From f3152722791b163fa66597b3684009058195ba33 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 24 Nov 2015 12:54:37 -0800 Subject: [SPARK-11946][SQL] Audit pivot API for 1.6. Currently pivot's signature looks like ```scala scala.annotation.varargs def pivot(pivotColumn: Column, values: Column*): GroupedData scala.annotation.varargs def pivot(pivotColumn: String, values: Any*): GroupedData ``` I think we can remove the one that takes "Column" types, since callers should always be passing in literals. It'd also be more clear if the values are not varargs, but rather Seq or java.util.List. I also made similar changes for Python. Author: Reynold Xin Closes #9929 from rxin/SPARK-11946. --- python/pyspark/sql/group.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) (limited to 'python/pyspark/sql/group.py') diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py index 227f40bc3c..d8ed7eb2dd 100644 --- a/python/pyspark/sql/group.py +++ b/python/pyspark/sql/group.py @@ -168,20 +168,24 @@ class GroupedData(object): """ @since(1.6) - def pivot(self, pivot_col, *values): + def pivot(self, pivot_col, values=None): """Pivots a column of the current DataFrame and preform the specified aggregation. :param pivot_col: Column to pivot :param values: Optional list of values of pivotColumn that will be translated to columns in the output data frame. If values are not provided the method with do an immediate call to .distinct() on the pivot column. - >>> df4.groupBy("year").pivot("course", "dotNET", "Java").sum("earnings").collect() + + >>> df4.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").collect() [Row(year=2012, dotNET=15000, Java=20000), Row(year=2013, dotNET=48000, Java=30000)] + >>> df4.groupBy("year").pivot("course").sum("earnings").collect() [Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)] """ - jgd = self._jdf.pivot(_to_java_column(pivot_col), - _to_seq(self.sql_ctx._sc, values, _create_column_from_literal)) + if values is None: + jgd = self._jdf.pivot(pivot_col) + else: + jgd = self._jdf.pivot(pivot_col, values) return GroupedData(jgd, self.sql_ctx) -- cgit v1.2.3