WARN: Remove false positive warning for iloc inplaceness

pandas/_testing/__init__.py

@@ -459,7 +459,8 @@ def all_timeseries_index_generator(k: int = 10) -> Iterable[Index]:
 def make_rand_series(name=None, dtype=np.float64) -> Series:
     index = makeStringIndex(_N)
     data = np.random.randn(_N)
-    data = data.astype(dtype, copy=False)
+    with np.errstate(invalid="ignore"):
+        data = data.astype(dtype, copy=False)
     return Series(data, index=index, name=name)
 
 

pandas/core/dtypes/cast.py

@@ -1970,7 +1970,8 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
             if tipo.kind not in ["i", "u"]:
                 if isinstance(element, np.ndarray) and element.dtype.kind == "f":
                     # If all can be losslessly cast to integers, then we can hold them
-                    casted = element.astype(dtype)
+                    with np.errstate(invalid="ignore"):
+                        casted = element.astype(dtype)
                     comp = casted == element
                     if comp.all():
                         # Return the casted values bc they can be passed to

pandas/core/indexing.py

@@ -2016,6 +2016,8 @@ def _setitem_single_column(self, loc: int, value, plane_indexer):
                 and (
                     np.shares_memory(new_values, orig_values)
                     or new_values.shape != orig_values.shape
+                    or not can_hold_element(orig_values, np.nan)
+                    and isna(new_values).any()
                 )
             ):
                 # TODO: get something like tm.shares_memory working?

pandas/tests/frame/indexing/test_indexing.py

@@ -1326,7 +1326,8 @@ def test_loc_expand_empty_frame_keep_midx_names(self):
     def test_loc_setitem_rhs_frame(self, idxr, val):
         # GH#47578
         df = DataFrame({"a": [1, 2]})
-        df.loc[:, "a"] = DataFrame({"a": [val, 11]}, index=[1, 2])
+        with tm.assert_produces_warning(None):
+            df.loc[:, idxr] = DataFrame({"a": [val, 11]}, index=[1, 2])
         expected = DataFrame({"a": [np.nan, val]})
         tm.assert_frame_equal(df, expected)