item")
             elif isinstance(col, slice):
-                if col != slice(None):
-                    raise NotImplementedError(col)
-                return self.values[[loc]]
+                # the is_null_slice check above assures that col is slice(None)
+                #  so what we want is a view on all our columns and row loc
+                if loc < 0:
+                    loc += len(self.values)
+                # Note: loc:loc+1 vs [[loc]] makes a difference when called
+                #  from fast_xs because we want to get a view back.
+                return self.values[loc : loc + 1]
             return self.values[loc]
         else:
             if i != 0:

pandas/tests/indexing/test_iloc.py

@@ -24,6 +24,7 @@
     array,
     concat,
     date_range,
+    interval_range,
     isna,
 )
 import pandas._testing as tm
@@ -1177,6 +1178,21 @@ def test_iloc_setitem_2d_ndarray_into_ea_block(self):
         expected = DataFrame({"status": ["a", "a", "c"]}, dtype=df["status"].dtype)
         tm.assert_frame_equal(df, expected)
 
+    @td.skip_array_manager_not_yet_implemented
+    def test_iloc_getitem_int_single_ea_block_view(self):
+        # GH#45241
+        # TODO: make an extension interface test for this?
+        arr = interval_range(1, 10.0)._values
+        df = DataFrame(arr)
+
+        # ser should be a *view* on the the DataFrame data
+        ser = df.iloc[2]
+
+        # if we have a view, then changing arr[2] should also change ser[0]
+        assert arr[2] != arr[-1]  # otherwise the rest isn't meaningful
+        arr[2] = arr[-1]
+        assert ser[0] == arr[-1]
+
 
 class TestILocErrors:
     # NB: this test should work for _any_ Series we can pass as