BUG: also return new object in case of null slice for both rows and c…

…olumsn (.(i)loc[:, :]) (#49469)

doc/source/whatsnew/v2.0.0.rst

@@ -405,6 +405,10 @@ Other API changes
 - Files are now closed when encountering an exception in :func:`read_json` (:issue:`49921`)
 - Changed behavior of :func:`read_csv`, :func:`read_json` & :func:`read_fwf`, where the index will now always be a :class:`RangeIndex`, when no index is specified. Previously the index would be a :class:`Index` with dtype ``object`` if the new DataFrame/Series has length 0 (:issue:`49572`)
 - :meth:`DataFrame.values`, :meth:`DataFrame.to_numpy`, :meth:`DataFrame.xs`, :meth:`DataFrame.reindex`, :meth:`DataFrame.fillna`, and :meth:`DataFrame.replace` no longer silently consolidate the underlying arrays; do ``df = df.copy()`` to ensure consolidation (:issue:`49356`)
+- Creating a new DataFrame using a full slice on both axes with :attr:`~DataFrame.loc`
+  or :attr:`~DataFrame.iloc` (thus, ``df.loc[:, :]`` or ``df.iloc[:, :]``) now returns a
+  new DataFrame (shallow copy) instead of the original DataFrame, consistent with other
+  methods to get a full slice (for example ``df.loc[:]`` or ``df[:]``) (:issue:`49469`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/indexing.py

@@ -934,6 +934,11 @@ def _getitem_tuple_same_dim(self, tup: tuple):
             #  be handled by the _getitem_lowerdim call above.
             assert retval.ndim == self.ndim
 
+        if retval is self.obj:
+            # if all axes were a null slice (`df.loc[:, :]`), ensure we still
+            # return a new object (https://github.com/pandas-dev/pandas/pull/49469)
+            retval = retval.copy(deep=False)
+
         return retval
 
     @final

pandas/tests/copy_view/test_indexing.py

@@ -612,6 +612,62 @@ def test_subset_chained_single_block_row(using_copy_on_write, using_array_manage
         assert subset.iloc[0] == 0
 
 
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda df: df[:],
+        lambda df: df.loc[:, :],
+        lambda df: df.loc[:],
+        lambda df: df.iloc[:, :],
+        lambda df: df.iloc[:],
+    ],
+    ids=["getitem", "loc", "loc-rows", "iloc", "iloc-rows"],
+)
+def test_null_slice(request, method, using_copy_on_write):
+    # Case: also all variants of indexing with a null slice (:) should return
+    # new objects to ensure we correctly use CoW for the results
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+    df_orig = df.copy()
+
+    df2 = method(df)
+
+    # we always return new objects (shallow copy), regardless of CoW or not
+    assert df2 is not df
+
+    # and those trigger CoW when mutated
+    df2.iloc[0, 0] = 0
+    if using_copy_on_write:
+        tm.assert_frame_equal(df, df_orig)
+    else:
+        assert df.iloc[0, 0] == 0
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        lambda s: s[:],
+        lambda s: s.loc[:],
+        lambda s: s.iloc[:],
+    ],
+    ids=["getitem", "loc", "iloc"],
+)
+def test_null_slice_series(request, method, using_copy_on_write):
+    s = Series([1, 2, 3], index=["a", "b", "c"])
+    s_orig = s.copy()
+
+    s2 = method(s)
+
+    # we always return new objects, regardless of CoW or not
+    assert s2 is not s
+
+    # and those trigger CoW when mutated
+    s2.iloc[0] = 0
+    if using_copy_on_write:
+        tm.assert_series_equal(s, s_orig)
+    else:
+        assert s.iloc[0] == 0
+
+
 # TODO add more tests modifying the parent
 
 

pandas/tests/indexing/test_loc.py

@@ -1097,6 +1097,7 @@ def test_identity_slice_returns_new_object(
         sliced_df = original_df.loc[:]
         assert sliced_df is not original_df
         assert original_df[:] is not original_df
+        assert original_df.loc[:, :] is not original_df
 
         # should be a shallow copy
         assert np.shares_memory(original_df["a"]._values, sliced_df["a"]._values)
@@ -1110,7 +1111,6 @@ def test_identity_slice_returns_new_object(
             assert (sliced_df["a"] == 4).all()
 
         # These should not return copies
-        assert original_df is original_df.loc[:, :]
         df = DataFrame(np.random.randn(10, 4))
         assert df[0] is df.loc[:, 0]