spark=SparkSession.builder \.appName("Add Row to DataFrame")\.getOrCreate() 1. 2. 3. 4. 5. 步骤2:创建DataFrame 接下来,我们需要创建一个DataFrame。假设我们已经有了一些数据,我们可以从列表、元组或字典中创建DataFrame。 data=[("Alice",34),("Bob",45),("Cathy",29)]columns=["Name","Age...
val record: RDD[Row] = tmpRdd.map(x => { Row(x._1.get(0), x._1.get(1), x._2) }) val schema = new StructType().add("name", "string") .add("age", "string") .add("id", "long") spark.createDataFrame(record, schema).show() 1. 2. 3. 4. 5. 6. 7. 8. 结果:...
.add("address",StringType)// 使用Row的子类GenericRowWithSchema创建新的RowvalnewRow:Row=newGenericRowWithSchema(buffer.toArray, schema)// 使用新的Row替换成原来的RownewRow }).map(row => {// 打印新的schemaprintln(row.schema)// 测试我们新增的字段valgender = row.getAs[String]("gender")// ...
以下是一个使用Scala在Spark DataFrame中添加新行的示例代码: 代码语言:txt 复制 import org.apache.spark.sql.{SparkSession, Row} import org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType} object AddRowExample { def main(args: Array[String]): Unit = { // 创建SparkSessio...
val spark = SparkSession.builder().appName("Add Rows to Empty Dataframe").getOrCreate() // 创建一个空Dataframe val emptyDF = spark.createDataFrame(spark.sparkContext.emptyRDD[Row], StructType(Seq(StructField("col1", StringType), StructField("col2", IntegerType))) // 创建一个包含新行记...
//dataframe新增一列方法1,利用createDataFrame方法val trdd = input.select(targetColumns).rdd.map(x=>{if(x.get(0).toString().toDouble > critValueR || x.get(0).toString().toDouble <critValueL) Row(x.get(0).toString().toDouble,"F")elseRow(x.get(0).toString().toDouble,"T") ...
//dataframe新增一列方法1,利用createDataFrame方法valtrdd=input.select(targetColumns).rdd.map(x=>{if(x.get(0).toString().toDouble>critValueR||x.get(0).toString().toDouble<critValueL)Row(x.get(0).toString().toDouble,"F")elseRow(x.get(0).toString().toDouble,"T")})valschema=input....
Row对象 DataFrame中每条数据封装在Row中,Row表示每行数据,具体哪些字段位置,获取DataFrame中第一条数据。 如何构建Row对象:传递value即可,官方实例代码: from pyspark.sql import Row // Create a Row from values. Row(value1, value2, value3, ...) ...
add(Row("刘三",21,15552211523L)) spark.createDataFrame(dataList,schema).show() +---+---+---+ |name|age| phone| +---+---+---+ |李明| 20|15552211521| |王红| 19|13287994007| |刘三| 21|15552211523| +---+---+---+ 二、DataFrame API基本操作1. 数据people.json...
import org.apache.spark.sql.Row val arraySchema = new StructType() .add("name",StringType) .add("subjects",ArrayType(StringType)) val arrayDF = spark.createDataFrame(arrayRDD, arraySchema) arrayDF.printSchema arrayDF.show() 输出结果如下: ...