pyspark MlLib：排除行中的列值

我试图从一个数据帧创建一个

LabeledPoint

的RDD，以便以后在MlLib中使用它

如果

my_target

列是sparkDF中的第一列，则下面的代码可以正常工作。但是，如果

my_target

列不是第一列，如何修改下面的代码以排除

my_target

以创建正确的标签点

import pyspark.mllib.classification as clf
labeledData = sparkDF.rdd.map(lambda row: clf.LabeledPoint(row['my_target'],row[1:]))

logRegr = clf.LogisticRegressionWithSGD.train(labeledData)

也就是说，

行[1:][/code>现在排除第一列中的值；如果我想排除第行第N列中的值，我该怎么做？谢谢

>>> a = [(1,21,31,41),(2,22,32,42),(3,23,33,43),(4,24,34,44),(5,25,35,45)]
>>> df = spark.createDataFrame(a,["foo","bar","baz","bat"])
>>> df.show()
+---+---+---+---+
|foo|bar|baz|bat|
+---+---+---+---+
|  1| 21| 31| 41|
|  2| 22| 32| 42|
|  3| 23| 33| 43|
|  4| 24| 34| 44|
|  5| 25| 35| 45|
+---+---+---+---+

>>> N = 2 
# N is the column that you want to exclude (in this example the third, indexing starts at 0)
>>> labeledData = df.rdd.map(lambda row: LabeledPoint(row['foo'],row[:N]+row[N+1:]))
# it is just a concatenation with N that is excluded both in row[:N] and row[N+1:]

>>> labeledData.collect()
[LabeledPoint(1.0, [1.0,21.0,41.0]), LabeledPoint(2.0, [2.0,22.0,42.0]), LabeledPoint(3.0, [3.0,23.0,43.0]), LabeledPoint(4.0, [4.0,24.0,44.0]), LabeledPoint(5.0, [5.0,25.0,45.0])]