[SPARK-58470][PS] Use native expressions for NumPy trunc

python/pyspark/pandas/numpy_compat.py

@@ -23,6 +23,7 @@
 from pyspark.sql.types import DoubleType, LongType, BooleanType
 from pyspark.pandas.base import IndexOpsMixin
 
+
 unary_np_spark_mappings = {
     "abs": F.abs,
     "absolute": F.abs,
@@ -78,7 +79,15 @@
     "square": lambda c: c.cast("double") * c,
     "tan": F.tan,
     "tanh": F.tanh,
-    "trunc": pandas_udf(lambda s: np.trunc(s), DoubleType()),  # type: ignore[call-overload]
+    "trunc": lambda c: F.when(
+        c.cast("double").isNull()
+        | F.isnan(c.cast("double"))
+        | c.cast("double").isin(float("-inf"), float("inf")),
+        c.cast("double"),
+    ).otherwise(
+        F.signum(c.cast("double"))
+        * (F.abs(c.cast("double")) - (F.abs(c.cast("double")) % F.lit(1.0)))
+    ),
 }
 
 binary_np_spark_mappings = {

python/pyspark/pandas/tests/test_numpy_compat.py

@@ -108,6 +108,24 @@ def test_np_math_functions(self):
             (np.sinh, [-np.inf, -64.0, -2.0, 0.0, 2.0, 64.0, np.inf, np.nan]),
             (np.square, [-np.inf, -64.0, -2.0, 0.0, 2.0, 64.0, np.inf, np.nan]),
             (np.tanh, [-np.inf, -64.0, -2.0, 0.0, 2.0, 64.0, np.inf, np.nan]),
+            (
+                np.trunc,
+                [
+                    -np.inf,
+                    -64.0,
+                    -2.0,
+                    -1.5,
+                    -0.5,
+                    -0.0,
+                    0.0,
+                    0.5,
+                    1.5,
+                    2.0,
+                    64.0,
+                    np.inf,
+                    np.nan,
+                ],
+            ),
         ):
             with self.subTest(name=np_func.__name__, values=values):
                 pdf = pd.DataFrame({"a": values})