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 <brk...@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 <brk...@gmail.com> Authored: Mon Aug 24 13:48:01 2015 -0700 Committer: Reynold Xin <r...@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 = { --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
