line_arr = line.split()
时间: 2023-05-11 21:05:50 浏览: 55
这是一个字符串操作,将字符串按照空格分割成一个列表。具体实现可以使用 Python 的 split() 方法,例如:
line = "hello world"
line_arr = line.split()
print(line_arr) # ['hello', 'world']
相关问题
package org.zkpk.lab import org.apache.spark.SparkContext import org.apache.spark.SparkConf import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext object sale_drug { case class Sale(cardId:String,productId:Int,productName:String ,quantity:Int,money:Double) def main(args: Array[String]): Unit = { val conf = new SparkConf().setAppName("Pharmacy Sales Analysis").setMaster("local") val sc = new SparkContext(conf) sc.setLogLevel("ERROR") val sqlContext = new SQLContext(sc) // load data val data = sc.textFile("/home/zkpk/saledata.txt") // parse data val salesRDD: RDD[Sale] = data.map(line => line.split(" ")).map(arr => Sale(arr(0).toString, arr(1).toInt, arr(2).toString, arr(3).toInt, arr(4).toDouble)) import sqlContext.implicits._ val df = salesRDD.toDF() val nameMoneyDF = df.select("productName","money") nameMoneyDF.show()报错:java.lang.ArrayIndexOutOfBoundsException: 1
根据你提供的代码和错误信息,可以看出问题出在以下这个行代码:
```
val salesRDD: RDD[Sale] = data.map(line => line.split(" ")).map(arr => Sale(arr(0).toString, arr(1).toInt, arr(2).toString, arr(3).toInt, arr(4).toDouble))
```
这个错误是由于你的 `line.split(" ")` 返回的数组长度不够,导致在构造 `Sale` 对象时访问了一个不存在的数组下标。你需要检查你的数据文件,确保数据格式正确,每行数据都包含了五个字段。你可以在代码中添加一些容错机制来处理这种情况,例如使用 `getOrElse` 方法来访问数组元素,或者使用 `try-catch` 来捕获异常。
import org.apache.spark.SparkContext import org.apache.spark.SparkConf import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.functions._ class Sale { } object Sale { case class Sale(cardId:String,productId:Int,quantity:Int,money:Double) def main(args: Array[String]): Unit = { val conf = new SparkConf().setAppName("Pharmacy Sales Analysis").setMaster("local") val sc = new SparkContext(conf) sc.setLogLevel("ERROR") val sqlContext = new SQLContext(sc) // load data val data = sc.textFile(args(0)) // parse data val salesRDD: RDD[Sale] = data.map(line => line.split(" ")).map(arr => Sale(arr(0), arr(1).toInt, arr(2).toInt, arr(3).toDouble)) import sqlContext.implicits._ val df = salesRDD.toDF() val nameMoneyDF = df.select("productId","money","quantity") nameMoneyDF.show(5) val totalSales = nameMoneyDF.groupBy("productId").sum("quantity") //totalSales.show(5) val topSales = totalSales.select(totalSales("productId"),totalSales("sum(quantity)").as("Totalquantity")) .orderBy(desc("Totalquantity")) topSales.show(5) val totalbuy = df.groupBy("cardId").sum("quantity") //totalbuy.show(5) val topbuy = totalbuy.select(totalbuy("cardId"),totalbuy("sum(quantity)").as("Totalquantity")) .orderBy(desc("Totalquantity")) topbuy.show(5) val Oneproduct:Dataset[Row]= topSales.select("productId","Totalquantity").filter(row=>row.getAs[String]("productId") == 230010) Oneproduct.show() sc.stop() }}帮我写个注释
```scala
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
class Sale { }
object Sale {
case class Sale(cardId:String,productId:Int,quantity:Int,money:Double)
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("Pharmacy Sales Analysis").setMaster("local")
val sc = new SparkContext(conf)
sc.setLogLevel("ERROR")
val sqlContext = new SQLContext(sc)
// load data
val data = sc.textFile(args(0))
// parse data
val salesRDD: RDD[Sale] = data.map(line => line.split(" ")).map(arr => Sale(arr(0), arr(1).toInt, arr(2).toInt, arr(3).toDouble))
import sqlContext.implicits._
val df = salesRDD.toDF()
// select columns and show top 5 rows
val nameMoneyDF = df.select("productId","money","quantity")
nameMoneyDF.show(5)
// group by productId and sum the quantity, then show top 5 products with highest sales
val totalSales = nameMoneyDF.groupBy("productId").sum("quantity")
val topSales = totalSales.select(totalSales("productId"),totalSales("sum(quantity)").as("Totalquantity")).orderBy(desc("Totalquantity"))
topSales.show(5)
// group by cardId and sum the quantity, then show top 5 customers with highest purchase quantity
val totalbuy = df.groupBy("cardId").sum("quantity")
val topbuy = totalbuy.select(totalbuy("cardId"),totalbuy("sum(quantity)").as("Totalquantity")).orderBy(desc("Totalquantity"))
topbuy.show(5)
// select rows for one specific product (productId = 230010) and show the result
val Oneproduct:Dataset[Row]= topSales.select("productId","Totalquantity").filter(row=>row.getAs[String]("productId") == 230010)
Oneproduct.show()
sc.stop()
}
}
```
注释如下:
```scala
// 导入Spark相关的库
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
// 定义Sale类
class Sale { }
// Sale伴生对象
object Sale {
// 定义Sale类的属性
case class Sale(cardId:String,productId:Int,quantity:Int,money:Double)
def main(args: Array[String]): Unit = {
// 设置Spark的配置信息,并创建SparkContext
val conf = new SparkConf().setAppName("Pharmacy Sales Analysis").setMaster("local")
val sc = new SparkContext(conf)
sc.setLogLevel("ERROR")
val sqlContext = new SQLContext(sc)
// 加载数据
val data = sc.textFile(args(0))
// 解析数据并创建RDD
val salesRDD: RDD[Sale] = data.map(line => line.split(" ")).map(arr => Sale(arr(0), arr(1).toInt, arr(2).toInt, arr(3).toDouble))
// 将RDD转换为DataFrame
import sqlContext.implicits._
val df = salesRDD.toDF()
// 选择需要的列并展示前5行
val nameMoneyDF = df.select("productId","money","quantity")
nameMoneyDF.show(5)
// 按productId分组并计算销售量,然后展示销售量最高的5个商品
val totalSales = nameMoneyDF.groupBy("productId").sum("quantity")
val topSales = totalSales.select(totalSales("productId"),totalSales("sum(quantity)").as("Totalquantity")).orderBy(desc("Totalquantity"))
topSales.show(5)
// 按cardId分组并计算购买量,然后展示购买量最高的5个客户
val totalbuy = df.groupBy("cardId").sum("quantity")
val topbuy = totalbuy.select(totalbuy("cardId"),totalbuy("sum(quantity)").as("Totalquantity")).orderBy(desc("Totalquantity"))
topbuy.show(5)
// 选择productId为230010的行并展示结果
val Oneproduct:Dataset[Row]= topSales.select("productId","Totalquantity").filter(row=>row.getAs[String]("productId") == 230010)
Oneproduct.show()
// 停止SparkContext
sc.stop()
}
}
```