Updated rest of the files

Author: rxin

python/pyspark/sql/dataframe.py

@@ -1069,7 +1069,7 @@ def agg(self, *exprs):
 
         >>> from pyspark.sql import functions as F
         >>> gdf.agg(F.min(df.age)).collect()
-        [Row(MIN(age)=2), Row(MIN(age)=5)]
+        [Row(name=u'Alice', MIN(age)=2), Row(name=u'Bob', MIN(age)=5)]
         """
         assert exprs, "exprs should not be empty"
         if len(exprs) == 1 and isinstance(exprs[0], dict):

sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala

@@ -135,8 +135,9 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression])
   }
 
   /**
-   * Compute aggregates by specifying a series of aggregate columns. Unlike other methods in this
-   * class, the resulting [[DataFrame]] won't automatically include the grouping columns.
+   * Compute aggregates by specifying a series of aggregate columns. Note that this function by
+   * default retains the grouping columns in its output. To not retain grouping columns, set
+   * `spark.sql.retainGroupColumns` to false.
    *
    * The available aggregate methods are defined in [[org.apache.spark.sql.functions]].
    *

sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala

@@ -102,7 +102,7 @@ private[sql] object StatFunctions extends Logging {
   /** Generate a table of frequencies for the elements of two columns. */
   private[sql] def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame = {
     val tableName = s"${col1}_$col2"
-    val counts = df.groupBy(col1, col2).agg(col(col1), col(col2), count("*")).take(1e6.toInt)
+    val counts = df.groupBy(col1, col2).agg(count("*")).take(1e6.toInt)
     if (counts.length == 1e6.toInt) {
       logWarning("The maximum limit of 1e6 pairs have been collected, which may not be all of " +
         "the pairs. Please try reducing the amount of distinct items in your columns.")

sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala

@@ -62,7 +62,7 @@ class DataFrameSuite extends QueryTest {
     val df = Seq((1,(1,1))).toDF()
 
     checkAnswer(
-      df.groupBy("_1").agg(col("_1"), sum("_2._1")).toDF("key", "total"),
+      df.groupBy("_1").agg(sum("_2._1")).toDF("key", "total"),
       Row(1, 1) :: Nil)
   }
 
@@ -127,7 +127,7 @@ class DataFrameSuite extends QueryTest {
       df2
         .select('_1 as 'letter, 'number)
         .groupBy('letter)
-        .agg('letter, countDistinct('number)),
+        .agg(countDistinct('number)),
       Row("a", 3) :: Row("b", 2) :: Row("c", 1) :: Nil
     )
   }