使用Spark将列转置为行

Question

我正在尝试将表的某些列转置为行。我正在使用Python和Spark 1.5.0。这是我的初始表：

+-----+-----+-----+-------+
|  A  |col_1|col_2|col_...|
+-----+-------------------+
|  1  |  0.0|  0.6|  ...  |
|  2  |  0.6|  0.7|  ...  |
|  3  |  0.5|  0.9|  ...  |
|  ...|  ...|  ...|  ...  |

我想要这样的东西：

+-----+--------+-----------+
|  A  | col_id | col_value |
+-----+--------+-----------+
|  1  |   col_1|        0.0|
|  1  |   col_2|        0.6|   
|  ...|     ...|        ...|    
|  2  |   col_1|        0.6|
|  2  |   col_2|        0.7| 
|  ...|     ...|        ...|  
|  3  |   col_1|        0.5|
|  3  |   col_2|        0.9|
|  ...|     ...|        ...|

有人知道我能做到吗？感谢您的帮助。

Answer 1

使用基本的Spark SQL函数相对简单。

蟒蛇

from pyspark.sql.functions import array, col, explode, struct, lit

df = sc.parallelize([(1, 0.0, 0.6), (1, 0.6, 0.7)]).toDF(["A", "col_1", "col_2"])

def to_long(df, by):

    # Filter dtypes and split into column names and type description
    cols, dtypes = zip(*((c, t) for (c, t) in df.dtypes if c not in by))
    # Spark SQL supports only homogeneous columns
    assert len(set(dtypes)) == 1, "All columns have to be of the same type"

    # Create and explode an array of (column_name, column_value) structs
    kvs = explode(array([
      struct(lit(c).alias("key"), col(c).alias("val")) for c in cols
    ])).alias("kvs")

    return df.select(by + [kvs]).select(by + ["kvs.key", "kvs.val"])

to_long(df, ["A"])

Scala ：

import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions.{array, col, explode, lit, struct}

val df = Seq((1, 0.0, 0.6), (1, 0.6, 0.7)).toDF("A", "col_1", "col_2")

def toLong(df: DataFrame, by: Seq[String]): DataFrame = {
  val (cols, types) = df.dtypes.filter{ case (c, _) => !by.contains(c)}.unzip
  require(types.distinct.size == 1, s"${types.distinct.toString}.length != 1")

  val kvs = explode(array(
    cols.map(c => struct(lit(c).alias("key"), col(c).alias("val"))): _*
  ))

  val byExprs = by.map(col(_))

  df
    .select(byExprs :+ kvs.alias("_kvs"): _*)
    .select(byExprs ++ Seq($"_kvs.key", $"_kvs.val"): _*)
}

toLong(df, Seq("A"))