BUG: failure to cast all-int floating dtypes when setting into int dtypes

doc/source/whatsnew/v1.4.0.rst

@@ -535,6 +535,7 @@ Indexing
 - Bug in :meth:`DataFrame.nlargest` and :meth:`Series.nlargest` where sorted result did not count indexes containing ``np.nan`` (:issue:`28984`)
 - Bug in indexing on a non-unique object-dtype :class:`Index` with an NA scalar (e.g. ``np.nan``) (:issue:`43711`)
 - Bug in :meth:`DataFrame.__setitem__` incorrectly writing into an existing column's array rather than setting a new array when the new dtype and the old dtype match (:issue:`43406`)
+- Bug in setting floating-dtype values into a :class:`Series` with integer dtype failing to set inplace when those values can be losslessly converted to integers (:issue:`44316`)
 - Bug in :meth:`Series.__setitem__` with object dtype when setting an array with matching size and dtype='datetime64[ns]' or dtype='timedelta64[ns]' incorrectly converting the datetime/timedeltas to integers (:issue:`43868`)
 - Bug in :meth:`DataFrame.sort_index` where ``ignore_index=True`` was not being respected when the index was already sorted (:issue:`43591`)
 - Bug in :meth:`Index.get_indexer_non_unique` when index contains multiple ``np.datetime64("NaT")`` and ``np.timedelta64("NaT")`` (:issue:`43869`)

pandas/core/dtypes/cast.py

@@ -2205,6 +2205,14 @@ def can_hold_element(arr: ArrayLike, element: Any) -> bool:
             if tipo.kind not in ["i", "u"]:
                 if is_float(element) and element.is_integer():
                     return True
+
+                if isinstance(element, np.ndarray) and element.dtype.kind == "f":
+                    # If all can be losslessly cast to integers, then we can hold them
+                    #  We do something similar in putmask_smart
+                    casted = element.astype(dtype)
+                    comp = casted == element
+                    return comp.all()
+
                 # Anything other than integer we cannot hold
                 return False
             elif dtype.itemsize < tipo.itemsize:

pandas/core/indexes/numeric.py

@@ -425,6 +425,18 @@ def asi8(self) -> npt.NDArray[np.int64]:
         )
         return self._values.view(self._default_dtype)
 
+    def _validate_fill_value(self, value):
+        # e.g. np.array([1.0]) we want np.array([1], dtype=self.dtype)
+        #  see TestSetitemFloatNDarrayIntoIntegerSeries
+        super()._validate_fill_value(value)
+        if hasattr(value, "dtype") and is_float_dtype(value.dtype):
+            converted = value.astype(self.dtype)
+            if (converted == value).all():
+                # See also: can_hold_element
+                return converted
+            raise TypeError
+        return value
+
 
 class Int64Index(IntegerIndex):
     _index_descr_args = {

pandas/core/internals/blocks.py

@@ -1193,6 +1193,14 @@ def where(self, other, cond) -> list[Block]:
                 values, icond.sum(), other  # type: ignore[arg-type]
             )
             if alt is not other:
+                if is_list_like(other) and len(other) < len(values):
+                    # call np.where with other to get the appropriate ValueError
+                    np.where(~icond, values, other)
+                    raise NotImplementedError(
+                        "This should not be reached; call to np.where above is "
+                        "expected to raise ValueError. Please report a bug at "
+                        "github.com/pandas-dev/pandas"
+                    )
                 result = values.copy()
                 np.putmask(result, icond, alt)
             else:

pandas/tests/dtypes/cast/test_can_hold_element.py

@@ -40,3 +40,16 @@ def test_can_hold_element_range(any_int_numpy_dtype):
     rng = range(10 ** 10, 10 ** 10)
     assert len(rng) == 0
     assert can_hold_element(arr, rng)
+
+
+def test_can_hold_element_int_values_float_ndarray():
+    arr = np.array([], dtype=np.int64)
+
+    element = np.array([1.0, 2.0])
+    assert can_hold_element(arr, element)
+
+    assert not can_hold_element(arr, element + 0.5)
+
+    # integer but not losslessly castable to int64
+    element = np.array([3, 2 ** 65], dtype=np.float64)
+    assert not can_hold_element(arr, element)

pandas/tests/indexing/multiindex/test_setitem.py

@@ -196,16 +196,35 @@ def test_multiindex_assignment(self):
         df.loc[4, "d"] = arr
         tm.assert_series_equal(df.loc[4, "d"], Series(arr, index=[8, 10], name="d"))
 
+    def test_multiindex_assignment_single_dtype(self, using_array_manager):
+        # GH3777 part 2b
         # single dtype
+        arr = np.array([0.0, 1.0])
+
         df = DataFrame(
             np.random.randint(5, 10, size=9).reshape(3, 3),
             columns=list("abc"),
             index=[[4, 4, 8], [8, 10, 12]],
+            dtype=np.int64,
         )
+        view = df["c"].iloc[:2].values
 
+        # arr can be losslessly cast to int, so this setitem is inplace
         df.loc[4, "c"] = arr
-        exp = Series(arr, index=[8, 10], name="c", dtype="float64")
-        tm.assert_series_equal(df.loc[4, "c"], exp)
+        exp = Series(arr, index=[8, 10], name="c", dtype="int64")
+        result = df.loc[4, "c"]
+        tm.assert_series_equal(result, exp)
+        if not using_array_manager:
+            # FIXME(ArrayManager): this correctly preserves dtype,
+            #  but incorrectly is not inplace.
+            # extra check for inplace-ness
+            tm.assert_numpy_array_equal(view, exp.values)
+
+        # arr + 0.5 cannot be cast losslessly to int, so we upcast
+        df.loc[4, "c"] = arr + 0.5
+        result = df.loc[4, "c"]
+        exp = exp + 0.5
+        tm.assert_series_equal(result, exp)
 
         # scalar ok
         df.loc[4, "c"] = 10

pandas/tests/indexing/test_iloc.py

@@ -515,9 +515,18 @@ def test_iloc_setitem_frame_duplicate_columns_multiple_blocks(
         #  but on a DataFrame with multiple blocks
         df = DataFrame([[0, 1], [2, 3]], columns=["B", "B"])
 
+        # setting float values that can be held by existing integer arrays
+        #  is inplace
         df.iloc[:, 0] = df.iloc[:, 0].astype("f8")
+        if not using_array_manager:
+            assert len(df._mgr.blocks) == 1
+
+        # if the assigned values cannot be held by existing integer arrays,
+        #  we cast
+        df.iloc[:, 0] = df.iloc[:, 0] + 0.5
         if not using_array_manager:
             assert len(df._mgr.blocks) == 2
+
         expected = df.copy()
 
         # assign back to self
@@ -892,7 +901,7 @@ def test_iloc_with_boolean_operation(self):
         tm.assert_frame_equal(result, expected)
 
         result.iloc[[False, False, True, True]] /= 2
-        expected = DataFrame([[0.0, 4.0], [8.0, 12.0], [4.0, 5.0], [6.0, np.nan]])
+        expected = DataFrame([[0, 4.0], [8, 12.0], [4, 5.0], [6, np.nan]])
         tm.assert_frame_equal(result, expected)
 
     def test_iloc_getitem_singlerow_slice_categoricaldtype_gives_series(self):

pandas/tests/series/indexing/test_setitem.py

@@ -620,23 +620,27 @@ def test_mask_key(self, obj, key, expected, val, indexer_sli):
         mask[key] = True
 
         obj = obj.copy()
+
+        if is_list_like(val) and len(val) < mask.sum():
+            msg = "boolean index did not match indexed array along dimension"
+            with pytest.raises(IndexError, match=msg):
+                indexer_sli(obj)[mask] = val
+            return
+
         indexer_sli(obj)[mask] = val
         tm.assert_series_equal(obj, expected)
 
     def test_series_where(self, obj, key, expected, val, is_inplace):
-        if is_list_like(val) and len(val) < len(obj):
-            # Series.where is not valid here
-            if isinstance(val, range):
-                return
-
-            # FIXME: The remaining TestSetitemDT64IntoInt that go through here
-            #  are relying on technically-incorrect behavior because Block.where
-            #  uses np.putmask instead of expressions.where in those cases,
-            #  which has different length-checking semantics.
-
         mask = np.zeros(obj.shape, dtype=bool)
         mask[key] = True
 
+        if is_list_like(val) and len(val) < len(obj):
+            # Series.where is not valid here
+            msg = "operands could not be broadcast together with shapes"
+            with pytest.raises(ValueError, match=msg):
+                obj.where(~mask, val)
+            return
+
         orig = obj
         obj = obj.copy()
         arr = obj._values
@@ -1014,6 +1018,39 @@ def inplace(self):
         return True
 
 
+@pytest.mark.parametrize(
+    "val",
+    [
+        np.array([2.0, 3.0]),
+        np.array([2.5, 3.5]),
+        np.array([2 ** 65, 2 ** 65 + 1], dtype=np.float64),  # all ints, but can't cast
+    ],
+)
+class TestSetitemFloatNDarrayIntoIntegerSeries(SetitemCastingEquivalents):
+    @pytest.fixture
+    def obj(self):
+        return Series(range(5), dtype=np.int64)
+
+    @pytest.fixture
+    def key(self):
+        return slice(0, 2)
+
+    @pytest.fixture
+    def inplace(self, val):
+        # NB: this condition is based on currently-harcoded "val" cases
+        return val[0] == 2
+
+    @pytest.fixture
+    def expected(self, val, inplace):
+        if inplace:
+            dtype = np.int64
+        else:
+            dtype = np.float64
+        res_values = np.array(range(5), dtype=dtype)
+        res_values[:2] = val
+        return Series(res_values)
+
+
 def test_setitem_int_as_positional_fallback_deprecation():
     # GH#42215 deprecated falling back to positional on __setitem__ with an
     #  int not contained in the index