REF/API: DataFrame.__setitem__ never operate in-place (#39510)

Author: jbrockmendel

doc/source/whatsnew/v1.3.0.rst

@@ -225,6 +225,41 @@ In pandas 1.3.0, ``df`` continues to share data with ``values``
    np.shares_memory(df["A"], values)
 
 
+.. _whatsnew_130.notable_bug_fixes.setitem_never_inplace:
+
+Never Operate Inplace When Setting ``frame[keys] = values``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+When setting multiple columns using ``frame[keys] = values`` new arrays will
+replace pre-existing arrays for these keys, which will *not* be over-written
+(:issue:`39510`).  As a result, the columns will retain the dtype(s) of ``values``,
+never casting to the dtypes of the existing arrays.
+
+.. ipython:: python
+
+   df = pd.DataFrame(range(3), columns=["A"], dtype="float64")
+   df[["A"]] = 5
+
+In the old behavior, ``5`` was cast to ``float64`` and inserted into the existing
+array backing ``df``:
+
+*pandas 1.2.x*
+
+.. code-block:: ipython
+
+   In [1]: df.dtypes
+   Out[1]:
+   A    float64
+
+In the new behavior, we get a new array, and retain an integer-dtyped ``5``:
+
+*pandas 1.3.0*
+
+.. ipython:: python
+
+   df.dtypes
+
+
 .. _whatsnew_130.notable_bug_fixes.setitem_with_bool_casting:
 
 Consistent Casting With Setting Into Boolean Series

pandas/core/frame.py

@@ -3548,6 +3548,7 @@ def _setitem_slice(self, key: slice, value):
     def _setitem_array(self, key, value):
         # also raises Exception if object array with NA values
         if com.is_bool_indexer(key):
+            # bool indexer is indexing along rows
             if len(key) != len(self.index):
                 raise ValueError(
                     f"Item wrong length {len(key)} instead of {len(self.index)}!"
@@ -3559,18 +3560,72 @@ def _setitem_array(self, key, value):
                 # GH#39931 reindex since iloc does not align
                 value = value.reindex(self.index.take(indexer))
             self.iloc[indexer] = value
+
         else:
             if isinstance(value, DataFrame):
                 check_key_length(self.columns, key, value)
                 for k1, k2 in zip(key, value.columns):
                     self[k1] = value[k2]
+
+            elif not is_list_like(value):
+                for col in key:
+                    self[col] = value
+
+            elif isinstance(value, np.ndarray) and value.ndim == 2:
+                self._iset_not_inplace(key, value)
+
+            elif np.ndim(value) > 1:
+                # list of lists
+                value = DataFrame(value).values
+                return self._setitem_array(key, value)
+
             else:
-                self.loc._ensure_listlike_indexer(key, axis=1, value=value)
-                indexer = self.loc._get_listlike_indexer(
-                    key, axis=1, raise_missing=False
-                )[1]
-                self._check_setitem_copy()
-                self.iloc[:, indexer] = value
+                self._iset_not_inplace(key, value)
+
+    def _iset_not_inplace(self, key, value):
+        # GH#39510 when setting with df[key] = obj with a list-like key and
+        #  list-like value, we iterate over those listlikes and set columns
+        #  one at a time.  This is different from dispatching to
+        #  `self.loc[:, key]= value`  because loc.__setitem__ may overwrite
+        #  data inplace, whereas this will insert new arrays.
+
+        def igetitem(obj, i: int):
+            # Note: we catch DataFrame obj before getting here, but
+            #  hypothetically would return obj.iloc[:, i]
+            if isinstance(obj, np.ndarray):
+                return obj[..., i]
+            else:
+                return obj[i]
+
+        if self.columns.is_unique:
+            if np.shape(value)[-1] != len(key):
+                raise ValueError("Columns must be same length as key")
+
+            for i, col in enumerate(key):
+                self[col] = igetitem(value, i)
+
+        else:
+
+            ilocs = self.columns.get_indexer_non_unique(key)[0]
+            if (ilocs < 0).any():
+                # key entries not in self.columns
+                raise NotImplementedError
+
+            if np.shape(value)[-1] != len(ilocs):
+                raise ValueError("Columns must be same length as key")
+
+            assert np.ndim(value) <= 2
+
+            orig_columns = self.columns
+
+            # Using self.iloc[:, i] = ... may set values inplace, which
+            #  by convention we do not do in __setitem__
+            try:
+                self.columns = Index(range(len(self.columns)))
+                for i, iloc in enumerate(ilocs):
+                    self[iloc] = igetitem(value, i)
+            finally:
+                self.columns = orig_columns
 
     def _setitem_frame(self, key, value):
         # support boolean setting with DataFrame input, e.g.

pandas/tests/frame/indexing/test_setitem.py

@@ -342,6 +342,10 @@ def test_setitem_complete_column_with_array(self):
                 "d": [1, 1, 1],
             }
         )
+        expected["c"] = expected["c"].astype(arr.dtype)
+        expected["d"] = expected["d"].astype(arr.dtype)
+        assert expected["c"].dtype == arr.dtype
+        assert expected["d"].dtype == arr.dtype
         tm.assert_frame_equal(df, expected)
 
     @pytest.mark.parametrize("dtype", ["f8", "i8", "u8"])
@@ -381,16 +385,35 @@ def test_setitem_frame_duplicate_columns(self, using_array_manager):
                 [np.nan, 1, 2, np.nan, 4, 5],
                 [np.nan, 1, 2, np.nan, 4, 5],
             ],
-            columns=cols,
             dtype="object",
         )
+
         if using_array_manager:
             # setitem replaces column so changes dtype
+
+            expected.columns = cols
             expected["C"] = expected["C"].astype("int64")
             # TODO(ArrayManager) .loc still overwrites
             expected["B"] = expected["B"].astype("int64")
+        else:
+            # set these with unique columns to be extra-unambiguous
+            expected[2] = expected[2].astype(np.int64)
+            expected[5] = expected[5].astype(np.int64)
+            expected.columns = cols
+
         tm.assert_frame_equal(df, expected)
 
+    def test_setitem_frame_duplicate_columns_size_mismatch(self):
+        # GH#39510
+        cols = ["A", "B", "C"] * 2
+        df = DataFrame(index=range(3), columns=cols)
+        with pytest.raises(ValueError, match="Columns must be same length as key"):
+            df[["A"]] = (0, 3, 5)
+
+        df2 = df.iloc[:, :3]  # unique columns
+        with pytest.raises(ValueError, match="Columns must be same length as key"):
+            df2[["A"]] = (0, 3, 5)
+
     @pytest.mark.parametrize("cols", [["a", "b", "c"], ["a", "a", "a"]])
     def test_setitem_df_wrong_column_number(self, cols):
         # GH#38604
@@ -890,3 +913,47 @@ def test_setitem_clear_caches(self):
 
         assert df["z"] is not foo
         tm.assert_series_equal(df["z"], expected)
+
+    def test_setitem_duplicate_columns_not_inplace(self):
+        # GH#39510
+        cols = ["A", "B"] * 2
+        df = DataFrame(0.0, index=[0], columns=cols)
+        df_copy = df.copy()
+        df_view = df[:]
+        df["B"] = (2, 5)
+
+        expected = DataFrame([[0.0, 2, 0.0, 5]], columns=cols)
+        tm.assert_frame_equal(df_view, df_copy)
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize("value", [1, np.array([[1], [1]]), [[1], [1]]])
+    def test_setitem_same_dtype_not_inplace(self, value, using_array_manager, request):
+        # GH#39510
+        if not using_array_manager:
+            mark = pytest.mark.xfail(
+                reason="Setitem with same dtype still changing inplace"
+            )
+            request.node.add_marker(mark)
+
+        cols = ["A", "B"]
+        df = DataFrame(0, index=[0, 1], columns=cols)
+        df_copy = df.copy()
+        df_view = df[:]
+        df[["B"]] = value
+
+        expected = DataFrame([[0, 1], [0, 1]], columns=cols)
+        tm.assert_frame_equal(df, expected)
+        tm.assert_frame_equal(df_view, df_copy)
+
+    @pytest.mark.parametrize("value", [1.0, np.array([[1.0], [1.0]]), [[1.0], [1.0]]])
+    def test_setitem_listlike_key_scalar_value_not_inplace(self, value):
+        # GH#39510
+        cols = ["A", "B"]
+        df = DataFrame(0, index=[0, 1], columns=cols)
+        df_copy = df.copy()
+        df_view = df[:]
+        df[["B"]] = value
+
+        expected = DataFrame([[0, 1.0], [0, 1.0]], columns=cols)
+        tm.assert_frame_equal(df_view, df_copy)
+        tm.assert_frame_equal(df, expected)

pandas/tests/indexing/test_indexing.py

@@ -115,7 +115,14 @@ def test_setitem_ndarray_3d(self, index, frame_or_series, indexer_sli):
             )
         else:
             err = ValueError
-            msg = r"Buffer has wrong number of dimensions \(expected 1, got 3\)|"
+            msg = "|".join(
+                [
+                    r"Buffer has wrong number of dimensions \(expected 1, got 3\)",
+                    "Cannot set values with ndim > 1",
+                    "Index data must be 1-dimensional",
+                    "Array conditional must be same shape as self",
+                ]
+            )
 
         with pytest.raises(err, match=msg):
             idxr[nd3] = 0

pandas/tests/reshape/test_pivot.py

@@ -966,7 +966,7 @@ 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).reshape(len(df), 3).astype("i8")
 
         mi_val = list(product(["bar", "foo"], ["one", "two"])) + [("All", "")]
         mi = MultiIndex.from_tuples(mi_val, names=("A", "B"))