spark 将DataFrame所有的列类型改为double

我的原创地址：https://dongkelun.com/2018/04/27/dfChangeAllColDatatypes/

前言

由于spark机器学习要求输入的DataFrame类型为数值类型，所以如果原始数据读进来的列为string类型，需要一一转化，而如果列很多的情况下一个转化很麻烦，所以能不能一个循环或者一个函数去解决呢。

1、单列转化方法

import org.apache.spark.sql.types._ val data = Array(("1", "2", "3", "4", "5"), ("6", "7", "8", "9", "10")) val df = spark.createDataFrame(data).toDF("col1", "col2", "col3", "col4", "col5") import org.apache.spark.sql.functions._ df.select(col("col1").cast(DoubleType)).show()

+----+ |col1| +----+ | 1.0| | 6.0| +----+

2、循环转变

然后就想能不能用这个方法循环把每一列转成double，但没想到怎么实现，可以用withColumn循环实现。

val colNames = df.columns var df1 = df for (colName <- colNames) { df1 = df1.withColumn(colName, col(colName).cast(DoubleType)) } df1.show()

+----+----+----+----+----+ |col1|col2|col3|col4|col5| +----+----+----+----+----+ | 1.0| 2.0| 3.0| 4.0| 5.0| | 6.0| 7.0| 8.0| 9.0|10.0| +----+----+----+----+----+

3、通过:_*

但是上面这个方法效率比较低，然后问了一下别人，发现scala 有array:_*这样传参这种语法，而df的select方法也支持这样传，于是最终可以按下面的这样写

val cols = colNames.map(f => col(f).cast(DoubleType)) df.select(cols: _*).show()

+----+----+----+----+----+ |col1|col2|col3|col4|col5| +----+----+----+----+----+ | 1.0| 2.0| 3.0| 4.0| 5.0| | 6.0| 7.0| 8.0| 9.0|10.0| +----+----+----+----+----+

这样就可以很方便的查询指定多列和转变指定列的类型了：

val name = "col1,col3,col5" df.select(name.split(",").map(name => col(name)): _*).show() df.select(name.split(",").map(name => col(name).cast(DoubleType)): _*).show()

+----+----+----+ |col1|col3|col5| +----+----+----+ | 1| 3| 5| | 6| 8| 10| +----+----+----+ +----+----+----+ |col1|col3|col5| +----+----+----+ | 1.0| 3.0| 5.0| | 6.0| 8.0|10.0| +----+----+----+

附完整代码：

package com.dkl.leanring.spark.test import org.apache.spark.sql.SparkSession import org.apache.spark.sql.types._ import org.apache.spark.sql.DataFrame object DfDemo { def main(args: Array[String]): Unit = { val spark = SparkSession.builder().appName("DfDemo").master("local").getOrCreate() import org.apache.spark.sql.types._ val data = Array(("1", "2", "3", "4", "5"), ("6", "7", "8", "9", "10")) val df = spark.createDataFrame(data).toDF("col1", "col2", "col3", "col4", "col5") import org.apache.spark.sql.functions._ df.select(col("col1").cast(DoubleType)).show() val colNames = df.columns var df1 = df for (colName <- colNames) { df1 = df1.withColumn(colName, col(colName).cast(DoubleType)) } df1.show() val cols = colNames.map(f => col(f).cast(DoubleType)) df.select(cols: _*).show() val name = "col1,col3,col5" df.select(name.split(",").map(name => col(name)): _*).show() df.select(name.split(",").map(name => col(name).cast(DoubleType)): _*).show() }