[SPARK-11690][PYSPARK] Add pivot to python api

This PR adds pivot to the python api of GroupedData with the same syntax as Scala/Java.

Author: Andrew Ray <ray.andrew@gmail.com>

Closes #9653 from aray/sql-pivot-python.

1 files changed, 23 insertions, 1 deletions

diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py
index 71c0bccc5e..227f40bc3c 100644
--- a/python/pyspark/sql/group.py
+++ b/python/pyspark/sql/group.py
@@ -17,7 +17,7 @@
 
 from pyspark import since
 from pyspark.rdd import ignore_unicode_prefix
-from pyspark.sql.column import Column, _to_seq
+from pyspark.sql.column import Column, _to_seq, _to_java_column, _create_column_from_literal
 from pyspark.sql.dataframe import DataFrame
 from pyspark.sql.types import *
 
@@ -167,6 +167,23 @@ class GroupedData(object):
         [Row(sum(age)=7, sum(height)=165)]
         """
 
+    @since(1.6)
+    def pivot(self, pivot_col, *values):
+        """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()
+        [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))
+        return GroupedData(jgd, self.sql_ctx)
+
 
 def _test():
     import doctest
@@ -182,6 +199,11 @@ def _test():
                           StructField('name', StringType())]))
     globs['df3'] = sc.parallelize([Row(name='Alice', age=2, height=80),
                                    Row(name='Bob', age=5, height=85)]).toDF()
+    globs['df4'] = sc.parallelize([Row(course="dotNET", year=2012, earnings=10000),
+                                   Row(course="Java",   year=2012, earnings=20000),
+                                   Row(course="dotNET", year=2012, earnings=5000),
+                                   Row(course="dotNET", year=2013, earnings=48000),
+                                   Row(course="Java",   year=2013, earnings=30000)]).toDF()
 
     (failure_count, test_count) = doctest.testmod(
         pyspark.sql.group, globs=globs,