Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/20908#discussion_r178904674
--- Diff: python/pyspark/sql/tests.py ---
@@ -3966,6 +3967,24 @@ def random_udf(v):
random_udf = random_udf.asNondeterministic()
return random_udf
+ def test_pandas_udf_tokenize(self):
+ from pyspark.sql.functions import pandas_udf
+ tokenize = pandas_udf(lambda s: s.apply(lambda str: str.split('
')),
+ ArrayType(StringType()))
+ self.assertEqual(tokenize.returnType, ArrayType(StringType()))
+ df = self.spark.createDataFrame([("hi boo",), ("bye boo",)],
["vals"])
+ result = df.select(tokenize("vals").alias("hi"))
+ self.assertEqual([Row(hi=[u'hi', u'boo']), Row(hi=[u'bye',
u'boo'])], result.collect())
+
+ def test_pandas_udf_nested_arrays_does_not_work(self):
--- End diff --
Sorry @holdenk , I should have been more clear about ArrayType support.
Nested Arrays actually do work ok, it's primarily use with timestamps/dates
that need to be adjusted, and lack of actual testing to verify it. So it was
easiest to just say nested Arrays are unsupported, but I'll update SPARK-21187
to reflect this.
I ran the test below and it does work, you just need to define `df` from
above (also `ArrowTypeError` isn't defined, but should just be `Exception` and
`assertRaises` is expecting a callable where `result.collect()` is)
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