Github user zasdfgbnm commented on a diff in the pull request:
https://github.com/apache/spark/pull/18444#discussion_r128127304
--- Diff: python/pyspark/sql/types.py ---
@@ -938,12 +1016,17 @@ def _infer_type(obj):
return MapType(_infer_type(key), _infer_type(value), True)
else:
return MapType(NullType(), NullType(), True)
- elif isinstance(obj, (list, array)):
+ elif isinstance(obj, list):
for v in obj:
if v is not None:
return ArrayType(_infer_type(obj[0]), True)
else:
return ArrayType(NullType(), True)
+ elif isinstance(obj, array):
+ if obj.typecode in _array_type_mappings:
+ return ArrayType(_array_type_mappings[obj.typecode](), False)
+ else:
+ raise TypeError("not supported type: array(%s)" % obj.typecode)
--- End diff --
Is there any reason to do so? I don't think array with unsupported typecode
will be correctly serialized or deserialized. In this case, it would be better
to raise an TypeError and let the user to pick another type.
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