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[SPARK-29126][PYSPARK][DOC] Pandas Cogroup udf usage guide #26110

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[SPARK-29126][PYSPARK][DOC] Pandas Cogroup udf usage guide #26110

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695e0ec
updated user guide ofr cogroup pandas udf
d80tb7 Sep 29, 2019
0ecba8a
Merge branch 'master' of https://github.com/apache/spark into SPARK-2…
d80tb7 Oct 14, 2019
1802cbd
formatting fixes
d80tb7 Oct 14, 2019
da4f00b
stop sphinx thinking we need to substitute
d80tb7 Oct 14, 2019
81713b7
Merge branch 'master' of https://github.com/apache/spark into SPARK-2…
d80tb7 Oct 27, 2019
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code review comments
d80tb7 Oct 30, 2019
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formatting fixes
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@d80tb7
d80tb7 committed Oct 14, 2019
commit 1802cbd98cab2724952a3f649945fc284f6e6331
9 changes: 5 additions & 4 deletions examples/src/main/python/sql/arrow.py
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Expand Up @@ -257,19 +257,20 @@ def filter_func(batch_iter):
# +---+---+
# $example off:map_iter_pandas_udf$


def cogrouped_map_pandas_udf_example(spark):
# $example on:cogrouped_map_pandas_udf$
import pandas as pd

from pyspark.sql.functions import pandas_udf, PandasUDFType

df1 = spark.createDataFrame(
[(20000101, 1, 1.0), (20000101, 2, 2.0), (20000102, 1, 3.0), (20000102, 2, 4.0)],
("time", "id", "v1"))
[(20000101, 1, 1.0), (20000101, 2, 2.0), (20000102, 1, 3.0), (20000102, 2, 4.0)],
("time", "id", "v1"))

df2 = spark.createDataFrame(
[(20000101, 1, "x"), (20000101, 2, "y")],
("time", "id", "v2"))
[(20000101, 1, "x"), (20000101, 2, "y")],
("time", "id", "v2"))

@pandas_udf("time int, id int, v1 double, v2 string", PandasUDFType.COGROUPED_MAP)
def asof_join(l, r):
Expand Down