BryanCutler commented on a change in pull request #23900: [SPARK-23836][PYTHON]
Add support for StructType return in Scalar Pandas UDF
URL: https://github.com/apache/spark/pull/23900#discussion_r261303303
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File path: python/pyspark/serializers.py
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@@ -295,7 +297,33 @@ def create_array(s, t):
raise RuntimeError(error_msg % (s.dtype, t), e)
return array
- arrs = [create_array(s, t) for s, t in series]
+ arrs = []
+ for s, t in series:
+ if t is not None and pa.types.is_struct(t):
+ if not isinstance(s, pd.DataFrame):
+ raise ValueError("A field of type StructType expects a
pandas.DataFrame, "
+ "but got: %s" % str(type(s)))
+
+ # Assign result columns by schema name if user labeled with
strings, else use position
+ struct_arrs = []
+ struct_names = []
+ if assign_cols_by_name and any(isinstance(name, basestring) for
name in s.columns):
Review comment:
Yeah, this is copied from grouped map wrap. It didn't seem necessary to
check all columns to be string. The only case that ends up weird is if the
columns have a mix of strings and other types. I think that would be a little
strange and I'm not sure that assigning by position is the right thing to do.
So this would probably end up with raising a `KeyError` so that the user can
fix it.
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