[SPARK-52570][PS] Enable divide-by-zero for numeric rmod with ANSI enabled

### What changes were proposed in this pull request?
Enable divide-by-zero for numeric rmod with ANSI enabled

### Why are the changes needed?
Part of https://issues.apache.org/jira/browse/SPARK-52169.

### Does this PR introduce _any_ user-facing change?
Yes.

```py
>>> ps.set_option("compute.fail_on_ansi_mode", False)
>>> ps.set_option("compute.ansi_mode_support", True)

>>> pdf = pd.DataFrame({"a": [0], "b": [False]})
>>> pdf.dtypes
a    int64
b     bool
dtype: object
>>> psdf = ps.from_pandas(pdf)
>>> 1 % psdf["a"]
0   NaN
Name: a, dtype: float64
>>> 1 % psdf["b"]
0   NaN
Name: b, dtype: float64
```

### How was this patch tested?
Unit tests.

```
(dev3.11) spark (bool_mod_new) % SPARK_ANSI_SQL_MODE=true  ./python/run-tests --python-executables=python3.11 --testnames "pyspark.pandas.tests.data_type_ops.test_boolean_ops
...
Tests passed in 4 seconds
```

### Was this patch authored or co-authored using generative AI tooling?
No.

Closes #51275 from xinrong-meng/bool_mod_new.

Authored-by: Xinrong Meng <[email protected]>
Signed-off-by: Xinrong Meng <[email protected]>

Author: xinrong-meng

python/pyspark/pandas/data_type_ops/boolean_ops.py

@@ -21,7 +21,7 @@
 import pandas as pd
 from pandas.api.types import CategoricalDtype
 
-from pyspark.pandas.base import column_op, IndexOpsMixin, numpy_column_op
+from pyspark.pandas.base import column_op, IndexOpsMixin
 from pyspark.pandas._typing import Dtype, IndexOpsLike, SeriesOrIndex
 from pyspark.pandas.data_type_ops.base import (
     DataTypeOps,
@@ -35,7 +35,6 @@
     _is_boolean_type,
 )
 from pyspark.pandas.typedef.typehints import as_spark_type, extension_dtypes, pandas_on_spark_type
-from pyspark.pandas.utils import is_ansi_mode_enabled
 from pyspark.sql import functions as F, Column as PySparkColumn
 from pyspark.sql.types import BooleanType, StringType
 from pyspark.errors import PySparkValueError
@@ -137,21 +136,13 @@ def mod(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
             raise TypeError(
                 "Modulo can not be applied to %s and the given type." % self.pretty_name
             )
-        spark_session = left._internal.spark_frame.sparkSession
-
-        def safe_mod(left_col: PySparkColumn, right_val: Any) -> PySparkColumn:
-            if is_ansi_mode_enabled(spark_session):
-                return F.when(F.lit(right_val == 0), F.lit(None)).otherwise(left_col % right_val)
-            else:
-                return left_col % right_val
-
         if isinstance(right, numbers.Number):
             left = transform_boolean_operand_to_numeric(left, spark_type=as_spark_type(type(right)))
-            return numpy_column_op(safe_mod)(left, right)
+            return left % right
         else:
             assert isinstance(right, IndexOpsMixin)
             left = transform_boolean_operand_to_numeric(left, spark_type=right.spark.data_type)
-            return numpy_column_op(safe_mod)(left, right)
+            return left % right
 
     def pow(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
         _sanitize_list_like(right)
@@ -235,18 +226,7 @@ def rmod(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
         _sanitize_list_like(right)
         if isinstance(right, numbers.Number) and not isinstance(right, bool):
             left = transform_boolean_operand_to_numeric(left, spark_type=as_spark_type(type(right)))
-            spark_session = left._internal.spark_frame.sparkSession
-
-            if is_ansi_mode_enabled(spark_session):
-
-                def safe_rmod(left_col: PySparkColumn, right_val: Any) -> PySparkColumn:
-                    return F.when(left_col != 0, F.pmod(F.lit(right_val), left_col)).otherwise(
-                        F.lit(None)
-                    )
-
-                return numpy_column_op(safe_rmod)(left, right)
-            else:
-                return right % left
+            return right % left
         else:
             raise TypeError(
                 "Modulo can not be applied to %s and the given type." % self.pretty_name

python/pyspark/pandas/data_type_ops/num_ops.py

@@ -160,12 +160,21 @@ def rmod(self, left: IndexOpsLike, right: Any) -> SeriesOrIndex:
         _sanitize_list_like(right)
         if not isinstance(right, numbers.Number):
             raise TypeError("Modulo can not be applied to given types.")
-
-        def rmod(left: PySparkColumn, right: Any) -> PySparkColumn:
-            return ((right % left) + left) % left
+        spark_session = left._internal.spark_frame.sparkSession
 
         right = transform_boolean_operand_to_numeric(right)
-        return column_op(rmod)(left, right)
+
+        def safe_rmod(left_col: PySparkColumn, right_val: Any) -> PySparkColumn:
+            if is_ansi_mode_enabled(spark_session):
+                # Java-style modulo -> Python-style modulo
+                result = F.when(
+                    left_col != 0, ((F.lit(right_val) % left_col) + left_col) % left_col
+                ).otherwise(F.lit(None))
+                return result
+            else:
+                return ((right % left) + left) % left
+
+        return column_op(safe_rmod)(left, right)
 
     def neg(self, operand: IndexOpsLike) -> IndexOpsLike:
         return operand._with_new_scol(-operand.spark.column, field=operand._internal.data_fields[0])

python/pyspark/pandas/tests/computation/test_binary_ops.py

@@ -225,11 +225,12 @@ def test_binary_operator_floordiv(self):
 
     def test_binary_operator_mod(self):
         # Positive
-        pdf = pd.DataFrame({"a": [3], "b": [2]})
+        pdf = pd.DataFrame({"a": [3], "b": [2], "c": [0]})
         psdf = ps.from_pandas(pdf)
 
         self.assert_eq(psdf["a"] % psdf["b"], pdf["a"] % pdf["b"])
         self.assert_eq(psdf["a"] % 0, pdf["a"] % 0)
+        self.assert_eq(1 % psdf["c"], 1 % pdf["c"])
 
         # Negative
         psdf = ps.DataFrame({"a": ["x"], "b": [1]})

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

@@ -235,16 +235,13 @@ def test_rpow(self):
 
     def test_rmod(self):
         psdf = self.psdf
+        pdf = self.pdf
 
         b_psser = psdf["bool"]
-        # 1 % False is 0.0 in pandas
-        self.assert_eq(pd.Series([0, 0, None], dtype=float, name="bool"), 1 % b_psser)
-        # 0.1 / True is 0.1 in pandas
-        self.assert_eq(
-            pd.Series([0.10000000000000009, 0.10000000000000009, None], dtype=float, name="bool"),
-            0.1 % b_psser,
-            check_exact=False,  # [0.1, 0.1, nan] for pandas-on-Spark
-        )
+        b_pser = pdf["bool"]
+        self.assert_eq(1 % b_pser.astype(float), 1 % b_psser)
+        # # Allow float precision diff: pandas:  0.10000000000000009; pandas on spark: 0.1
+        self.assert_eq(0.1 % b_pser, 0.1 % b_psser, almost=True)
         self.assertRaises(TypeError, lambda: datetime.date(1994, 1, 1) % b_psser)
         self.assertRaises(TypeError, lambda: True % b_psser)