[SPARK-55818][PANDAS/PS] Decimal-float mixed arithmetic should always raise TypeError

python/pyspark/pandas/data_type_ops/num_ops.py

@@ -439,6 +439,35 @@ class FractionalOps(NumericOps):
     def pretty_name(self) -> str:
         return "fractions"
 
+    def add(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
+        _sanitize_list_like(right)
+        if not is_valid_operand_for_numeric_arithmetic(right):
+            raise TypeError("Addition can not be applied to given types.")
+        # Always raise TypeError for decimal-float mixed operations (SPARK-55818)
+        # This matches pandas behavior regardless of ANSI mode.
+        if _is_decimal_float_mixed(left, right):
+            raise TypeError("Addition can not be applied to given types.")
+        new_right = transform_boolean_operand_to_numeric(right, spark_type=left.spark.data_type)
+
+        def wrapped_add(lc: PySparkColumn, rc: Any) -> PySparkColumn:
+            return _cast_back_float(PySparkColumn.__add__(lc, rc), left.dtype, right)
+
+        return column_op(wrapped_add)(left, new_right)
+
+    def sub(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
+        _sanitize_list_like(right)
+        if not is_valid_operand_for_numeric_arithmetic(right):
+            raise TypeError("Subtraction can not be applied to given types.")
+        # Always raise TypeError for decimal-float mixed operations (SPARK-55818)
+        if _is_decimal_float_mixed(left, right):
+            raise TypeError("Subtraction can not be applied to given types.")
+        new_right = transform_boolean_operand_to_numeric(right, spark_type=left.spark.data_type)
+
+        def wrapped_sub(lc: PySparkColumn, rc: Any) -> PySparkColumn:
+            return _cast_back_float(PySparkColumn.__sub__(lc, rc), left.dtype, right)
+
+        return column_op(wrapped_sub)(left, new_right)
+
     def mul(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
         _sanitize_list_like(right)
         if not is_valid_operand_for_numeric_arithmetic(right):

python/pyspark/pandas/tests/data_type_ops/test_decimal_float_arithmetic.py

@@ -0,0 +1,86 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import decimal
+
+from pyspark import pandas as ps
+from pyspark.testing.pandasutils import PandasOnSparkTestCase
+
+
+# This file contains test cases for SPARK-55818:
+# "Decimal-float mixed arithmetic should always raise TypeError"
+# https://issues.apache.org/jira/browse/SPARK-55818
+class DecimalFloatArithmeticMixin:
+    """
+    Tests that float Series + decimal.Decimal scalar always raises TypeError,
+    matching native pandas behavior, regardless of ANSI mode.
+    """
+
+    def test_float_add_decimal_raises(self):
+        psser = ps.Series([1.0, 2.0, 3.0])
+        with self.assertRaises(TypeError):
+            psser + decimal.Decimal("1.5")
+
+    def test_float_sub_decimal_raises(self):
+        psser = ps.Series([1.0, 2.0, 3.0])
+        with self.assertRaises(TypeError):
+            psser - decimal.Decimal("1.5")
+
+    def test_float_mul_decimal_raises(self):
+        psser = ps.Series([1.0, 2.0, 3.0])
+        with self.assertRaises(TypeError):
+            psser * decimal.Decimal("2")
+
+    def test_float_truediv_decimal_raises(self):
+        psser = ps.Series([1.0, 2.0, 3.0])
+        with self.assertRaises(TypeError):
+            psser / decimal.Decimal("2")
+
+    def test_float_floordiv_decimal_raises(self):
+        psser = ps.Series([1.0, 2.0, 3.0])
+        with self.assertRaises(TypeError):
+            psser // decimal.Decimal("2")
+
+    def test_float_mod_decimal_raises(self):
+        psser = ps.Series([1.0, 2.0, 3.0])
+        with self.assertRaises(TypeError):
+            psser % decimal.Decimal("2")
+
+    def test_radd_decimal_raises(self):
+        psser = ps.Series([1.0, 2.0, 3.0])
+        with self.assertRaises(TypeError):
+            decimal.Decimal("1.5") + psser
+
+    def test_rsub_decimal_raises(self):
+        psser = ps.Series([1.0, 2.0, 3.0])
+        with self.assertRaises(TypeError):
+            decimal.Decimal("1.5") - psser
+
+    def test_rmul_decimal_raises(self):
+        psser = ps.Series([1.0, 2.0, 3.0])
+        with self.assertRaises(TypeError):
+            decimal.Decimal("2") * psser
+
+
+class DecimalFloatArithmeticTests(DecimalFloatArithmeticMixin, PandasOnSparkTestCase):
+    pass
+
+
+if __name__ == "__main__":
+    import unittest
+
+    unittest.main()