我的输入数据中有一组列,我根据多列对这些列进行透视。
旋转完成后我遇到列标题问题。
输入数据
我的方法生成的输出 -
预期的输出 header :
我需要输出的标题看起来像 -
到目前为止为实现我得到的输出所做的步骤 -
// *Load the data*
scala> val input_data =spark.read.option("header","true").option("inferschema","true").option("delimiter","\t").csv("s3://mybucket/data.tsv")
// *Filter the data where residentFlag column = T*
scala> val filtered_data = input_data.select("numericID","age","salary","gender","residentFlag").filter($"residentFlag".contains("T"))
// *Now we will the pivot the filtered data by each column*
scala> val pivotByAge = filtered_data.groupBy("age","numericID").pivot("age").agg(expr("coalesce(first(numericID),'-')")).drop("age")
// *Pivot the data by the second column named "salary"*
scala> val pivotBySalary = filtered_data.groupBy("salary","numericID").pivot("salary").agg(expr("coalesce(first(numericID),'-')")).drop("salary")
// *Join the above two dataframes based on the numericID*
scala> val intermediateDf = pivotByAge.join(pivotBySalary,"numericID")
// *Now pivot the filtered data on Step 2 on the third column named Gender*
scala> val pivotByGender = filtered_data.groupBy("gender","numericID").pivot("gender").agg(expr("coalesce(first(numericID),'-')")).drop("gender")
// *Join the above dataframe with the intermediateDf*
scala> val outputDF= pivotByGender.join(intermediateDf ,"numericID")
如何重命名旋转后生成的列?
我可以采用不同的方法来基于多列(将近 300 列)旋转数据集吗?
对于提高性能有什么优化/建议吗?
最佳答案
可以考虑使用foldLeft遍历 to-pivot 列的列表以连续创建 pivot 数据框,重命名生成的 pivot 列,然后是 cumulative join:
val data = Seq(
(1, 30, 50000, "M"),
(1, 25, 70000, "F"),
(1, 40, 70000, "M"),
(1, 30, 80000, "M"),
(2, 30, 80000, "M"),
(2, 40, 50000, "F"),
(2, 25, 70000, "F")
).toDF("numericID", "age", "salary", "gender")
// Create list pivotCols which consists columns to pivot
val id = data.columns.head
val pivotCols = data.columns.filter(_ != "numericID")
// Create the first pivot dataframe from the first column in list pivotCols and
// rename each of the generated pivot columns
val c1 = pivotCols.head
val df1 = data.groupBy(c1, id).pivot(c1).agg(expr(s"coalesce(first($id),'-')")).drop(c1)
val df1Renamed = df1.columns.tail.foldLeft( df1 )( (acc, x) =>
acc.withColumnRenamed(x, c1 + "_" + x)
)
// Using the first pivot dataframe as the initial dataframe, process each of the
// remaining columns in list pivotCols similar to how the first column is processed,
// and cumulatively join each of them with the previously joined dataframe
pivotCols.tail.foldLeft( df1Renamed )(
(accDF, c) => {
val df = data.groupBy(c, id).pivot(c).agg(expr(s"coalesce(first($id),'-')")).drop(c)
val dfRenamed = df.columns.tail.foldLeft( df )( (acc, x) =>
acc.withColumnRenamed(x, c + "_" + x)
)
dfRenamed.join(accDF, Seq(id))
}
)
// +---------+--------+--------+------------+------------+------------+------+------+------+
// |numericID|gender_F|gender_M|salary_50000|salary_70000|salary_80000|age_25|age_30|age_40|
// +---------+--------+--------+------------+------------+------------+------+------+------+
// |2 |2 |- |2 |- |- |- |2 |- |
// |2 |2 |- |2 |- |- |2 |- |- |
// |2 |2 |- |2 |- |- |- |- |2 |
// |2 |2 |- |- |2 |- |- |2 |- |
// |2 |2 |- |- |2 |- |2 |- |- |
// |2 |2 |- |- |2 |- |- |- |2 |
// |2 |2 |- |- |- |2 |- |2 |- |
// |2 |2 |- |- |- |2 |2 |- |- |
// |2 |2 |- |- |- |2 |- |- |2 |
// |2 |- |2 |2 |- |- |- |2 |- |
// |2 |- |2 |2 |- |- |2 |- |- |
// |2 |- |2 |2 |- |- |- |- |2 |
// |2 |- |2 |- |2 |- |- |2 |- |
// |2 |- |2 |- |2 |- |2 |- |- |
// |2 |- |2 |- |2 |- |- |- |2 |
// |2 |- |2 |- |- |2 |- |2 |- |
// |2 |- |2 |- |- |2 |2 |- |- |
// |2 |- |2 |- |- |2 |- |- |2 |
// |1 |- |1 |- |1 |- |1 |- |- |
// |1 |- |1 |- |1 |- |- |- |1 |
// ...
关于scala - Spark 中多个 Pivoted 列的重命名和优化,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/49284397/