Yikun commented on code in PR #37437:
URL: https://github.com/apache/spark/pull/37437#discussion_r941297621
##########
python/pyspark/sql/group.py:
##########
@@ -261,17 +413,69 @@ def pivot(self, pivot_col: str, values:
Optional[List["LiteralType"]] = None) ->
Examples
--------
- # Compute the sum of earnings for each year by course with each course
as a separate column
-
- >>> df4.groupBy("year").pivot("course", ["dotNET",
"Java"]).sum("earnings").collect()
- [Row(year=2012, dotNET=15000, Java=20000), Row(year=2013,
dotNET=48000, Java=30000)]
-
- # Or without specifying column values (less efficient)
-
- >>> df4.groupBy("year").pivot("course").sum("earnings").collect()
- [Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000,
dotNET=48000)]
- >>>
df5.groupBy("sales.year").pivot("sales.course").sum("sales.earnings").collect()
- [Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000,
dotNET=48000)]
+ >>> from pyspark.sql import Row
+ >>> spark = SparkSession.builder.master("local[4]").appName("sql.group
tests").getOrCreate()
Review Comment:
Sorry to post review, just curious why not use the `spark` directly in here?
I think this example is a little bit different with others.
For all PRs in this series, I think `sc` (Spark context) and `spark`
(SparkSession) can define in the bottom, and use it directly in every doctest
(just like pyspark shell, sc and spark already available), right?
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