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[SPARK-23672][PYTHON] Document support for nested return types in scalar with arrow udfs #20908

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[SPARK-23672][PYTHON] Document support for nested return types in scalar with arrow udfs #20908

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da8dbaf
Demonstrate tokenize udf
holdenk Mar 26, 2018
342d222
Long lines are bad, kthnx
holdenk Mar 26, 2018
88b65c5
Update description to clarify non-supported types are only map/struct…
holdenk Mar 30, 2018
091a761
Fix long line
holdenk Apr 2, 2018
729abc9
Switch nested array test to a positive test
holdenk Apr 6, 2018
f5aeafc
Merge branch 'master' into SPARK-23672-document-support-for-nested-re…
holdenk Apr 6, 2018
7a42096
Merge branch 'master' into SPARK-23672-document-support-for-nested-re…
holdenk Aug 13, 2018
03798e0
Clarify we can support nested array types and add a test demonstratin…
holdenk Aug 13, 2018
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3 changes: 2 additions & 1 deletion python/pyspark/sql/functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2718,9 +2718,10 @@ def pandas_udf(f=None, returnType=None, functionType=None):
1. SCALAR

A scalar UDF defines a transformation: One or more `pandas.Series` -> A `pandas.Series`.
The returnType should be a primitive data type, e.g., :class:`DoubleType`.
The length of the returned `pandas.Series` must be of the same as the input `pandas.Series`.

:class:`MapType`, :class:`StructType` are currently not supported as output types.

Scalar UDFs are used with :meth:`pyspark.sql.DataFrame.withColumn` and
:meth:`pyspark.sql.DataFrame.select`.

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19 changes: 19 additions & 0 deletions python/pyspark/sql/tests.py
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Expand Up @@ -4358,6 +4358,7 @@ def test_timestamp_dst(self):
not _have_pandas or not _have_pyarrow,
_pandas_requirement_message or _pyarrow_requirement_message)
class PandasUDFTests(ReusedSQLTestCase):

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@HyukjinKwon HyukjinKwon Mar 27, 2018

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seems unrelated ..

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@holdenk holdenk Mar 28, 2018

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It is, however makes it fit with the style of the rest of the file.

def test_pandas_udf_basic(self):
from pyspark.rdd import PythonEvalType
from pyspark.sql.functions import pandas_udf, PandasUDFType
Expand Down Expand Up @@ -4573,6 +4574,24 @@ 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(' ')),
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@BryanCutler BryanCutler Mar 26, 2018

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instead of s.apply with a lambda, you could do lambda s: s.str.split(' '), both do the same just a little more compact

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@HyukjinKwon HyukjinKwon Mar 27, 2018

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hm. I thought this PR targets to clarify array type wuth primitive types. can we improve the test case here -https://github.com/holdenk/spark/blob/342d2228a5c68fd2c07bd8c1b518da6135ce1bf6/python/pyspark/sql/tests.py#L3998, and remove this test case?

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@icexelloss icexelloss Mar 28, 2018

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I prefer to have a test case for primitive array type as well.

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@BryanCutler BryanCutler Apr 3, 2018

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I think this is a pretty common use to tokenize, so I think it's fine to have an explicit test for this

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@HyukjinKwon HyukjinKwon Apr 3, 2018

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I dont think this PR targets to fix or support tokenizing in an udf ..

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@holdenk holdenk Apr 6, 2018

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@HyukjinKwon It doesn't, but given that the old documentation implied that the ionization usecase wouldn't work I thought it would be good to illustrate that it does in a test.

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(self):
from pyspark.sql.functions import pandas_udf
tokenize = pandas_udf(lambda s: s.apply(lambda str: [str.split(' ')]),
ArrayType(ArrayType(StringType())))
self.assertEqual(tokenize.returnType, ArrayType(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_vectorized_udf_basic(self):
from pyspark.sql.functions import pandas_udf, col, array
df = self.spark.range(10).select(
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