BUG: Dtype was not changed when float was assignt to int column

doc/source/whatsnew/v1.2.0.rst

@@ -584,6 +584,7 @@ Indexing
 - Bug in :meth:`DataFrame.loc` returned requested key plus missing values when ``loc`` was applied to single level from :class:`MultiIndex` (:issue:`27104`)
 - Bug in indexing on a :class:`Series` or :class:`DataFrame` with a :class:`CategoricalIndex` using a listlike indexer containing NA values (:issue:`37722`)
 - Bug in :meth:`DataFrame.xs` ignored ``droplevel=False`` for columns (:issue:`19056`)
+- Bug in :meth:`DataFrame.at` and :meth:`Series.at` did not adjust dtype when float was assigned to integer column (:issue:`26395`, :issue:`20643`)
 
 Missing
 ^^^^^^^

pandas/core/frame.py

@@ -91,7 +91,6 @@
     maybe_downcast_to_dtype,
     maybe_infer_to_datetimelike,
     maybe_upcast,
-    validate_numeric_casting,
 )
 from pandas.core.dtypes.common import (
     ensure_int64,
@@ -3206,13 +3205,8 @@ def _set_value(self, index, col, value, takeable: bool = False):
                 return
 
             series = self._get_item_cache(col)
-            engine = self.index._engine
-            loc = engine.get_loc(index)
-            validate_numeric_casting(series.dtype, value)
-
-            series._values[loc] = value
-            # Note: trying to use series._set_value breaks tests in
-            #  tests.frame.indexing.test_indexing and tests.indexing.test_partial
+            self.index._engine.get_loc(index)
+            series._set_value(index, value, takeable)
         except (KeyError, TypeError):
             # set using a non-recursive method & reset the cache
             if takeable:

pandas/core/series.py

@@ -42,6 +42,7 @@
 
 from pandas.core.dtypes.cast import (
     convert_dtypes,
+    infer_dtype_from_scalar,
     maybe_cast_to_extension_array,
     validate_numeric_casting,
 )
@@ -50,10 +51,12 @@
     is_bool,
     is_categorical_dtype,
     is_dict_like,
+    is_dtype_equal,
     is_extension_array_dtype,
     is_integer,
     is_iterator,
     is_list_like,
+    is_numeric_dtype,
     is_object_dtype,
     is_scalar,
     validate_all_hashable,
@@ -1043,7 +1046,15 @@ def _set_with_engine(self, key, value):
         # fails with AttributeError for IntervalIndex
         loc = self.index._engine.get_loc(key)
         validate_numeric_casting(self.dtype, value)
-        self._values[loc] = value
+        dtype, _ = infer_dtype_from_scalar(value)
+        if is_dtype_equal(self.dtype, dtype):
+            self._values[loc] = value
+        else:
+            # This only raises when index contains tuples
+            try:
+                self.loc[key] = value
+            except KeyError:
+                self._values[loc] = value
 
     def _set_with(self, key, value):
         # other: fancy integer or otherwise
@@ -1105,8 +1116,16 @@ def _set_value(self, label, value, takeable: bool = False):
         takeable : interpret the index as indexers, default False
         """
         try:
-            if takeable:
+            dtype, _ = infer_dtype_from_scalar(value, pandas_dtype=True)
+            if takeable and is_dtype_equal(self.dtype, dtype):
                 self._values[label] = value
+            elif takeable:
+                self.iloc[label] = value
+            elif not is_dtype_equal(self.dtype, dtype) and is_numeric_dtype(dtype):
+                loc = self.index.get_loc(label)
+                validate_numeric_casting(self.dtype, value)
+                self.loc[label] = value
+                return
             else:
                 loc = self.index.get_loc(label)
                 validate_numeric_casting(self.dtype, value)

pandas/tests/indexing/test_at.py

@@ -126,3 +126,33 @@ def test_at_getitem_mixed_index_no_fallback(self):
             ser.at[0]
         with pytest.raises(KeyError, match="^4$"):
             ser.at[4]
+
+
+@pytest.mark.parametrize("func", ["at", "loc"])
+def test_at_assign_float_to_int_frame(func):
+    # GH: 26395
+    obj = DataFrame([0, 0, 0], index=["A", "B", "C"], columns=["D"])
+    getattr(obj, func)["C", "D"] = 44.5
+    expected = DataFrame([0, 0, 44.5], index=["A", "B", "C"], columns=["D"])
+    tm.assert_frame_equal(obj, expected)
+
+
+@pytest.mark.parametrize("func", ["at", "loc"])
+def test_at_assign_float_to_int_series(func):
+    # GH: 26395
+    obj = Series([0, 0, 0], index=["A", "B", "C"])
+    getattr(obj, func)["C"] = 44.5
+    expected = Series([0, 0, 44.5], index=["A", "B", "C"])
+    tm.assert_series_equal(obj, expected)
+
+
+def test_assign_float_to_int_series_takeable():
+    # GH: 20643
+    ser = Series([0, 1, 2], index=list("abc"))
+    ser.iat[1] = 3.1
+    expected = Series([0, 3.1, 2], index=list("abc"))
+    tm.assert_series_equal(ser, expected)
+
+    ser = Series([0, 1, 2], index=list("abc"))
+    ser.at["b"] = 3.1
+    tm.assert_series_equal(ser, expected)

pandas/tests/indexing/test_coercion.py

@@ -114,31 +114,17 @@ def test_setitem_series_int64(self, val, exp_dtype, request):
         obj = pd.Series([1, 2, 3, 4])
         assert obj.dtype == np.int64
 
-        if exp_dtype is np.float64:
-            exp = pd.Series([1, 1, 3, 4])
-            self._assert_setitem_series_conversion(obj, 1.1, exp, np.int64)
-            mark = pytest.mark.xfail(reason="GH12747 The result must be float")
-            request.node.add_marker(mark)
-
         exp = pd.Series([1, val, 3, 4])
         self._assert_setitem_series_conversion(obj, val, exp, exp_dtype)
 
     @pytest.mark.parametrize(
-        "val,exp_dtype", [(np.int32(1), np.int8), (np.int16(2 ** 9), np.int16)]
+        "val,exp_dtype", [(np.int32(1), np.int32), (np.int16(2 ** 9), np.int16)]
     )
     def test_setitem_series_int8(self, val, exp_dtype, request):
         obj = pd.Series([1, 2, 3, 4], dtype=np.int8)
         assert obj.dtype == np.int8
 
-        if exp_dtype is np.int16:
-            exp = pd.Series([1, 0, 3, 4], dtype=np.int8)
-            self._assert_setitem_series_conversion(obj, val, exp, np.int8)
-            mark = pytest.mark.xfail(
-                reason="BUG: it must be pd.Series([1, 1, 3, 4], dtype=np.int16"
-            )
-            request.node.add_marker(mark)
-
-        exp = pd.Series([1, val, 3, 4], dtype=np.int8)
+        exp = pd.Series([1, val, 3, 4], dtype=exp_dtype)
         self._assert_setitem_series_conversion(obj, val, exp, exp_dtype)
 
     @pytest.mark.parametrize(
@@ -171,33 +157,17 @@ def test_setitem_series_complex128(self, val, exp_dtype):
     @pytest.mark.parametrize(
         "val,exp_dtype",
         [
-            (1, np.int64),
-            (3, np.int64),
-            (1.1, np.float64),
-            (1 + 1j, np.complex128),
+            (1, "object"),
+            (3, "object"),
+            (1.1, "object"),
+            (1 + 1j, "object"),
             (True, np.bool_),
         ],
     )
     def test_setitem_series_bool(self, val, exp_dtype, request):
         obj = pd.Series([True, False, True, False])
         assert obj.dtype == np.bool_
 
-        mark = None
-        if exp_dtype is np.int64:
-            exp = pd.Series([True, True, True, False])
-            self._assert_setitem_series_conversion(obj, val, exp, np.bool_)
-            mark = pytest.mark.xfail(reason="TODO_GH12747 The result must be int")
-        elif exp_dtype is np.float64:
-            exp = pd.Series([True, True, True, False])
-            self._assert_setitem_series_conversion(obj, val, exp, np.bool_)
-            mark = pytest.mark.xfail(reason="TODO_GH12747 The result must be float")
-        elif exp_dtype is np.complex128:
-            exp = pd.Series([True, True, True, False])
-            self._assert_setitem_series_conversion(obj, val, exp, np.bool_)
-            mark = pytest.mark.xfail(reason="TODO_GH12747 The result must be complex")
-        if mark is not None:
-            request.node.add_marker(mark)
-
         exp = pd.Series([True, val, True, False])
         self._assert_setitem_series_conversion(obj, val, exp, exp_dtype)
 

pandas/tests/indexing/test_partial.py

@@ -47,7 +47,6 @@ def test_partial_setting(self):
         with pytest.raises(IndexError, match=msg):
             s.iloc[3] = 5.0
 
-        msg = "index 3 is out of bounds for axis 0 with size 3"
         with pytest.raises(IndexError, match=msg):
             s.iat[3] = 5.0
 

pandas/tests/series/indexing/test_setitem.py

@@ -266,3 +266,11 @@ def test_setitem_slice_into_readonly_backing_data():
         series[1:3] = 1
 
     assert not array.any()
+
+
+def test_setitem_float_to_int():
+    # GH 20643
+    ser = Series([0, 1, 2], index=list("abc"))
+    ser["b"] = 3.1
+    expected = Series([0, 3.1, 2], index=list("abc"))
+    tm.assert_series_equal(ser, expected)