BUG: Dataframe arithmatic operators don't work with Series using fill_value

doc/source/whatsnew/v3.0.0.rst

@@ -1082,7 +1082,7 @@ MultiIndex
 - :meth:`MultiIndex.insert` would not insert NA value correctly at unified location of index -1 (:issue:`59003`)
 - :func:`MultiIndex.get_level_values` accessing a :class:`DatetimeIndex` does not carry the frequency attribute along (:issue:`58327`, :issue:`57949`)
 - Bug in :class:`DataFrame` arithmetic operations in case of unaligned MultiIndex columns (:issue:`60498`)
-- Bug in :class:`DataFrame` arithmetic operations with :class:`Series` in case of unaligned MultiIndex (:issue:`61009`)
+- Bug in :class:`DataFrame` arithmetic operations with :class:`Series` now works with ``fill_value`` parameter (:issue:`61581`)
 - Bug in :meth:`MultiIndex.union` raising when indexes have duplicates with differing names (:issue:`62059`)
 - Bug in :meth:`MultiIndex.from_tuples` causing wrong output with input of type tuples having NaN values (:issue:`60695`, :issue:`60988`)
 - Bug in :meth:`DataFrame.__setitem__` where column alignment logic would reindex the assigned value with an empty index, incorrectly setting all values to ``NaN``.(:issue:`61841`)

pandas/core/frame.py

@@ -8491,7 +8491,10 @@ def to_series(right):
             # pass dtype to avoid doing inference, which would break consistency
             #  with Index/Series ops
             dtype = None
-            if getattr(right, "dtype", None) == object:
+            if (
+                getattr(right, "dtype", None) == "object"
+                or getattr(right, "dtype", None) == object
+            ):
                 # can't pass right.dtype unconditionally as that would break on e.g.
                 #  datetime64[h] ndarray
                 dtype = object
@@ -8595,27 +8598,34 @@ def _maybe_align_series_as_frame(self, series: Series, axis: AxisInt):
         blockwise.
         """
         rvalues = series._values
-        if not isinstance(rvalues, np.ndarray):
-            # TODO(EA2D): no need to special-case with 2D EAs
-            if rvalues.dtype in ("datetime64[ns]", "timedelta64[ns]"):
-                # We can losslessly+cheaply cast to ndarray
-                rvalues = np.asarray(rvalues)
+        if lib.is_np_dtype(rvalues.dtype):
+            # We can losslessly+cheaply cast to ndarray
+            # i.e. ndarray or dt64[naive], td64
+            # TODO(EA2D): no need to special case with 2D EAs
+            rvalues = np.asarray(rvalues)
+
+            if axis == 0:
+                rvalues = rvalues.reshape(-1, 1)
             else:
-                return series
+                rvalues = rvalues.reshape(1, -1)
 
-        if axis == 0:
-            rvalues = rvalues.reshape(-1, 1)
-        else:
-            rvalues = rvalues.reshape(1, -1)
+            rvalues = np.broadcast_to(rvalues, self.shape)
+            # pass dtype to avoid doing inference
+            df = self._constructor(rvalues, dtype=rvalues.dtype)
 
-        rvalues = np.broadcast_to(rvalues, self.shape)
-        # pass dtype to avoid doing inference
-        return self._constructor(
-            rvalues,
-            index=self.index,
-            columns=self.columns,
-            dtype=rvalues.dtype,
-        ).__finalize__(series)
+        else:
+            # GH#61581
+            if axis == 0:
+                df = DataFrame(dict.fromkeys(range(self.shape[1]), rvalues))
+            else:
+                nrows = self.shape[0]
+                df = DataFrame(
+                    {i: rvalues[[i]].repeat(nrows) for i in range(self.shape[1])},
+                    dtype=rvalues.dtype,
+                )
+        df.index = self.index
+        df.columns = self.columns
+        return df.__finalize__(series)
 
     def _flex_arith_method(
         self, other, op, *, axis: Axis = "columns", level=None, fill_value=None
@@ -8625,11 +8635,6 @@ def _flex_arith_method(
         if self._should_reindex_frame_op(other, op, axis, fill_value, level):
             return self._arith_method_with_reindex(other, op)
 
-        if isinstance(other, Series) and fill_value is not None:
-            # TODO: We could allow this in cases where we end up going
-            #  through the DataFrame path
-            raise NotImplementedError(f"fill_value {fill_value} not supported.")
-
         other = ops.maybe_prepare_scalar_for_op(other, self.shape)
         self, other = self._align_for_op(other, axis, flex=True, level=level)
 

pandas/tests/arithmetic/test_period.py

@@ -1361,12 +1361,7 @@ def test_period_add_timestamp_raises(self, box_with_array):
             arr + ts
         with pytest.raises(TypeError, match=msg):
             ts + arr
-        if box_with_array is pd.DataFrame:
-            # TODO: before implementing resolution-inference we got the same
-            #  message with DataFrame and non-DataFrame.  Why did that change?
-            msg = "cannot add PeriodArray and Timestamp"
-        else:
-            msg = "cannot add PeriodArray and DatetimeArray"
+        msg = "cannot add PeriodArray and DatetimeArray"
         with pytest.raises(TypeError, match=msg):
             arr + Series([ts])
         with pytest.raises(TypeError, match=msg):
@@ -1376,16 +1371,9 @@ def test_period_add_timestamp_raises(self, box_with_array):
         with pytest.raises(TypeError, match=msg):
             pd.Index([ts]) + arr
 
-        if box_with_array is pd.DataFrame:
-            msg = "cannot add PeriodArray and DatetimeArray"
-        else:
-            msg = r"unsupported operand type\(s\) for \+: 'Period' and 'DatetimeArray"
+        msg = "cannot add PeriodArray and DatetimeArray"
         with pytest.raises(TypeError, match=msg):
             arr + pd.DataFrame([ts])
-        if box_with_array is pd.DataFrame:
-            msg = "cannot add PeriodArray and DatetimeArray"
-        else:
-            msg = r"unsupported operand type\(s\) for \+: 'DatetimeArray' and 'Period'"
         with pytest.raises(TypeError, match=msg):
             pd.DataFrame([ts]) + arr
 

pandas/tests/arithmetic/test_string.py

@@ -244,9 +244,6 @@ def test_mul(any_string_dtype):
 
 def test_add_strings(any_string_dtype, request):
     dtype = any_string_dtype
-    if dtype != np.dtype(object):
-        mark = pytest.mark.xfail(reason="GH-28527")
-        request.applymarker(mark)
     arr = pd.array(["a", "b", "c", "d"], dtype=dtype)
     df = pd.DataFrame([["t", "y", "v", "w"]], dtype=object)
     assert arr.__add__(df) is NotImplemented
@@ -261,10 +258,15 @@ def test_add_strings(any_string_dtype, request):
 
 
 @pytest.mark.xfail(reason="GH-28527")
-def test_add_frame(dtype):
+def test_add_frame(request, dtype):
+    if dtype.storage == "python":
+        # Inconsistent behavior between different versions of the python engine.
+        # Some return correctly, some return but with the wrong dtype
+        # Others just fail, we are blanket failing all
+        mark = pytest.mark.xfail(reason="[XPASS(strict)] GH-28527")
+        request.node.applymarker(mark)
     arr = pd.array(["a", "b", np.nan, np.nan], dtype=dtype)
     df = pd.DataFrame([["x", np.nan, "y", np.nan]])
-
     assert arr.__add__(df) is NotImplemented
 
     result = arr + df

pandas/tests/arrays/boolean/test_arithmetic.py

@@ -118,7 +118,7 @@ def test_error_invalid_values(data, all_arithmetic_operators):
         ops(pd.Timestamp("20180101"))
 
     # invalid array-likes
-    if op not in ("__mul__", "__rmul__"):
+    if op not in ("__mul__", "__rmul__", "__add__", "__radd__"):
         # TODO(extension) numpy's mul with object array sees booleans as numbers
         msg = "|".join(
             [

pandas/tests/arrays/floating/test_arithmetic.py

@@ -160,8 +160,9 @@ def test_error_invalid_values(data, all_arithmetic_operators):
         ops(pd.Timestamp("20180101"))
 
     # invalid array-likes
+    str_ser = pd.Series("foo", index=s.index)
     with pytest.raises(TypeError, match=msg):
-        ops(pd.Series("foo", index=s.index))
+        ops(str_ser)
 
     msg = "|".join(
         [

pandas/tests/arrays/string_/test_string.py

@@ -258,8 +258,7 @@ def test_astype_float(dtype, any_float_dtype):
     # Don't compare arrays (37974)
     ser = pd.Series(["1.1", pd.NA, "3.3"], dtype=dtype)
     result = ser.astype(any_float_dtype)
-    item = np.nan if isinstance(result.dtype, np.dtype) else pd.NA
-    expected = pd.Series([1.1, item, 3.3], dtype=any_float_dtype)
+    expected = pd.Series([1.1, np.nan, 3.3], dtype=any_float_dtype)
     tm.assert_series_equal(result, expected)
 
 

pandas/tests/frame/test_arithmetic.py

@@ -629,11 +629,43 @@ def test_arith_flex_frame_corner(self, float_frame):
         expected = float_frame.sort_index() * np.nan
         tm.assert_frame_equal(result, expected)
 
-        with pytest.raises(NotImplementedError, match="fill_value"):
-            float_frame.add(float_frame.iloc[0], fill_value=3)
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_arith_flex_frame_fill_value_series(self, float_frame, axis):
+        rng = np.random.default_rng(60)
+        mask = rng.random(float_frame.shape) < 0.2
+        left = float_frame.mask(mask)
+        right = left.iloc[0]
+
+        result = left.add(right, axis=axis, fill_value=3)
+
+        if axis == 0:  # axis = index, vertical
+            pad_num = abs(result.shape[0] - len(right))
+            mult_num = result.shape[1]
+            right_pad = np.pad(
+                right, (0, pad_num), mode="constant", constant_values=(np.nan)
+            )
+            right_df = DataFrame(
+                [right_pad] * mult_num, columns=result.index, index=result.columns
+            ).T
+
+            left = left.reindex_like(result)
+
+        else:  # axis = columns, horizontal
+            pad_num = abs(result.shape[1] - len(right))
+            mult_num = result.shape[0]
+            right_pad = np.pad(
+                right, (0, pad_num), mode="constant", constant_values=(np.nan)
+            )
+            right_df = DataFrame(
+                [right_pad] * mult_num, index=result.index, columns=result.columns
+            )
 
-        with pytest.raises(NotImplementedError, match="fill_value"):
-            float_frame.add(float_frame.iloc[0], axis="index", fill_value=3)
+        left_filled = left.fillna(3)
+        right_filled = right_df.fillna(3)
+        expected = right_filled + left_filled
+        expected = expected.mask(expected == 6, pd.NA)
+
+        tm.assert_frame_equal(result, expected)
 
     @pytest.mark.parametrize("op", ["add", "sub", "mul", "mod"])
     def test_arith_flex_series_ops(self, simple_frame, op):
@@ -675,11 +707,21 @@ def test_arith_flex_zero_len_raises(self):
         df_len0 = DataFrame(columns=["A", "B"])
         df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
 
-        with pytest.raises(NotImplementedError, match="fill_value"):
+        msg = r"unsupported operand type\(s\) for \+: 'int' and 'str'"
+        with pytest.raises(TypeError, match=msg):
             df.add(ser_len0, fill_value="E")
 
-        with pytest.raises(NotImplementedError, match="fill_value"):
-            df_len0.sub(df["A"], axis=None, fill_value=3)
+        result = df_len0.sub(df, axis=None, fill_value=3)
+        expected = DataFrame([[2, 1], [0, -1]], columns=["A", "B"])
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+        result = df_len0.sub(df["A"], axis=0, fill_value=3)
+        expected = DataFrame([[2, 2], [0, 0]], columns=["A", "B"])
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+        result = df_len0.sub(df["A"], axis=1, fill_value=3)
+        expected = DataFrame([], columns=["A", "B", 0, 1])
+        tm.assert_frame_equal(result, expected, check_dtype=False)
 
     def test_flex_add_scalar_fill_value(self):
         # GH#12723
@@ -2201,3 +2243,54 @@ def test_mixed_col_index_dtype(string_dtype_no_object):
     expected.columns = expected.columns.astype(string_dtype_no_object)
 
     tm.assert_frame_equal(result, expected)
+
+
+dt_params = [
+    (tm.ALL_INT_NUMPY_DTYPES[0], 10),
+    (tm.ALL_INT_EA_DTYPES[0], 10),
+    (tm.FLOAT_NUMPY_DTYPES[0], 4.9),
+    (tm.FLOAT_EA_DTYPES[0], 4.9),
+]
+
+axes = [0, 1]
+
+
+@pytest.mark.parametrize(
+    "data_type,fill_val, axis",
+    [(dt, val, axis) for axis in axes for dt, val in dt_params],
+)
+def test_df_mul_array_fill_value(data_type, fill_val, axis):
+    # GH 61581
+    base_data = np.arange(12).reshape(4, 3)
+    df = DataFrame(base_data)
+    mult_list = [np.nan, 1, 5, np.nan]
+    mult_list = mult_list[: df.shape[axis]]
+
+    if data_type in tm.ALL_INT_NUMPY_DTYPES:
+        # Numpy int type cannot represent NaN
+        mult_np = np.asarray(mult_list)
+        mult_list = np.nan_to_num(mult_np, nan=fill_val)
+
+    mult_data = pd.array(mult_list, dtype=data_type)
+
+    for i in range(df.shape[0]):
+        try:
+            df.iat[i, i] = np.nan
+            df.iat[i + 2, i] = pd.NA
+        except IndexError:
+            pass
+
+    if axis == 0:
+        mult_mat = np.broadcast_to(mult_data.reshape(-1, 1), df.shape)
+        mask = np.isnan(mult_mat)
+    else:
+        mult_mat = np.broadcast_to(mult_data.reshape(1, -1), df.shape)
+        mask = np.isnan(mult_mat)
+    mask = df.isna().values & mask
+
+    df_result = df.mul(mult_data, axis=axis, fill_value=fill_val)
+    df_expected = (df.fillna(fill_val).mul(mult_data.fillna(fill_val), axis=axis)).mask(
+        mask, np.nan
+    )
+
+    tm.assert_frame_equal(df_result, df_expected)