[SPARK-15973][PYSPARK] Fix GroupedData Documentation

*This contribution is my original work and that I license the work to the project under the project's open source license.*

## What changes were proposed in this pull request?

Documentation updates to PySpark's GroupedData

## How was this patch tested?

Manual Tests

Author: Josh Howes <josh.howes@gmail.com>
Author: Josh Howes <josh.howes@maxpoint.com>

Closes #13724 from josh-howes/bugfix/SPARK-15973.

python/pyspark/sql/group.py

@@ -27,7 +27,7 @@ __all__ = ["GroupedData"]
 def dfapi(f):
     def _api(self):
         name = f.__name__
-        jdf = getattr(self._jdf, name)()
+        jdf = getattr(self._jgd, name)()
         return DataFrame(jdf, self.sql_ctx)
     _api.__name__ = f.__name__
     _api.__doc__ = f.__doc__
@@ -35,9 +35,9 @@ def dfapi(f):
 
 
 def df_varargs_api(f):
-    def _api(self, *args):
+    def _api(self, *cols):
         name = f.__name__
-        jdf = getattr(self._jdf, name)(_to_seq(self.sql_ctx._sc, args))
+        jdf = getattr(self._jgd, name)(_to_seq(self.sql_ctx._sc, cols))
         return DataFrame(jdf, self.sql_ctx)
     _api.__name__ = f.__name__
     _api.__doc__ = f.__doc__
@@ -54,8 +54,8 @@ class GroupedData(object):
     .. versionadded:: 1.3
     """
 
-    def __init__(self, jdf, sql_ctx):
-        self._jdf = jdf
+    def __init__(self, jgd, sql_ctx):
+        self._jgd = jgd
         self.sql_ctx = sql_ctx
 
     @ignore_unicode_prefix
@@ -83,11 +83,11 @@ class GroupedData(object):
         """
         assert exprs, "exprs should not be empty"
         if len(exprs) == 1 and isinstance(exprs[0], dict):
-            jdf = self._jdf.agg(exprs[0])
+            jdf = self._jgd.agg(exprs[0])
         else:
             # Columns
             assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column"
-            jdf = self._jdf.agg(exprs[0]._jc,
+            jdf = self._jgd.agg(exprs[0]._jc,
                                 _to_seq(self.sql_ctx._sc, [c._jc for c in exprs[1:]]))
         return DataFrame(jdf, self.sql_ctx)
 
@@ -178,18 +178,18 @@ class GroupedData(object):
         :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
+        # 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)
+        # 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)]
         """
         if values is None:
-            jgd = self._jdf.pivot(pivot_col)
+            jgd = self._jgd.pivot(pivot_col)
         else:
-            jgd = self._jdf.pivot(pivot_col, values)
+            jgd = self._jgd.pivot(pivot_col, values)
         return GroupedData(jgd, self.sql_ctx)