如何在 spark 中使用 Regexp_replace

假设有一个数据框 x 和列 x4x41,34351,6566-0,34435我希望输出为x41.34351.6566-0.34435我使用的代码是import org.apache.spark.sql.Columndef replace = regexp_replace((x.x4,1,6566:String,1.6566:String)x.x4)但我收到以下错误import org.apache.spark.sql.Column<控制台>:1: 错误: ')' 预期但 '.'成立.def 替换 = regexp_replace((train_df.x37,0,160430299:String,0.160430299:String)train_df.x37)对语法、逻辑或任何其他合适方式的任何帮助将不胜感激 解决方案 这是一个可重现的示例，假设 x4 是一个字符串列.import org.apache.spark.sql.functions.regexp_replaceval df = spark.createDataFrame(Seq((1, "1,3435"),(2, "1,6566"),(3, "-0,34435"))).toDF("Id", "x4")语法是regexp_replace(str,pattern,replacement)，转换为:df.withColumn("x4New", regexp_replace(df("x4"), "\\,", ".")).show+---+--------+--------+|编号|x4|x4新|+---+--------+--------+|1|1,3435|1.3435||2|1,6566|1.6566||3|-0,34435|-0.34435|+---+--------+--------+I am pretty new to spark and would like to perform an operation on a column of a dataframe so as to replace all the , in the column with .Assume there is a dataframe x and column x4x41,34351,6566-0,34435I want the output to be asx41.34351.6566-0.34435The code I am using is import org.apache.spark.sql.Columndef replace = regexp_replace((x.x4,1,6566:String,1.6566:String)x.x4)But I get the following errorimport org.apache.spark.sql.Column<console>:1: error: ')' expected but '.' found. def replace = regexp_replace((train_df.x37,0,160430299:String,0.160430299:String)train_df.x37)Any help on the syntax, logic or any other suitable way would be much appreciated 解决方案 Here's a reproducible example, assuming x4 is a string column.import org.apache.spark.sql.functions.regexp_replaceval df = spark.createDataFrame(Seq( (1, "1,3435"), (2, "1,6566"), (3, "-0,34435"))).toDF("Id", "x4")The syntax is regexp_replace(str, pattern, replacement), which translates to:df.withColumn("x4New", regexp_replace(df("x4"), "\\,", ".")).show+---+--------+--------+| Id| x4| x4New|+---+--------+--------+| 1| 1,3435| 1.3435|| 2| 1,6566| 1.6566|| 3|-0,34435|-0.34435|+---+--------+--------+ 这篇关于如何在 spark 中使用 Regexp_replace的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！