火花的行计算[复制]

假设您的输入数据帧如下

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

然后,您可以编写一个udf函数来获得所需的输出列

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])))

它应该给你

+----+----+----+----+----------------------------------+
|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

你评论为

我的计算应该是sum(值/索引),所以可能使用您的udf函数,我应该返回某种reduce(add,)?

为此你可以做

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])))

它应该给你

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