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[SPARK-23334][SQL][PYTHON] Fix pandas_udf with return type StringType() to handle str type properly in Python 2. #20507

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ueshin wants to merge 3 commits into from
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[SPARK-23334][SQL][PYTHON] Fix pandas_udf with return type StringType() to handle str type properly in Python 2. #20507

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Fix pandas_udf with return type StringType() to handle str type prope…
ueshin Feb 5, 2018
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Address comments.
ueshin Feb 5, 2018
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ueshin Feb 6, 2018
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3 changes: 3 additions & 0 deletions python/pyspark/serializers.py
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Expand Up @@ -230,6 +230,9 @@ def create_array(s, t):
s = _check_series_convert_timestamps_internal(s.fillna(0), timezone)
# TODO: need cast after Arrow conversion, ns values cause error with pandas 0.19.2
return pa.Array.from_pandas(s, mask=mask).cast(t, safe=False)
elif t is not None and pa.types.is_string(t) and sys.version < '3':
# TODO: need decode before converting to Arrow in Python 2
return pa.Array.from_pandas(s.str.decode('utf-8'), mask=mask, type=t)
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@HyukjinKwon HyukjinKwon Feb 5, 2018 •
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@ueshin, actually, how about s.apply(lambda v: v.decode("utf-8") if isinstance(v, str) else v) to allow non-ascii encodable unicodes too like u"아"? I was worried of performance but I ran a simple perf test vs s.str.decode('utf-8') for sure. Seems actually fine.

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@ueshin ueshin Feb 6, 2018

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Good catch! I'll take it. Thanks!

return pa.Array.from_pandas(s, mask=mask, type=t)

arrs = [create_array(s, t) for s, t in series]
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8 changes: 8 additions & 0 deletions python/pyspark/sql/tests.py
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Expand Up @@ -3920,6 +3920,14 @@ def test_vectorized_udf_null_string(self):
res = df.select(str_f(col('str')))
self.assertEquals(df.collect(), res.collect())

def test_vectorized_udf_string_in_udf(self):
from pyspark.sql.functions import pandas_udf, col
import pandas as pd
df = self.spark.range(10)
str_f = pandas_udf(lambda x: pd.Series(["%s" % i for i in x]), StringType())
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@HyukjinKwon HyukjinKwon Feb 5, 2018

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Not a big deal. How about pd.Series(map(str, x))?

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@ueshin ueshin Feb 5, 2018

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Sounds good. I'll take it.

res = df.select(str_f(col('id')))
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@HyukjinKwon HyukjinKwon Feb 5, 2018

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How about variable names 'expected' and 'actual'?

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@ueshin ueshin Feb 5, 2018

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Sure, I'll update it.

self.assertEquals(df.select(col('id').cast('string')).collect(), res.collect())

def test_vectorized_udf_datatype_string(self):
from pyspark.sql.functions import pandas_udf, col
df = self.spark.range(10).select(
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