[GitHub] [spark] xinrong-meng commented on a diff in pull request #37635: [SPARK-40131][PYTHON] Support NumPy ndarray in built-in functions

[GitBox <gi...@apache.org>]

xinrong-meng commented on code in PR #37635:
URL: https://github.com/apache/spark/pull/37635#discussion_r962400610


##########
python/pyspark/sql/types.py:
##########
@@ -2268,12 +2268,40 @@ def convert(self, obj: "np.generic", gateway_client: GatewayClient) -> Any:
         return obj.item()
 
 
+class NumpyArrayConverter:
+    def can_convert(self, obj: Any) -> bool:
+        return has_numpy and isinstance(obj, np.ndarray)
+
+    def convert(self, obj: "np.ndarray", gateway_client: GatewayClient) -> JavaObject:
+        from pyspark import SparkContext
+
+        gateway = SparkContext._gateway
+        assert gateway is not None
+
+        plist = obj.tolist()
+        # np.array([]).dtype is dtype('float64') so set float for empty plist
+        ptpe = type(plist[0]) if len(plist) > 0 else float
+        tpe_dict = {
+            int: gateway.jvm.int,

Review Comment:
   Makes sense!
   
   One limitation is `np.dtype("int8")` cann't be mapped to `gateway.jvm.byte`, create `jarr` accordingly and then do the per-element assignment.
   
   `TypeError: 'bytes' object does not support item assignment` is caused in `jarr[i] = plist[i]`.
   
   So both `int8` and `int16` are mapped to `gateway.jvm.short`.



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