代码 - 柚子湖

代码

1.employee.json

import org.apache.spark.sql.sparksession
import spark.implicits._
import org.apache.spark.sql.types.{structType，structField,stringType,FloatType}

inport org.apache.spark.sql.{DataFrane，Row，SparkSession}
val spark = Sparksession.builder().getorcreate()
val df1 = spark.read.format("json").load("file:/ //home/gbc/employee.json")
df1.show()

df1.distinct().show()

df1.drop("id").show()

df1.filter(col( "age") > 30 ).show()

df1.groupBy("age").count().show()

df1.sort(col("name" ).asc).show()

df1.take(3)

df1.select(col("name").as("usernane")).show()

df1.agg("age"->"avg").show()
df1.agg("age"->"min").show()

import org.apache.spark.{SparkConf, SparkContext}  
import org.apache.spark.sql.{DataFrame, SparkSession}  
import play.api.libs.json.{JsObject, Json}  
  
object JsonExample {  
  def main(args: Array[String]): Unit = {  
    val conf = new SparkConf().setAppName("JsonExample")  
    val sc = new SparkContext(conf)  
    val spark = SparkSession.builder().sparkContext(sc).getOrCreate()  
  
    import spark.implicits._  
  
    // 读取文件  
    val jsonFile = sc.textFile("/home/gbc/employee.json")  
  
    // 将每一行JSON数据转化为JsObject  
    val jsonData = jsonFile.map(line => Json.parse(line).as[JsObject])  
  
    // 转化为DataFrame  
    val df = spark.createDataFrame(jsonData.rdd, Seq(classOf[JsObject]))  
  
    // 执行各种操作  
    // (1)查询所有数据  
    df.show()  
  
    // (2)查询所有数据，并去除重复的数据  
    df.distinct().show()  
  
    // (3)查询所有数据，打印时去除id字段  
    df.select("name", "age").show()  
  
    // (4)筛选出age>30的记录  
    df.filter(df("age") > 30).show()  
  
    // (5)将数据按age分组  
    df.groupBy("age").count().show()  
  
    // (6)将数据按name升序排列  
    df.sort("name").show()  
  
    // (7)取出前3行数据  
    df.limit(3).show()  
  
    // (8)查询所有记录的name列，并为其取别名为username  
    df.select("name").alias("username").show()  
  
    // (9)查询年龄age的平均值  
    df.agg(avg("age")).show()  
  
    // (10)查询年龄age的最小值  
    df.agg(min("age)).show()  
  }  
}

2.Coffee Chain.csv

import org.apache.spark.sql.{SparkSession, functions}  
  
object CoffeeChain {  
  def main(args: Array[String]): Unit = {  
    val spark = SparkSession.builder()  
      .appName("CoffeeChain")  
      .master("local[*]") // 在本地的所有CPU核心上运行  
      .getOrCreate()  
  
    import spark.implicits._  
  
    // 加载CSV文件  
    val df = spark.read.option("header", "true").csv("/home/gbc/Coffee Chain.csv")  
  
    // 显示咖啡连锁店的销售量排名，按照销售量降序排列  
    df.sort("Market", "Product Type", "Product", "Type").select("Market", "Product Type", "Product", "Type", "Marketing").show()  
  
    // 查看咖啡销售量和所在州的关系，按降序排列  
    df.sort("State", "Product Type", "Product", "Type").select("State", "Product Type", "Product", "Type", "Marketing").show()  
  
    // 查询咖啡的平均利润和售价，按平均利润降序排列  
    df.sort("Product Type", "Product", "Type", functions.avg("Profit").desc).select("Product Type", "Product", "Type", functions.avg("Profit"), functions.avg("Cogs")).show()  
  
    // 查询市场规模、市场地域与销售量的关系。按总销量降序排列  
    df.sort("Market Size", "Market", "Product Type", "Product", "Type", functions.sum("Marketing").desc).select("Market Size", "Market", "Product Type", "Product", "Type", functions.sum("Marketing")).show()  
  
    // 查询咖啡属性与平均售价、平均利润、销售量与其他成本的关系  
    df.sort("Product Type", "Product", "Type", functions.avg("Cogs"), functions.avg("Profit"), functions.sum("Marketing").desc).select("Product Type", "Product", "Type", functions.avg("Cogs"), functions.avg("Profit"), functions.sum("Marketing")).show()  
  }  
}

3.Sport.txt

import org.apache.spark.sql.SparkSession  
  
val spark = SparkSession.builder()  
  .appName("Sport数据分析")  
  .master("local[*]") // 在本地的所有CPU核心上运行  
  .getOrCreate()  
  
val path = "/home/gbc/Sport.txt"  
val df = spark.read.option("header", "true").csv(path)
val avg_scores = df.select("比赛项目", "成绩").groupBy("比赛项目").agg(avg("成绩"))  
avg_scores.show()
val class_ranks = df.select("班级", "名次").groupBy("班级").agg(countDistinct("名次").alias("rank_count"))  
class_ranks.show()
val track_events = df.filter(col("比赛项目").isin("100米短跑", "200米短跑"))  
track_events.show()
val top_ranked_track_athletes = track_events.select("比赛项目", "运动员").groupBy("比赛项目", "运动员").agg(countDistinct("名次").alias("rank_count"))  
top_ranked_track_athletes.show()

UserID, Timestamp, EventType, EventContent
1, 2023-04-15 08:30:00, Click, Product A
2, 2023-04-15 08:35:00, View, Product B
1, 2023-04-15 08:40:00, Add to Cart, Product A
3, 2023-04-15 08:42:00, Click, Product C
2, 2023-04-15 08:50:00, Purchase, Product B
1, 2023-04-15 08:55:00, Click, Product D
4, 2023-04-15 08:57:00, View, Product E
2, 2023-04-15 09:00:00, Click, Product F
3, 2023-04-15 09:05:00, Add to Cart, Product C
5, 2023-04-15 09:10:00, Click, Product G
1, 2023-04-15 08:30:00, Click, Product A
2, 2023-04-15 08:35:00, View, Product B
1, 2023-04-15 08:40:00, Add to Cart, Product A
3, 2023-04-15 08:42:00, Click, Product C
2, 2023-04-15 08:50:00, Purchase, Product B
1, 2023-04-15 08:55:00, Click, Product D
4, 2023-04-15 08:57:00, View, Product E
2, 2023-04-15 09:00:00, Click, Product F
3, 2023-04-15 09:05:00, Add to Cart, Product C
5, 2023-04-15 09:10:00, Click, Product G
1, 2023-04-15 09:20:00, Purchase, Product D
2, 2023-04-15 09:25:00, Click, Product F
3, 2023-04-15 09:30:00, View, Product C
1, 2023-04-15 09:35:00, Click, Product A
2, 2023-04-15 09:40:00, Add to Cart, Product F
4, 2023-04-15 09:45:00, Click, Product E
1, 2023-04-15 09:50:00, View, Product D
2, 2023-04-15 09:55:00, Click, Product B
3, 2023-04-15 10:00:00, Purchase, Product C
5, 2023-04-15 10:05:00, Click, Product G

UserID, UserName
1, Alice
2, Bob
3, Carol
4, Dave
5, Eve

比赛项目, 班级, 运动员, 成绩, 名次
100米短跑, A班, 张三, 12.45, 1
100米短跑, B班, 李四, 12.62, 2
100米短跑, C班, 王五, 12.75, 3
100米短跑, A班, 李华, 12.82, 4
100米短跑, C班, 王明, 13.05, 5
200米短跑, A班, 张三, 21.81, 2
200米短跑, A班, 刘强, 21.10, 1
200米短跑, C班, 王五, 22.35, 3
200米短跑, C班, 王明, 22.45, 4
200米短跑, B班, 李四, 22.60, 5
跳高, A班, 张三, 1.85, 2
跳高, B班, 李四, 1.90, 1
跳高, C班, 王五, 1.75, 3
铅球, A班, 张三, 12.34, 1
铅球, C班, 王明, 11.92, 2
铅球, C班, 王五, 11.50, 3
跳远, A班, 张三, 7.05, 2
跳远, B班, 李四, 6.95, 1
跳远, B班, 李华, 6.80, 3

import org.apache.spark.sql.{SparkSession, functions}

object Sports数据分析 {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.appName("Sports数据分析")
.master("local[*]")
.getOrCreate()

import spark.implicits._  

// 读取数据集  
val dataset = spark.read.csv("竞赛结果.csv", header = true, inferSchema = true)  
dataset.show()  

// 计算所有比赛项目的平均成绩  
val averageScores = dataset.groupBy("比赛项目").agg(functions.avg("成绩"))  
averageScores.show()  

// 统计每个班级的名次总数  
val classRankCounts = dataset.groupBy("班级").agg(functions.sum("名次").alias("总名次"))  
classRankCounts.show()  

val firstClass = classRankCounts.filter(col("总名次") === 1)  
val secondClass = classRankCounts.filter(col("总名次") === 2)  
val thirdClass = classRankCounts.filter(col("总名次") === 3)  

firstClass.show()  
secondClass.show()  
thirdClass.show()  

// 筛选并统计特定项目的成绩  
val trackAndFieldDataset = dataset.filter(col("比赛项目").isin("100米短跑", "200米短跑"))  
trackAndFieldDataset.show()  

val top3Counts = trackAndFieldDataset.groupBy("比赛项目", "班级").agg(functions.countDistinct("运动员").alias("个人数量"))  
top3Counts.show()

}
}

import org.apache.spark.sql.{SparkSession, functions}

object SportsDataAnalysis {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.appName("Sports Data Analysis")
.master("local[*]")
.getOrCreate()

import spark.implicits._  

// 读取用户信息数据集  
val userInfo = spark.read.format("csv").option("header", "true").load("User Info Data.txt")  
val userInfoWithID = userInfo.withColumn("用户ID", col("UserID").cast("string"))  

// 读取用户行为数据集  
val userActivity = spark.read.format("csv").option("header", "true").load("User Activity Data.txt")  

// 按照用户ID升序，时间戳降序排序，筛选前10条记录  
val sortedAndFiltered1 = userActivity.orderBy("用户ID").desc("时间戳").limit(10)  
sortedAndFiltered1.show()  

// 按照用户ID降序，事件类型升序排序，筛选前10条记录  
val sortedAndFiltered2 = userActivity.orderBy("用户ID").asc("事件类型").limit(10)  
sortedAndFiltered2.show()  

// 去重和统计  
val uniqueEvents = userActivity.select("用户ID", "事件类型", "事件内容").distinct()  
val eventCounts = uniqueEvents.groupBy("事件类型").count()  
eventCounts.show()  

// 数据连接和格式化  
val joined = userInfoWithID.join(userActivity, "用户ID")  
val formattedReport = joined.select(col("UserName").alias("用户名称"), col("事件类型"), col("事件内容"))  
  .withColumn("事件数量", functions.countDistinct("事件内容").alias("事件数量"))  
formattedReport.show()

}
}

posted on 2023-10-12 14:09 柚子湖阅读(12) 评论(0) 编辑收藏举报

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