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[SPARK-7710][SPARK-7998][DOCS] Docs for DataFrameStatFunctions #8378

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[SPARK-7710][SPARK-7998][DOCS] Docs for DataFrameStatFunctions #8378

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2 changes: 1 addition & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -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)()
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101 changes: 101 additions & 0 deletions sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
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Expand Up @@ -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 = {
Expand All @@ -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 = {
Expand All @@ -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 = {
Expand All @@ -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 = {
Expand All @@ -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($"a-b_freqItems").as("freq_ab")).show()
* +----------+
* | freq_ab|
* +----------+
* | [1,-1.0]|
* | ... |
* +----------+
* }}}
*
* @since 1.4.0
*/
def freqItems(cols: Array[String], support: Double): DataFrame = {
Expand Down Expand Up @@ -147,6 +208,32 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
* @param cols the names of the columns to search frequent items in.
* @return A Local DataFrame with the Array of frequent items for each column.
*
* {{{
* val rows = Seq.tabulate(100) { i =>
* if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
* }
* val df = sqlContext.createDataFrame(rows).toDF("a", "b")
* // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
* // "a" and "b"
* val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
* freqSingles.show()
* +-----------+-------------+
* |a_freqItems| b_freqItems|
* +-----------+-------------+
* | [1, 99]|[-1.0, -99.0]|
* +-----------+-------------+
* // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
* val pairDf = df.select(struct("a", "b").as("a-b"))
* val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
* freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
* +----------+
* | freq_ab|
* +----------+
* | [1,-1.0]|
* | ... |
* +----------+
* }}}
*
* @since 1.4.0
*/
def freqItems(cols: Seq[String], support: Double): DataFrame = {
Expand Down Expand Up @@ -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 = {
Expand Down