From a77bdb34f8e3993e19ef3b93ab29f155a39570e0 Mon Sep 17 00:00:00 2001 From: Andrew Ray Date: Mon, 7 Dec 2015 13:55:20 -0600 Subject: [PATCH] Make python api doc consistant with scala doc that was changed in api audit SPARK-11946 --- python/pyspark/sql/group.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py index 1911588309aff..9ca303a974cd4 100644 --- a/python/pyspark/sql/group.py +++ b/python/pyspark/sql/group.py @@ -169,16 +169,20 @@ def sum(self, *cols): @since(1.6) def pivot(self, pivot_col, values=None): - """Pivots a column of the current DataFrame and perform the specified aggregation. + """ + Pivots a column of the current [[DataFrame]] and perform the specified aggregation. + There are two versions of pivot function: one that requires the caller to specify the list + of distinct values to pivot on, and one that does not. The latter is more concise but less + efficient, because Spark needs to first compute the list of distinct values internally. - :param pivot_col: Column to pivot - :param values: Optional list of values of pivot column that will be translated to columns in - the output DataFrame. If values are not provided the method will do an immediate call - to .distinct() on the pivot column. + :param pivot_col: Name of the column to pivot. + :param values: List of values that will be translated to columns in the output DataFrame. + // 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)] """