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From r...@apache.org
Subject spark git commit: [SPARK-7710] [SPARK-7998] [DOCS] Docs for DataFrameStatFunctions
Date Mon, 24 Aug 2015 20:48:05 GMT
Repository: spark
Updated Branches:
  refs/heads/master 7478c8b66 -> 9ce0c7ad3


[SPARK-7710] [SPARK-7998] [DOCS] Docs for DataFrameStatFunctions

This PR contains examples on how to use some of the Stat Functions available for DataFrames
under `df.stat`.

rxin

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #8378 from brkyvz/update-sql-docs.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/9ce0c7ad
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/9ce0c7ad
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/9ce0c7ad

Branch: refs/heads/master
Commit: 9ce0c7ad333f4a3c01207e5e9ed42bcafb99d894
Parents: 7478c8b
Author: Burak Yavuz <brkyvz@gmail.com>
Authored: Mon Aug 24 13:48:01 2015 -0700
Committer: Reynold Xin <rxin@databricks.com>
Committed: Mon Aug 24 13:48:01 2015 -0700

----------------------------------------------------------------------
 .../scala/org/apache/spark/sql/DataFrame.scala  |   2 +-
 .../spark/sql/DataFrameStatFunctions.scala      | 101 +++++++++++++++++++
 2 files changed, 102 insertions(+), 1 deletion(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/9ce0c7ad/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
----------------------------------------------------------------------
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index d6688b2..791c10c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -684,7 +684,7 @@ class DataFrame private[sql](
       // make it a NamedExpression.
       case Column(u: UnresolvedAttribute) => UnresolvedAlias(u)
       case Column(expr: NamedExpression) => expr
-      // Leave an unaliased explode with an empty list of names since the analzyer will generate
the
+      // Leave an unaliased explode with an empty list of names since the analyzer will generate
the
       // correct defaults after the nested expression's type has been resolved.
       case Column(explode: Explode) => MultiAlias(explode, Nil)
       case Column(expr: Expression) => Alias(expr, expr.prettyString)()

http://git-wip-us.apache.org/repos/asf/spark/blob/9ce0c7ad/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
----------------------------------------------------------------------
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 2e68e35..69c9847 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
@@ -39,6 +39,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the second column
    * @return the covariance of the two columns.
    *
+   * {{{
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.cov("rand1", "rand2")
+   *    res1: Double = 0.065...
+   * }}}
+   *
    * @since 1.4.0
    */
   def cov(col1: String, col2: String): Double = {
@@ -54,6 +61,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the column to calculate the correlation against
    * @return The Pearson Correlation Coefficient as a Double.
    *
+   * {{{
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.corr("rand1", "rand2")
+   *    res1: Double = 0.613...
+   * }}}
+   *
    * @since 1.4.0
    */
   def corr(col1: String, col2: String, method: String): Double = {
@@ -69,6 +83,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the column to calculate the correlation against
    * @return The Pearson Correlation Coefficient as a Double.
    *
+   * {{{
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.corr("rand1", "rand2", "pearson")
+   *    res1: Double = 0.613...
+   * }}}
+   *
    * @since 1.4.0
    */
   def corr(col1: String, col2: String): Double = {
@@ -92,6 +113,20 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    *             of the DataFrame.
    * @return A DataFrame containing for the contingency table.
    *
+   * {{{
+   *    val df = sqlContext.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3,
2),
+   *      (3, 3))).toDF("key", "value")
+   *    val ct = df.stat.crosstab("key", "value")
+   *    ct.show()
+   *    +---------+---+---+---+
+   *    |key_value|  1|  2|  3|
+   *    +---------+---+---+---+
+   *    |        2|  2|  0|  1|
+   *    |        1|  1|  1|  0|
+   *    |        3|  0|  1|  1|
+   *    +---------+---+---+---+
+   * }}}
+   *
    * @since 1.4.0
    */
   def crosstab(col1: String, col2: String): DataFrame = {
@@ -112,6 +147,32 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    *                than 1e-4.
    * @return A Local DataFrame with the Array of frequent items for each column.
    *
+   * {{{
+   *    val rows = Seq.tabulate(100) { i =>
+   *      if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+   *    }
+   *    val df = sqlContext.createDataFrame(rows).toDF("a", "b")
+   *    // find the items with a frequency greater than 0.4 (observed 40% of the time) for
columns
+   *    // "a" and "b"
+   *    val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+   *    freqSingles.show()
+   *    +-----------+-------------+
+   *    |a_freqItems|  b_freqItems|
+   *    +-----------+-------------+
+   *    |    [1, 99]|[-1.0, -99.0]|
+   *    +-----------+-------------+
+   *    // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+   *    val pairDf = df.select(struct("a", "b").as("a-b"))
+   *    val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+   *    freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+   *    +----------+
+   *    |   freq_ab|
+   *    +----------+
+   *    |  [1,-1.0]|
+   *    |   ...    |
+   *    +----------+
+   * }}}
+   *
    * @since 1.4.0
    */
   def freqItems(cols: Array[String], support: Double): DataFrame = {
@@ -147,6 +208,32 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param cols the names of the columns to search frequent items in.
    * @return A Local DataFrame with the Array of frequent items for each column.
    *
+   * {{{
+   *    val rows = Seq.tabulate(100) { i =>
+   *      if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+   *    }
+   *    val df = sqlContext.createDataFrame(rows).toDF("a", "b")
+   *    // find the items with a frequency greater than 0.4 (observed 40% of the time) for
columns
+   *    // "a" and "b"
+   *    val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+   *    freqSingles.show()
+   *    +-----------+-------------+
+   *    |a_freqItems|  b_freqItems|
+   *    +-----------+-------------+
+   *    |    [1, 99]|[-1.0, -99.0]|
+   *    +-----------+-------------+
+   *    // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+   *    val pairDf = df.select(struct("a", "b").as("a-b"))
+   *    val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+   *    freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+   *    +----------+
+   *    |   freq_ab|
+   *    +----------+
+   *    |  [1,-1.0]|
+   *    |   ...    |
+   *    +----------+
+   * }}}
+   *
    * @since 1.4.0
    */
   def freqItems(cols: Seq[String], support: Double): DataFrame = {
@@ -180,6 +267,20 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @tparam T stratum type
    * @return a new [[DataFrame]] that represents the stratified sample
    *
+   * {{{
+   *    val df = sqlContext.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3,
2),
+   *      (3, 3))).toDF("key", "value")
+   *    val fractions = Map(1 -> 1.0, 3 -> 0.5)
+   *    df.stat.sampleBy("key", fractions, 36L).show()
+   *    +---+-----+
+   *    |key|value|
+   *    +---+-----+
+   *    |  1|    1|
+   *    |  1|    2|
+   *    |  3|    2|
+   *    +---+-----+
+   * }}}
+   *
    * @since 1.5.0
    */
   def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {


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