Spark dataframe 列内容修改答案

【问题标题】：Spark dataframe Column content modificationSpark dataframe 列内容修改
【发布时间】：2019-03-13 05:53:23
【问题描述】：

我有一个如下所示的数据框df.show():

+--------+---------+---------+---------+---------+
|  Col11 |  Col22  | Expend1 | Expend2 | Expend3 |
+--------+---------+---------+---------+---------+
| Value1 | value1  |     123 |    2264 |      56 |
| Value1 | value2  |     124 |    2255 |      23 |
+--------+---------+---------+---------+---------+

我可以使用一些 SQL 将上面的数据框转换为下面的数据框吗？

+--------+---------+-------------+---------------+------------+
| Col11  |  Col22  | Expend1     | Expend2       | Expend3    |
+--------+---------+-------------+---------------+------------+
| Value1 | value1  | Expend1:123 | Expend2: 2264 | Expend3:56 |
| Value1 | value2  | Expend1:124 | Expend2: 2255 | Expend3:23 |
+--------+---------+-------------+---------------+------------+

【问题讨论】：

标签： scala apache-spark apache-spark-sql

【解决方案1】：

这里可以使用foldLeft的思路

import spark.implicits._
import org.apache.spark.sql.functions._

val df = spark.sparkContext.parallelize(Seq(
  ("Value1", "value1", "123", "2264", "56"),
  ("Value1", "value2", "124", "2255", "23")
)).toDF("Col11", "Col22", "Expend1", "Expend2", "Expend3")

//Lists your columns for operation
val cols = List("Expend1", "Expend2", "Expend3")

val newDF = cols.foldLeft(df){(acc, name)  =>
  acc.withColumn(name, concat(lit(name + ":"), col(name)))
}

newDF.show()

输出：

+------+------+-----------+------------+----------+
| Col11| Col22|    Expend1|     Expend2|   Expend3|
+------+------+-----------+------------+----------+
|Value1|value1|Expend1:123|Expend2:2264|Expend3:56|
|Value1|value2|Expend1:124|Expend2:2255|Expend3:23|
+------+------+-----------+------------+----------+

【讨论】：

【解决方案2】：

你可以使用简单的 sql select 语句来做到这一点，如果你想也可以使用 udf

Ex -> select Col11 , Col22 , 'Expend1:' + cast(Expend1 as varchar(10)) as Expend1, .... from table

【讨论】：

【解决方案3】：

 val df = Seq(("Value1", "value1",    "123",   "2264",     "56"), ("Value1", "value2",    "124",   "2255",     "23") ).toDF("Col11", "Col22", "Expend1", "Expend2", "Expend3")
 val cols = df.columns.filter(!_.startsWith("Col"))   // It will only fetch other than col% prefix columns

 val getCombineData = udf { (colName:String, colvalue:String) => colName + ":"+ colvalue}
 var in = df 
 for (e <- cols) {
    in = in.withColumn(e, getCombineData(lit(e), col(e)) )
 } 
 in.show

 // results
 +------+------+-----------+------------+----------+
 | Col11| Col22|    Expend1|     Expend2|   Expend3|
 +------+------+-----------+------------+----------+
 |Value1|value1|Expend1:123|Expend2:2264|Expend3:56|
 |Value1|value2|Expend1:124|Expend2:2255|Expend3:23|
 +------+------+-----------+------------+----------+

【讨论】：