diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index 42e5cbc05e1e0..ba90888226194 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.sql.execution.stat.FrequentItems
+import org.apache.spark.sql.execution.stat.{ContingencyTable, FrequentItems}
 
 /**
  * :: Experimental ::
@@ -27,6 +27,20 @@ import org.apache.spark.sql.execution.stat.FrequentItems
 @Experimental
 final class DataFrameStatFunctions private[sql](df: DataFrame) {
 
+  /**
+   * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+   * The number of distinct values for each column should be less than Int.MaxValue. The first
+   * column of each row will be the distinct values of `col1` and the column names will be the
+   * distinct values of `col2` sorted in lexicographical order. Counts will be returned as `Long`s.
+   *
+   * @param col1 The name of the first column.
+   * @param col2 The name of the second column.
+   * @return A Local DataFrame containing the table
+   */
+  def crosstab(col1: String, col2: String): DataFrame = {
+    ContingencyTable.crossTabulate(df, col1, col2)
+  }
+
   /**
    * Finding frequent items for columns, possibly with false positives. Using the
    * frequent element count algorithm described in
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/ContingencyTable.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/ContingencyTable.scala
new file mode 100644
index 0000000000000..916df88fec957
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/ContingencyTable.scala
@@ -0,0 +1,38 @@
+package org.apache.spark.sql.execution.stat
+
+import org.apache.spark.sql.{Row, DataFrame}
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.functions._
+
+
+private[sql] object ContingencyTable {
+
+  /** 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 distinctVals = df.select(countDistinct(col1), countDistinct(col2)).collect().head
+    val distinctCol1 = distinctVals.getLong(0)
+    val distinctCol2 = distinctVals.getLong(1)
+
+    require(distinctCol1 < Int.MaxValue, s"The number of distinct values for $col1, can't " +
+      s"exceed Int.MaxValue. Currently $distinctCol1")
+    require(distinctCol2 < Int.MaxValue, s"The number of distinct values for $col2, can't " +
+      s"exceed Int.MaxValue. Currently $distinctCol2")
+    // Aggregate the counts for the two columns
+    val allCounts =
+      df.groupBy(col1, col2).agg(col(col1), col(col2), count("*")).orderBy(col1, col2).collect()
+    // Pivot the table
+    val pivotedTable = allCounts.grouped(distinctCol2.toInt).toArray
+    // Get the column names (distinct values of col2)
+    val headerNames = pivotedTable.head.map(r => StructField(r.get(1).toString, LongType))
+    val schema = StructType(StructField(tableName, StringType) +: headerNames)
+    val table = pivotedTable.map { rows =>
+      // the value of col1 is the first value, the rest are the counts
+      val rowValues = rows.head.get(0).toString +: rows.map(_.getLong(2))
+      Row(rowValues:_*)
+    }
+    new DataFrame(df.sqlContext, LocalRelation(schema.toAttributes, table))
+  }
+
+}
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index ebe96e649d940..3896ef96ffb40 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -178,6 +178,24 @@ public void testCreateDataFrameFromJavaBeans() {
       Assert.assertEquals(bean.getD().get(i), d.apply(i));
     }
   }
+
+  @Test
+  public void testCrosstab() {
+    DataFrame df = context.table("testData2");
+    DataFrame crosstab = df.stat().crosstab("a", "b");
+    String[] columnNames = crosstab.schema().fieldNames();
+    Assert.assertEquals(columnNames[0], "a_b");
+    Assert.assertEquals(columnNames[1], "1");
+    Assert.assertEquals(columnNames[2], "2");
+    Row[] rows = crosstab.collect();
+    Integer count = 1;
+    for (Row row : rows) {
+      Assert.assertEquals(row.get(0).toString(), count.toString());
+      Assert.assertEquals(row.getLong(1), 1L);
+      Assert.assertEquals(row.getLong(2), 1L);
+      count++;
+    }
+  }
   
   @Test
   public void testFrequentItems() {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index bb1d29c71d23b..c0afef2b7fe11 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -24,9 +24,26 @@ import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.test.TestSQLContext.implicits._
 
 class DataFrameStatSuite extends FunSuite  {
+  import TestData._
 
   val sqlCtx = TestSQLContext
 
+  test("crosstab") {
+    val crosstab = testData2.stat.crosstab("a", "b")
+    val columnNames = crosstab.schema.fieldNames
+    assert(columnNames(0) === "a_b")
+    assert(columnNames(1) === "1")
+    assert(columnNames(2) === "2")
+    val rows: Array[Row] = crosstab.collect()
+    var count: Integer = 1
+    rows.foreach { row =>
+      assert(row.get(0).toString === count.toString)
+      assert(row.getLong(1) === 1L)
+      assert(row.getLong(2) === 1L)
+      count += 1
+    }
+  }
+
   test("Frequent Items") {
     def toLetter(i: Int): String = (i + 96).toChar.toString
     val rows = Array.tabulate(1000) { i =>