Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/18732#discussion_r141892887
--- Diff: python/pyspark/sql/group.py ---
@@ -194,6 +194,28 @@ def pivot(self, pivot_col, values=None):
jgd = self._jgd.pivot(pivot_col, values)
return GroupedData(jgd, self.sql_ctx)
+ def apply(self, udf_obj):
--- End diff --
I'm basically concerned that there is no distinct difference between the
current pandas udf and the new one for `apply`. But seems we can distinguish
them by looking at the return type? If so, we may no need of `pandas_df_udf`.
But we should update the doc of `pandas_udf` for this kind of (`apply`)
pandas udf.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
