python - Spark 上的行明智计算

Question

这个问题已经有答案了:

How to sort by column in descending order in Spark SQL? (6 个回答)

已关闭 5 年前。

基于此answer我需要进行一些逐行计算

result= (reduce(add, (<some row wise calculation on col(x)> for x in df.columns[1:])) / n).alias("result")

但在此之前我需要按降序对行值进行排序(更改数据框中每行的列顺序？)假设我有以下行

 3,7,21,9
 5,15,10,2

例如，我需要知道每行每个值的排名(顺序)，然​​后计算总和(值/索引)对于第一行

21 ->4,9->3,7->3,3->1,sum(21/4,9/3,7/3,3/1)

第二行

15->4,10->3,5->2,2->1,sum(15/4,10/4,5/2,2/1)

不是重复的，因为我需要不是按列而是按行排序

Answer 1

假设您的输入数据框如下

+----+----+----+----+
|col1|col2|col3|col4|
+----+----+----+----+
|3   |7   |21  |9   |
|5   |15  |10  |2   |
+----+----+----+----+

然后你可以编写一个udf函数来获取你想要的输出列

from pyspark.sql import functions as f
from pyspark.sql import types as t
def sortAndIndex(list):
    return sorted([(value, index+1) for index, value in enumerate(sorted(list))],  reverse=True)

sortAndIndexUdf = f.udf(sortAndIndex, t.ArrayType(t.StructType([t.StructField('key', t.IntegerType(), True), t.StructField('value', t.IntegerType(), True)])))

df.withColumn('sortedAndIndexed', sortAndIndexUdf(f.array([x for x in df.columns])))

这应该给你

+----+----+----+----+----------------------------------+
|col1|col2|col3|col4|sortedAndIndexed                  |
+----+----+----+----+----------------------------------+
|3   |7   |21  |9   |[[21, 4], [9, 3], [7, 2], [3, 1]] |
|5   |15  |10  |2   |[[15, 4], [10, 3], [5, 2], [2, 1]]|
+----+----+----+----+----------------------------------+

更新

您评论为

my calculation should be sum(value/index) so probably using yours udf funcrtion I should return some kind of reduce(add,)?

为此你可以做到

from pyspark.sql import functions as f
from pyspark.sql import types as t
def divideAndSum(list):
    return sum([float(value)/(index+1) for index, value in enumerate(sorted(list))])

divideAndSumUdf = f.udf(divideAndSum, t.DoubleType())

df.withColumn('divideAndSum', divideAndSumUdf(f.array([x for x in df.columns])))

这应该给你

+----+----+----+----+------------------+
|col1|col2|col3|col4|divideAndSum      |
+----+----+----+----+------------------+
|3   |7   |21  |9   |14.75             |
|5   |15  |10  |2   |11.583333333333334|
+----+----+----+----+------------------+