sparkSQL上的转换类似于python Pandas的DataFrame

Question

如果我想在Spark DataFrame上进行一些转换，并且转换的执行方式应与熊猫的DataFrame.apply()类似，该怎么办。

import org.apache.spark.sql.SparkSession

object SparkSQLTransform {
  def main(args: Array[String]): Unit = {
    val sparkSession = SparkSession
      .builder()
      .master("local")
      .getOrCreate()
    val peopleDF = sparkSession.read.json("resources/people.json")
    peopleDF.show()
    /*
    +---+-------+------+
    |age|   name|   sex|
    +---+-------+------+
    | 25|Michael|female|
    | 30|   Andy|female|
    | 19| Justin|  male|
    | 39|  Alice|female|
    +---+-------+------+
     */

    //if there is some common operations act as the pandas.DataFrame.apply() method, which
    //can got the result below:
    /*
    +---+-------+------+
    |age|   name|   sex|
    +---+-------+------+
    | 25|Michael|0|
    | 30|   Andy|0|
    | 19| Justin| 1|
    | 39|  Alice|0|
    +---+-------+------+
     */
  }

}

the content of people.json :
{"name":"Michael", "age":25, "sex": "female"}
{"name":"Andy", "age":30, "sex":"female"}
{"name":"Justin", "age":19, "sex": "male"}
{"name":"Alice", "age": 39, "sex": "female"}