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[英]Scala/Spark dataframes: find the column name corresponding to the max
[英]scala: get column name corresponding to max column value from variable columns list
我在 databricks 筆記本中有以下工作解決方案作為測試。
var maxcol = udf((col1: Long, col2: Long, col3: Long) => {
var res = ""
if (col1 > col2 && col1 > col3) res = "col1"
else if (col2 > col1 && col2 > col3) res = "col2"
else res = "col3"
res
})
val someDF = Seq(
(8, 10, 12, "bat"),
(64, 61, 59, "mouse"),
(-27, -30, -15, "horse")
).toDF("number1", "number2", "number3", "word")
.withColumn("maxColVal", greatest("number1", "number2", "number3"))
.withColumn("maxColVal_Name", maxcol(col("number1"), col("number2"), col("number3")))
display(someDF)
有沒有辦法讓這個通用? 我有一個用例來使變量列傳遞給這個 UDF,並且仍然獲得最大列名稱為 output 對應於具有最大值的列。 與上面我在 UDF 中硬編碼列名 'col1'、'col2' 和 'col3' 的情況不同。
下面使用:
val df = List((1,2,3,5,"a"),(4,2,3,1,"a"),(1,20,3,1,"a"),(1,22,22,2,"a")).toDF("mycol1","mycol2","mycol3","mycol4","mycol5")
//list all your columns among which you want to find the max value
val colGroup = List(df("mycol1"),df("mycol2"),df("mycol3"),df("mycol4"))
//list column value -> column name of the columns among which you want to find max value column NAME
val colGroupMap = List(df("mycol1"),lit("mycol1"),
df("mycol2"),lit("mycol2"),
df("mycol3"),lit("mycol3"),
df("mycol4"),lit("mycol4"))
var maxcol = udf((colVal: Map[Int,String]) => {
colVal.max._2 //you can easily find the column name of the max column value
})
df.withColumn("maxColValue",greatest(colGroup:_*)).withColumn("maxColVal_Name",maxcol(map(colGroupMap:_*))).show(false)
+------+------+------+------+------+-----------+--------------+
|mycol1|mycol2|mycol3|mycol4|mycol5|maxColValue|maxColVal_Name|
+------+------+------+------+------+-----------+--------------+
|1 |2 |3 |5 |a |5 |mycol4 |
|4 |2 |3 |1 |a |4 |mycol1 |
|1 |20 |3 |1 |a |20 |mycol2 |
|1 |22 |22 |2 |a |22 |mycol3 |
+------+------+------+------+------+-----------+--------------+
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