DEPR: df.iloc[:, foo] = bar attempt to set inplace

doc/source/user_guide/10min.rst

@@ -328,6 +328,7 @@ Setting values by position:
 Setting by assigning with a NumPy array:
 
 .. ipython:: python
+   :okwarning:
 
    df.loc[:, "D"] = np.array([5] * len(df))
 

doc/source/whatsnew/v1.5.0.rst

@@ -533,6 +533,73 @@ As ``group_keys=True`` is the default value of :meth:`DataFrame.groupby` and
 raise a ``FutureWarning``. This can be silenced and the previous behavior
 retained by specifying ``group_keys=False``.
 
+.. _whatsnew_150.notable_bug_fixes.setitem_column_try_inplace:
+   _ see also _whatsnew_130.notable_bug_fixes.setitem_column_try_inplace
+
+Try operating inplace when setting values with ``loc`` and ``iloc``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Most of the time setting values with ``frame.iloc`` attempts to set values
+in-place, only falling back to inserting a new array if necessary. In the past,
+setting entire columns has been an exception to this rule:
+
+.. ipython:: python
+
+   values = np.arange(4).reshape(2, 2)
+   df = pd.DataFrame(values)
+   ser = df[0]
+
+*Old behavior*:
+
+.. code-block:: ipython
+
+    In [3]: df.iloc[:, 0] = np.array([10, 11])
+    In [4]: ser
+    Out[4]:
+    0    0
+    1    2
+    Name: 0, dtype: int64
+
+This behavior is deprecated. In a future version, setting an entire column with
+iloc will attempt to operate inplace.
+
+*Future behavior*:
+
+.. code-block:: ipython
+
+    In [3]: df.iloc[:, 0] = np.array([10, 11])
+    In [4]: ser
+    Out[4]:
+    0    10
+    1    11
+    Name: 0, dtype: int64
+
+To get the old behavior, use :meth:`DataFrame.__setitem__` directly:
+
+*Future behavior*:
+
+.. code-block:: ipython
+
+    In [5]: df[0] = np.array([21, 31])
+    In [4]: ser
+    Out[4]:
+    0    10
+    1    11
+    Name: 0, dtype: int64
+
+In the case where ``df.columns`` is not unique, use :meth:`DataFrame.isetitem`:
+
+*Future behavior*:
+
+.. code-block:: ipython
+
+    In [5]: df.columns = ["A", "A"]
+    In [5]: df.isetitem(0, np.array([21, 31]))
+    In [4]: ser
+    Out[4]:
+    0    10
+    1    11
+    Name: 0, dtype: int64
+
 .. _whatsnew_150.deprecations.numeric_only_default:
 
 ``numeric_only`` default value

pandas/core/frame.py

@@ -3687,6 +3687,29 @@ def _get_value(self, index, col, takeable: bool = False) -> Scalar:
         loc = engine.get_loc(index)
         return series._values[loc]
 
+    def isetitem(self, loc, value) -> None:
+        """
+        Set the given value in the column with position 'loc'.
+
+        This is a positional analogue to __setitem__.
+
+        Parameters
+        ----------
+        loc : int or sequence of ints
+        value : scalar or arraylike
+
+        Notes
+        -----
+        Unlike `frame.iloc[:, i] = value`, `frame.isetitem(loc, value)` will
+        _never_ try to set the values in place, but will always insert a new
+        array.
+
+        In cases where `frame.columns` is unique, this is equivalent to
+        `frame[frame.columns[i]] = value`.
+        """
+        arraylike = self._sanitize_column(value)
+        self._iset_item_mgr(loc, arraylike, inplace=False)
+
     def __setitem__(self, key, value):
         key = com.apply_if_callable(key, self)
 
@@ -5467,7 +5490,7 @@ def _replace_columnwise(
                 target, value = mapping[ax[i]]
                 newobj = ser.replace(target, value, regex=regex)
 
-                res.iloc[:, i] = newobj
+                res._iset_item(i, newobj)
 
         if inplace:
             return

pandas/core/groupby/groupby.py

@@ -2257,7 +2257,13 @@ def sem(self, ddof: int = 1, numeric_only: bool | lib.NoDefault = lib.no_default
             counts = self.count()
             result_ilocs = result.columns.get_indexer_for(cols)
             count_ilocs = counts.columns.get_indexer_for(cols)
-            result.iloc[:, result_ilocs] /= np.sqrt(counts.iloc[:, count_ilocs])
+            with warnings.catch_warnings():
+                # TODO(2.0): once iloc[:, foo] = bar depecation is enforced,
+                #  this catching will be unnecessary
+                warnings.filterwarnings(
+                    "ignore", ".*will attempt to set the values inplace.*"
+                )
+                result.iloc[:, result_ilocs] /= np.sqrt(counts.iloc[:, count_ilocs])
         return result
 
     @final

pandas/core/indexing.py

@@ -1937,24 +1937,25 @@ def _setitem_single_column(self, loc: int, value, plane_indexer):
         pi = plane_indexer
 
         ser = self.obj._ixs(loc, axis=1)
+        orig_values = ser._values
 
         # perform the equivalent of a setitem on the info axis
         # as we have a null slice or a slice with full bounds
         # which means essentially reassign to the columns of a
         # multi-dim object
         # GH#6149 (null slice), GH#10408 (full bounds)
         if com.is_null_slice(pi) or com.is_full_slice(pi, len(self.obj)):
-            ser = value
+            pass
         elif (
             is_array_like(value)
             and is_exact_shape_match(ser, value)
             and not is_empty_indexer(pi)
         ):
             if is_list_like(pi):
-                ser = value[np.argsort(pi)]
+                value = value[np.argsort(pi)]
             else:
                 # in case of slice
-                ser = value[pi]
+                value = value[pi]
         else:
             # set the item, first attempting to operate inplace, then
             #  falling back to casting if necessary; see
@@ -1970,8 +1971,40 @@ def _setitem_single_column(self, loc: int, value, plane_indexer):
             self.obj._iset_item(loc, ser)
             return
 
-        # reset the sliced object if unique
-        self.obj._iset_item(loc, ser)
+        # We will not operate in-place, but will attempt to in the future.
+        #  To determine whether we need to issue a FutureWarning, see if the
+        #  setting in-place would work, i.e. behavior will change.
+        warn = can_hold_element(ser._values, value)
+        # Don't issue the warning yet, as we can still trim a few cases where
+        #  behavior will not change.
+
+        self.obj._iset_item(loc, value)
+
+        if warn:
+            new_values = self.obj._ixs(loc, axis=1)._values
+
+            if (
+                isinstance(new_values, np.ndarray)
+                and isinstance(orig_values, np.ndarray)
+                and np.shares_memory(new_values, orig_values)
+            ):
+                # TODO: get something like tm.shares_memory working?
+                # The values were set inplace after all, no need to warn,
+                #  e.g. test_rename_nocopy
+                pass
+            else:
+                warnings.warn(
+                    "In a future version, `df.iloc[:, i] = newvals` will attempt "
+                    "to set the values inplace instead of always setting a new "
+                    "array. To retain the old behavior, use either "
+                    "`df[df.columns[i]] = newvals` or, if columns are non-unique, "
+                    "`df.isetitem(i, newvals)`",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                # TODO: how to get future behavior?
+                # TODO: what if we got here indirectly via loc?
+        return
 
     def _setitem_single_block(self, indexer, value, name: str):
         """
@@ -1981,7 +2014,6 @@ def _setitem_single_block(self, indexer, value, name: str):
 
         info_axis = self.obj._info_axis_number
         item_labels = self.obj._get_axis(info_axis)
-
         if isinstance(indexer, tuple):
 
             # if we are setting on the info axis ONLY
@@ -1996,7 +2028,9 @@ def _setitem_single_block(self, indexer, value, name: str):
                 if len(item_labels.get_indexer_for([col])) == 1:
                     # e.g. test_loc_setitem_empty_append_expands_rows
                     loc = item_labels.get_loc(col)
-                    self.obj._iset_item(loc, value)
+                    # Go through _setitem_single_column to get
+                    #  FutureWarning if relevant.
+                    self._setitem_single_column(loc, value, indexer[0])
                     return
 
             indexer = maybe_convert_ix(*indexer)  # e.g. test_setitem_frame_align

pandas/tests/extension/base/setitem.py

@@ -1,6 +1,13 @@
 import numpy as np
 import pytest
 
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    IntervalDtype,
+    PandasDtype,
+    PeriodDtype,
+)
+
 import pandas as pd
 import pandas._testing as tm
 from pandas.tests.extension.base.base import BaseExtensionTests
@@ -361,6 +368,11 @@ def test_setitem_frame_2d_values(self, data):
         # GH#44514
         df = pd.DataFrame({"A": data})
 
+        # These dtypes have non-broken implementations of _can_hold_element
+        has_can_hold_element = isinstance(
+            data.dtype, (PandasDtype, PeriodDtype, IntervalDtype, DatetimeTZDtype)
+        )
+
         # Avoiding using_array_manager fixture
         #  https://github.com/pandas-dev/pandas/pull/44514#discussion_r754002410
         using_array_manager = isinstance(df._mgr, pd.core.internals.ArrayManager)
@@ -369,13 +381,24 @@ def test_setitem_frame_2d_values(self, data):
 
         orig = df.copy()
 
-        df.iloc[:] = df
+        msg = "will attempt to set the values inplace instead"
+        warn = None
+        if has_can_hold_element and not isinstance(data.dtype, PandasDtype):
+            # PandasDtype excluded because it isn't *really* supported.
+            warn = FutureWarning
+
+        with tm.assert_produces_warning(warn, match=msg):
+            df.iloc[:] = df
         self.assert_frame_equal(df, orig)
 
         df.iloc[:-1] = df.iloc[:-1]
         self.assert_frame_equal(df, orig)
 
-        df.iloc[:] = df.values
+        if isinstance(data.dtype, DatetimeTZDtype):
+            # no warning bc df.values casts to object dtype
+            warn = None
+        with tm.assert_produces_warning(warn, match=msg):
+            df.iloc[:] = df.values
         self.assert_frame_equal(df, orig)
         if not using_array_manager:
             # GH#33457 Check that this setting occurred in-place

pandas/tests/frame/indexing/test_coercion.py

@@ -36,7 +36,10 @@ def test_loc_setitem_multiindex_columns(self, consolidate):
         A.loc[2:3, (1, slice(2, 3))] = np.ones((2, 2), dtype=np.float32)
         assert (A.dtypes == np.float32).all()
 
-        A.loc[0:5, (1, slice(2, 3))] = np.ones((6, 2), dtype=np.float32)
+        msg = "will attempt to set the values inplace instead"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            A.loc[0:5, (1, slice(2, 3))] = np.ones((6, 2), dtype=np.float32)
+
         assert (A.dtypes == np.float32).all()
 
         A.loc[:, (1, slice(2, 3))] = np.ones((6, 2), dtype=np.float32)
@@ -129,7 +132,10 @@ def test_iloc_setitem_unnecesssary_float_upcasting():
     orig = df.copy()
 
     values = df[0].values.reshape(2, 1)
-    df.iloc[:, 0:1] = values
+
+    msg = "will attempt to set the values inplace instead"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.iloc[:, 0:1] = values
 
     tm.assert_frame_equal(df, orig)
 

pandas/tests/frame/indexing/test_indexing.py

@@ -688,7 +688,7 @@ def test_getitem_setitem_boolean_multi(self):
         expected.loc[[0, 2], [1]] = 5
         tm.assert_frame_equal(df, expected)
 
-    def test_getitem_setitem_float_labels(self):
+    def test_getitem_setitem_float_labels(self, using_array_manager):
         index = Index([1.5, 2, 3, 4, 5])
         df = DataFrame(np.random.randn(5, 5), index=index)
 
@@ -771,7 +771,10 @@ def test_getitem_setitem_float_labels(self):
         assert len(result) == 5
 
         cp = df.copy()
-        cp.loc[1.0:5.0] = 0
+        warn = FutureWarning if using_array_manager else None
+        msg = "will attempt to set the values inplace"
+        with tm.assert_produces_warning(warn, match=msg):
+            cp.loc[1.0:5.0] = 0
         result = cp.loc[1.0:5.0]
         assert (result == 0).values.all()
 

pandas/tests/frame/indexing/test_setitem.py

@@ -399,10 +399,14 @@ def test_setitem_frame_length_0_str_key(self, indexer):
 
     def test_setitem_frame_duplicate_columns(self, using_array_manager):
         # GH#15695
+        warn = FutureWarning if using_array_manager else None
+        msg = "will attempt to set the values inplace"
+
         cols = ["A", "B", "C"] * 2
         df = DataFrame(index=range(3), columns=cols)
         df.loc[0, "A"] = (0, 3)
-        df.loc[:, "B"] = (1, 4)
+        with tm.assert_produces_warning(warn, match=msg):
+            df.loc[:, "B"] = (1, 4)
         df["C"] = (2, 5)
         expected = DataFrame(
             [
@@ -769,7 +773,9 @@ def test_setitem_string_column_numpy_dtype_raising(self):
     def test_setitem_empty_df_duplicate_columns(self):
         # GH#38521
         df = DataFrame(columns=["a", "b", "b"], dtype="float64")
-        df.loc[:, "a"] = list(range(2))
+        msg = "will attempt to set the values inplace instead"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            df.loc[:, "a"] = list(range(2))
         expected = DataFrame(
             [[0, np.nan, np.nan], [1, np.nan, np.nan]], columns=["a", "b", "b"]
         )

pandas/tests/frame/indexing/test_where.py

@@ -365,7 +365,7 @@ def test_where_bug_transposition(self):
         result = a.where(do_not_replace, b)
         tm.assert_frame_equal(result, expected)
 
-    def test_where_datetime(self):
+    def test_where_datetime(self, using_array_manager):
 
         # GH 3311
         df = DataFrame(
@@ -385,7 +385,11 @@ def test_where_datetime(self):
 
         expected = df.copy()
         expected.loc[[0, 1], "A"] = np.nan
-        expected.loc[:, "C"] = np.nan
+
+        warn = FutureWarning if using_array_manager else None
+        msg = "will attempt to set the values inplace"
+        with tm.assert_produces_warning(warn, match=msg):
+            expected.loc[:, "C"] = np.nan
         tm.assert_frame_equal(result, expected)
 
     def test_where_none(self):
@@ -513,7 +517,7 @@ def test_where_axis_with_upcast(self):
         assert return_value is None
         tm.assert_frame_equal(result, expected)
 
-    def test_where_axis_multiple_dtypes(self):
+    def test_where_axis_multiple_dtypes(self, using_array_manager):
         # Multiple dtypes (=> multiple Blocks)
         df = pd.concat(
             [
@@ -569,7 +573,10 @@ def test_where_axis_multiple_dtypes(self):
 
         d2 = df.copy().drop(1, axis=1)
         expected = df.copy()
-        expected.loc[:, 1] = np.nan
+        warn = FutureWarning if using_array_manager else None
+        msg = "will attempt to set the values inplace"
+        with tm.assert_produces_warning(warn, match=msg):
+            expected.loc[:, 1] = np.nan
 
         result = df.where(mask, d2)
         tm.assert_frame_equal(result, expected)